Polars_Notebook.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/practicatto/Intro-Polars/blob/main/Polars_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Primeros Pasos en Polars 🤓\n",
        "Este notebook es usado como material para explicar las características core de Polars en IWD Camino Ecuador 2024"
      ],
      "metadata": {
        "id": "G7d1n3QiVyyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install polars plotly matplotlib pandas"
      ],
      "metadata": {
        "id": "OqTt7On0VxqU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import polars as pl\n",
        "import pandas as pd\n",
        "import time"
      ],
      "metadata": {
        "id": "2mFSarhPWw0j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup configurations\n",
        "Podemos setear polars con varias configuraciones, ahora revisaremos una, chequea el resto en [aquí!!](https://pola-rs.github.io/polars/py-polars/html/reference/config.html)"
      ],
      "metadata": {
        "id": "K9z5I4XLXAyY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pl.Config.set_fmt_str_lengths(100)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "5a2c1954-077e-43e0-8726-3dcba2790d99"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "polars.config.Config"
            ],
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              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
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              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>polars.config.Config</b><br/>def __call__(func)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/polars/config.py</a>Configure polars; offers options for table formatting and more.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Can also be used as a context manager OR a function decorator in order to\n",
              "temporarily scope the lifetime of specific options. For example:\n",
              "\n",
              "&gt;&gt;&gt; with pl.Config() as cfg:\n",
              "...     # set verbose for more detailed output within the scope\n",
              "...     cfg.set_verbose(True)  # doctest: +IGNORE_RESULT\n",
              "...\n",
              "&gt;&gt;&gt; # scope exit - no longer in verbose mode\n",
              "\n",
              "This can also be written more compactly as:\n",
              "\n",
              "&gt;&gt;&gt; with pl.Config(verbose=True):\n",
              "...     pass\n",
              "...\n",
              "\n",
              "(The compact format is available for all `Config` methods that take a single value).\n",
              "\n",
              "Alternatively, you can use as a decorator in order to scope the duration of the\n",
              "selected options to a specific function:\n",
              "\n",
              "&gt;&gt;&gt; @pl.Config(verbose=True)\n",
              "... def test():\n",
              "...     pass\n",
              "...</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Leamos un archivo!\n",
        "Polars puede leer múltiples archivos: JSON, CSV, PARQUET, ARROW, EXCEL y también conexiones de bases.\n",
        "\n",
        "### Sobre nuestro dataset\n",
        "Titanic\n",
        "\n",
        "Tamaño de dataset: 28 kb\n",
        "\n",
        "Formato: csv\n",
        "\n"
      ],
      "metadata": {
        "id": "FLNStMswXbfP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file=\"/content/drive/MyDrive/dataset_polars/titanic.csv\""
      ],
      "metadata": {
        "id": "zU4-6r2DfqJl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "start_time_pandas = time.time()\n",
        "pd_df = pd.read_csv(file)\n",
        "time_taken_pandas = time.time() - start_time_pandas\n",
        "\n",
        "print(f\"Time taken by Pandas: {time_taken_pandas*1000} milliseconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzNzPtaPYLiv",
        "outputId": "d63a78d6-25d1-40a9-b1f7-387abfac4f1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken by Pandas: 39.333343505859375 milliseconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "start_time_polars = time.time()\n",
        "pl_df = pl.read_csv(file)\n",
        "time_taken_polars = time.time() - start_time_polars\n",
        "print(f\"Time taken by Polars: {time_taken_polars*1000} milliseconds\")"
      ],
      "metadata": {
        "id": "lfZm70offWuF",
        "outputId": "35f07702-7155-48e7-f819-6f299d28334a",
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Time taken by Polars: 246.70124053955078 milliseconds\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "pl_df.head()"
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              "shape: (5, 11)\n",
              "┌─────────────┬────────┬────────────────────┬────────┬───┬─────────┬─────────┬───────┬──────────┐\n",
              "│ PassengerId ┆ Pclass ┆ Name               ┆ Sex    ┆ … ┆ Ticket  ┆ Fare    ┆ Cabin ┆ Embarked │\n",
              "│ ---         ┆ ---    ┆ ---                ┆ ---    ┆   ┆ ---     ┆ ---     ┆ ---   ┆ ---      │\n",
              "│ i64         ┆ i64    ┆ str                ┆ str    ┆   ┆ str     ┆ f64     ┆ str   ┆ str      │\n",
              "╞═════════════╪════════╪════════════════════╪════════╪═══╪═════════╪═════════╪═══════╪══════════╡\n",
              "│ 892         ┆ 3      ┆ Kelly, Mr. James   ┆ male   ┆ … ┆ 330911  ┆ 7.8292  ┆ null  ┆ Q        │\n",
              "│ 893         ┆ 3      ┆ Wilkes, Mrs. James ┆ female ┆ … ┆ 363272  ┆ 7.0     ┆ null  ┆ S        │\n",
              "│             ┆        ┆ (Ellen Needs)      ┆        ┆   ┆         ┆         ┆       ┆          │\n",
              "│ 894         ┆ 2      ┆ Myles, Mr. Thomas  ┆ male   ┆ … ┆ 240276  ┆ 9.6875  ┆ null  ┆ Q        │\n",
              "│             ┆        ┆ Francis            ┆        ┆   ┆         ┆         ┆       ┆          │\n",
              "│ 895         ┆ 3      ┆ Wirz, Mr. Albert   ┆ male   ┆ … ┆ 315154  ┆ 8.6625  ┆ null  ┆ S        │\n",
              "│ 896         ┆ 3      ┆ Hirvonen, Mrs.     ┆ female ┆ … ┆ 3101298 ┆ 12.2875 ┆ null  ┆ S        │\n",
              "│             ┆        ┆ Alexander (Helga … ┆        ┆   ┆         ┆         ┆       ┆          │\n",
              "└─────────────┴────────┴────────────────────┴────────┴───┴─────────┴─────────┴───────┴──────────┘"
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              "<div><style>\n",
              ".dataframe > thead > tr,\n",
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              "<small>shape: (5, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PassengerId</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th></tr><tr><td>i64</td><td>i64</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>i64</td><td>str</td><td>f64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>892</td><td>3</td><td>&quot;Kelly, Mr. Jam…</td><td>&quot;male&quot;</td><td>34.5</td><td>0</td><td>0</td><td>&quot;330911&quot;</td><td>7.8292</td><td>null</td><td>&quot;Q&quot;</td></tr><tr><td>893</td><td>3</td><td>&quot;Wilkes, Mrs. J…</td><td>&quot;female&quot;</td><td>47.0</td><td>1</td><td>0</td><td>&quot;363272&quot;</td><td>7.0</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>894</td><td>2</td><td>&quot;Myles, Mr. Tho…</td><td>&quot;male&quot;</td><td>62.0</td><td>0</td><td>0</td><td>&quot;240276&quot;</td><td>9.6875</td><td>null</td><td>&quot;Q&quot;</td></tr><tr><td>895</td><td>3</td><td>&quot;Wirz, Mr. Albe…</td><td>&quot;male&quot;</td><td>27.0</td><td>0</td><td>0</td><td>&quot;315154&quot;</td><td>8.6625</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>896</td><td>3</td><td>&quot;Hirvonen, Mrs.…</td><td>&quot;female&quot;</td><td>22.0</td><td>1</td><td>1</td><td>&quot;3101298&quot;</td><td>12.2875</td><td>null</td><td>&quot;S&quot;</td></tr></tbody></table></div>"
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "# Ver información general en pandas\n",
        "pd_df.info()\n",
        "\n",
        "# Ver las estadisticas\n",
        "pd_df.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 622
        },
        "id": "1cPSN2yBaV-i",
        "outputId": "f40de9c7-fd83-456d-93fb-39b885600bd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 11 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  418 non-null    int64  \n",
            " 1   Pclass       418 non-null    int64  \n",
            " 2   Name         418 non-null    object \n",
            " 3   Sex          418 non-null    object \n",
            " 4   Age          332 non-null    float64\n",
            " 5   SibSp        418 non-null    int64  \n",
            " 6   Parch        418 non-null    int64  \n",
            " 7   Ticket       418 non-null    object \n",
            " 8   Fare         417 non-null    float64\n",
            " 9   Cabin        91 non-null     object \n",
            " 10  Embarked     418 non-null    object \n",
            "dtypes: float64(2), int64(4), object(5)\n",
            "memory usage: 36.0+ KB\n"
          ]
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              "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
              "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
              "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
              "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
              "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
              "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
              "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
              "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
              "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
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              "      <th>count</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>332.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>418.000000</td>\n",
              "      <td>417.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>2.265550</td>\n",
              "      <td>30.272590</td>\n",
              "      <td>0.447368</td>\n",
              "      <td>0.392344</td>\n",
              "      <td>35.627188</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>120.810458</td>\n",
              "      <td>0.841838</td>\n",
              "      <td>14.181209</td>\n",
              "      <td>0.896760</td>\n",
              "      <td>0.981429</td>\n",
              "      <td>55.907576</td>\n",
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              "      <th>min</th>\n",
              "      <td>892.000000</td>\n",
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              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>996.250000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1100.500000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>27.000000</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>1204.750000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.500000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
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              "        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-940dd694-b74b-440d-a2d5-d8c8d9a58c67\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-940dd694-b74b-440d-a2d5-d8c8d9a58c67')\"\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-940dd694-b74b-440d-a2d5-d8c8d9a58c67 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 412.1232851470217,\n        \"min\": 120.81045760473994,\n        \"max\": 1309.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          418.0,\n          1100.5,\n          1204.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.0758997861715,\n        \"min\": 0.8418375519640503,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.2655502392344498,\n          3.0,\n          0.8418375519640503\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 109.15868834351015,\n        \"min\": 0.17,\n        \"max\": 332.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.272590361445783,\n          27.0,\n          332.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.28745840271156,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          418.0,\n          0.4473684210526316,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 147.29373273558997,\n        \"min\": 0.0,\n        \"max\": 418.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3923444976076555,\n          9.0,\n          0.9814288785371691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 206.20808392426468,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.627188489208635,\n          14.4542,\n          417.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "a8h67zehaleP",
        "outputId": "ae8de1cf-4959-4370-fac3-76709035c834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (9, 12)\n",
              "┌────────────┬───────────────┬──────────┬──────────────┬───┬────────┬───────────┬───────┬──────────┐\n",
              "│ describe   ┆ PassengerId   ┆ Pclass   ┆ Name         ┆ … ┆ Ticket ┆ Fare      ┆ Cabin ┆ Embarked │\n",
              "│ ---        ┆ ---           ┆ ---      ┆ ---          ┆   ┆ ---    ┆ ---       ┆ ---   ┆ ---      │\n",
              "│ str        ┆ f64           ┆ f64      ┆ str          ┆   ┆ str    ┆ f64       ┆ str   ┆ str      │\n",
              "╞════════════╪═══════════════╪══════════╪══════════════╪═══╪════════╪═══════════╪═══════╪══════════╡\n",
              "│ count      ┆ 418.0         ┆ 418.0    ┆ 418          ┆ … ┆ 418    ┆ 417.0     ┆ 91    ┆ 418      │\n",
              "│ null_count ┆ 0.0           ┆ 0.0      ┆ 0            ┆ … ┆ 0      ┆ 1.0       ┆ 327   ┆ 0        │\n",
              "│ mean       ┆ 1100.5        ┆ 2.26555  ┆ null         ┆ … ┆ null   ┆ 35.627188 ┆ null  ┆ null     │\n",
              "│ std        ┆ 120.810458    ┆ 0.841838 ┆ null         ┆ … ┆ null   ┆ 55.907576 ┆ null  ┆ null     │\n",
              "│ min        ┆ 892.0         ┆ 1.0      ┆ Abbott,      ┆ … ┆ 110469 ┆ 0.0       ┆ A11   ┆ C        │\n",
              "│            ┆               ┆          ┆ Master.      ┆   ┆        ┆           ┆       ┆          │\n",
              "│            ┆               ┆          ┆ Eugene       ┆   ┆        ┆           ┆       ┆          │\n",
              "│            ┆               ┆          ┆ Joseph       ┆   ┆        ┆           ┆       ┆          │\n",
              "│ 25%        ┆ 996.0         ┆ 1.0      ┆ null         ┆ … ┆ null   ┆ 7.8958    ┆ null  ┆ null     │\n",
              "│ 50%        ┆ 1101.0        ┆ 3.0      ┆ null         ┆ … ┆ null   ┆ 14.4542   ┆ null  ┆ null     │\n",
              "│ 75%        ┆ 1205.0        ┆ 3.0      ┆ null         ┆ … ┆ null   ┆ 31.5      ┆ null  ┆ null     │\n",
              "│ max        ┆ 1309.0        ┆ 3.0      ┆ van          ┆ … ┆ W.E.P. ┆ 512.3292  ┆ G6    ┆ S        │\n",
              "│            ┆               ┆          ┆ Billiard,    ┆   ┆ 5734   ┆           ┆       ┆          │\n",
              "│            ┆               ┆          ┆ Master.      ┆   ┆        ┆           ┆       ┆          │\n",
              "│            ┆               ┆          ┆ Walter Joh…  ┆   ┆        ┆           ┆       ┆          │\n",
              "└────────────┴───────────────┴──────────┴──────────────┴───┴────────┴───────────┴───────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (9, 12)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>describe</th><th>PassengerId</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th></tr><tr><td>str</td><td>f64</td><td>f64</td><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td><td>str</td><td>f64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>418.0</td><td>418.0</td><td>&quot;418&quot;</td><td>&quot;418&quot;</td><td>332.0</td><td>418.0</td><td>418.0</td><td>&quot;418&quot;</td><td>417.0</td><td>&quot;91&quot;</td><td>&quot;418&quot;</td></tr><tr><td>&quot;null_count&quot;</td><td>0.0</td><td>0.0</td><td>&quot;0&quot;</td><td>&quot;0&quot;</td><td>86.0</td><td>0.0</td><td>0.0</td><td>&quot;0&quot;</td><td>1.0</td><td>&quot;327&quot;</td><td>&quot;0&quot;</td></tr><tr><td>&quot;mean&quot;</td><td>1100.5</td><td>2.26555</td><td>null</td><td>null</td><td>30.27259</td><td>0.447368</td><td>0.392344</td><td>null</td><td>35.627188</td><td>null</td><td>null</td></tr><tr><td>&quot;std&quot;</td><td>120.810458</td><td>0.841838</td><td>null</td><td>null</td><td>14.181209</td><td>0.89676</td><td>0.981429</td><td>null</td><td>55.907576</td><td>null</td><td>null</td></tr><tr><td>&quot;min&quot;</td><td>892.0</td><td>1.0</td><td>&quot;Abbott, Master…</td><td>&quot;female&quot;</td><td>0.17</td><td>0.0</td><td>0.0</td><td>&quot;110469&quot;</td><td>0.0</td><td>&quot;A11&quot;</td><td>&quot;C&quot;</td></tr><tr><td>&quot;25%&quot;</td><td>996.0</td><td>1.0</td><td>null</td><td>null</td><td>21.0</td><td>0.0</td><td>0.0</td><td>null</td><td>7.8958</td><td>null</td><td>null</td></tr><tr><td>&quot;50%&quot;</td><td>1101.0</td><td>3.0</td><td>null</td><td>null</td><td>27.0</td><td>0.0</td><td>0.0</td><td>null</td><td>14.4542</td><td>null</td><td>null</td></tr><tr><td>&quot;75%&quot;</td><td>1205.0</td><td>3.0</td><td>null</td><td>null</td><td>39.0</td><td>1.0</td><td>0.0</td><td>null</td><td>31.5</td><td>null</td><td>null</td></tr><tr><td>&quot;max&quot;</td><td>1309.0</td><td>3.0</td><td>&quot;van Billiard, …</td><td>&quot;male&quot;</td><td>76.0</td><td>8.0</td><td>9.0</td><td>&quot;W.E.P. 5734&quot;</td><td>512.3292</td><td>&quot;G6&quot;</td><td>&quot;S&quot;</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ver valores de embarcado\n",
        "pd_df['Embarked'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9fYMSL5aqjD",
        "outputId": "70e90732-29e4-465e-dcbc-fa595faca807"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    270\n",
              "C    102\n",
              "Q     46\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ver valores de embarcado polars\n",
        "pl_df[\"Embarked\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "BMQuBVKEcmVP",
        "outputId": "13df67d3-11ce-4474-fa4a-f396e8ae37af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (3, 2)\n",
              "┌──────────┬───────┐\n",
              "│ Embarked ┆ count │\n",
              "│ ---      ┆ ---   │\n",
              "│ str      ┆ u32   │\n",
              "╞══════════╪═══════╡\n",
              "│ Q        ┆ 46    │\n",
              "│ C        ┆ 102   │\n",
              "│ S        ┆ 270   │\n",
              "└──────────┴───────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (3, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Embarked</th><th>count</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>&quot;Q&quot;</td><td>46</td></tr><tr><td>&quot;C&quot;</td><td>102</td></tr><tr><td>&quot;S&quot;</td><td>270</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#slice dataframe\n",
        "\n",
        "pd_df[12:28]\n",
        "\n",
        "pl_df[12:28]"
      ],
      "metadata": {
        "id": "L5PQzXV0cuIM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ver si habian adolescentes en el titanic\n",
        "\n",
        "pd_df[pd_df['Age'].between(13, 19)]\n"
      ],
      "metadata": {
        "id": "M7HXCTezdFYJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.filter(pl_df['Age'].is_between(13, 19))"
      ],
      "metadata": {
        "id": "6Xj93CK4dYmv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#llenar valores vacios\n",
        "\n",
        "pd_df['Embarked'].fillna('S', inplace = True)\n"
      ],
      "metadata": {
        "id": "uGSsE217duuo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df = pl_df.with_columns(pl.col('Embarked').fill_null('S'))\n"
      ],
      "metadata": {
        "id": "R56ps3BydzcA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#agrupar por sexo y clase\n",
        "\n",
        "pd_df.groupby(by = ['Pclass', 'Sex']).count()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "LGDZBPMUd-zN",
        "outputId": "91fbbb62-2cd8-4134-f78d-f18fa88f2924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               PassengerId  Name  Age  SibSp  Parch  Ticket  Fare  Cabin  \\\n",
              "Pclass Sex                                                                 \n",
              "1      female           50    50   48     50     50      50    50     40   \n",
              "       male             57    57   50     57     57      57    57     40   \n",
              "2      female           30    30   29     30     30      30    30      3   \n",
              "       male             63    63   59     63     63      63    63      4   \n",
              "3      female           72    72   50     72     72      72    72      1   \n",
              "       male            146   146   96    146    146     146   145      3   \n",
              "\n",
              "               Embarked  \n",
              "Pclass Sex               \n",
              "1      female        50  \n",
              "       male          57  \n",
              "2      female        30  \n",
              "       male          63  \n",
              "3      female        72  \n",
              "       male         146  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca5e9f2e-46f7-4f33-af10-be6c12b12305\" 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></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>female</th>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>48</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>57</td>\n",
              "      <td>57</td>\n",
              "      <td>50</td>\n",
              "      <td>57</td>\n",
              "      <td>57</td>\n",
              "      <td>57</td>\n",
              "      <td>57</td>\n",
              "      <td>40</td>\n",
              "      <td>57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>female</th>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>59</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>4</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
              "      <th>female</th>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>50</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>1</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>96</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>146</td>\n",
              "      <td>145</td>\n",
              "      <td>3</td>\n",
              "      <td>146</td>\n",
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              "  </tbody>\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-ca5e9f2e-46f7-4f33-af10-be6c12b12305 button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --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",
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              "    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",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd_df\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22,\n        \"min\": 29,\n        \"max\": 96,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          50,\n          96,\n          29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 145,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          145\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 1,\n        \"max\": 40,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 30,\n        \"max\": 146,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          50,\n          57,\n          146\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.groupby(by = ['Pclass', 'Sex']).count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "VtOT88oneE5q",
        "outputId": "eef90729-a5d3-4d15-e10c-ba8452a33319"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-35-d28c6a695643>:1: DeprecationWarning: `groupby` is deprecated. It has been renamed to `group_by`.\n",
            "  pl_df.groupby(by = ['Pclass', 'Sex']).count()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (6, 3)\n",
              "┌────────┬────────┬───────┐\n",
              "│ Pclass ┆ Sex    ┆ count │\n",
              "│ ---    ┆ ---    ┆ ---   │\n",
              "│ i64    ┆ str    ┆ u32   │\n",
              "╞════════╪════════╪═══════╡\n",
              "│ 3      ┆ male   ┆ 146   │\n",
              "│ 3      ┆ female ┆ 72    │\n",
              "│ 2      ┆ male   ┆ 63    │\n",
              "│ 1      ┆ female ┆ 50    │\n",
              "│ 1      ┆ male   ┆ 57    │\n",
              "│ 2      ┆ female ┆ 30    │\n",
              "└────────┴────────┴───────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (6, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Pclass</th><th>Sex</th><th>count</th></tr><tr><td>i64</td><td>str</td><td>u32</td></tr></thead><tbody><tr><td>3</td><td>&quot;male&quot;</td><td>146</td></tr><tr><td>3</td><td>&quot;female&quot;</td><td>72</td></tr><tr><td>2</td><td>&quot;male&quot;</td><td>63</td></tr><tr><td>1</td><td>&quot;female&quot;</td><td>50</td></tr><tr><td>1</td><td>&quot;male&quot;</td><td>57</td></tr><tr><td>2</td><td>&quot;female&quot;</td><td>30</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ordenar valores"
      ],
      "metadata": {
        "id": "Ys9TkP2NlBr_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd_df.sort_values(\"Age\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "Nf82kfW-j67H",
        "outputId": "2b28ca84-c09c-4d20-ac30-1f595468feae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Pclass                                     Name     Sex  \\\n",
              "354         1246       3  Dean, Miss. Elizabeth Gladys Millvina\"\"  female   \n",
              "201         1093       3  Danbom, Master. Gilbert Sigvard Emanuel    male   \n",
              "281         1173       3           Peacock, Master. Alfred Edward    male   \n",
              "307         1199       3                Aks, Master. Philip Frank    male   \n",
              "250         1142       2                    West, Miss. Barbara J  female   \n",
              "..           ...     ...                                      ...     ...   \n",
              "408         1300       3          Riordan, Miss. Johanna Hannah\"\"  female   \n",
              "410         1302       3                   Naughton, Miss. Hannah  female   \n",
              "413         1305       3                       Spector, Mr. Woolf    male   \n",
              "416         1308       3                      Ware, Mr. Frederick    male   \n",
              "417         1309       3                 Peter, Master. Michael J    male   \n",
              "\n",
              "      Age  SibSp  Parch              Ticket     Fare Cabin Embarked  \n",
              "354  0.17      1      2           C.A. 2315  20.5750   NaN        S  \n",
              "201  0.33      0      2              347080  14.4000   NaN        S  \n",
              "281  0.75      1      1  SOTON/O.Q. 3101315  13.7750   NaN        S  \n",
              "307  0.83      0      1              392091   9.3500   NaN        S  \n",
              "250  0.92      1      2          C.A. 34651  27.7500   NaN        S  \n",
              "..    ...    ...    ...                 ...      ...   ...      ...  \n",
              "408   NaN      0      0              334915   7.7208   NaN        Q  \n",
              "410   NaN      0      0              365237   7.7500   NaN        Q  \n",
              "413   NaN      0      0           A.5. 3236   8.0500   NaN        S  \n",
              "416   NaN      0      0              359309   8.0500   NaN        S  \n",
              "417   NaN      1      1                2668  22.3583   NaN        C  \n",
              "\n",
              "[418 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b168c5bc-a0ed-45e1-8cc8-79e939073676\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
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              "    <tr>\n",
              "      <th>354</th>\n",
              "      <td>1246</td>\n",
              "      <td>3</td>\n",
              "      <td>Dean, Miss. Elizabeth Gladys Millvina\"\"</td>\n",
              "      <td>female</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>C.A. 2315</td>\n",
              "      <td>20.5750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>201</th>\n",
              "      <td>1093</td>\n",
              "      <td>3</td>\n",
              "      <td>Danbom, Master. Gilbert Sigvard Emanuel</td>\n",
              "      <td>male</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347080</td>\n",
              "      <td>14.4000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>281</th>\n",
              "      <td>1173</td>\n",
              "      <td>3</td>\n",
              "      <td>Peacock, Master. Alfred Edward</td>\n",
              "      <td>male</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>SOTON/O.Q. 3101315</td>\n",
              "      <td>13.7750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>1199</td>\n",
              "      <td>3</td>\n",
              "      <td>Aks, Master. Philip Frank</td>\n",
              "      <td>male</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>392091</td>\n",
              "      <td>9.3500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>1142</td>\n",
              "      <td>2</td>\n",
              "      <td>West, Miss. Barbara J</td>\n",
              "      <td>female</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>C.A. 34651</td>\n",
              "      <td>27.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>408</th>\n",
              "      <td>1300</td>\n",
              "      <td>3</td>\n",
              "      <td>Riordan, Miss. Johanna Hannah\"\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>334915</td>\n",
              "      <td>7.7208</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>1302</td>\n",
              "      <td>3</td>\n",
              "      <td>Naughton, Miss. Hannah</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>365237</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>1305</td>\n",
              "      <td>3</td>\n",
              "      <td>Spector, Mr. Woolf</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A.5. 3236</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>1308</td>\n",
              "      <td>3</td>\n",
              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>418 rows × 11 columns</p>\n",
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              "        document.querySelector('#' + key + ' button');\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd_df\",\n  \"rows\": 418,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 120,\n        \"min\": 892,\n        \"max\": 1309,\n        \"num_unique_values\": 418,\n        \"samples\": [\n          1044,\n          894,\n          1160\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 418,\n        \"samples\": [\n          \"Storey, Mr. Thomas\",\n          \"Myles, Mr. Thomas Francis\",\n          \"Howard, Miss. May Elizabeth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.181209235624422,\n        \"min\": 0.17,\n        \"max\": 76.0,\n        \"num_unique_values\": 79,\n        \"samples\": [\n          24.0,\n          0.17\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 9,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 363,\n        \"samples\": [\n          \"2003\",\n          \"S.O.C. 14879\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.907576179973844,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 169,\n        \"samples\": [\n          25.7,\n          7.8958\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 76,\n        \"samples\": [\n          \"D30\",\n          \"A29\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.sort(\"Age\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1hhwyvFMkKLW",
        "outputId": "96a7c0c5-b394-488c-c709-3710049dba20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (418, 11)\n",
              "┌─────────────┬────────┬─────────────────┬────────┬───┬────────────┬──────────┬─────────┬──────────┐\n",
              "│ PassengerId ┆ Pclass ┆ Name            ┆ Sex    ┆ … ┆ Ticket     ┆ Fare     ┆ Cabin   ┆ Embarked │\n",
              "│ ---         ┆ ---    ┆ ---             ┆ ---    ┆   ┆ ---        ┆ ---      ┆ ---     ┆ ---      │\n",
              "│ i64         ┆ i64    ┆ str             ┆ str    ┆   ┆ str        ┆ f64      ┆ str     ┆ str      │\n",
              "╞═════════════╪════════╪═════════════════╪════════╪═══╪════════════╪══════════╪═════════╪══════════╡\n",
              "│ 902         ┆ 3      ┆ Ilieff, Mr.     ┆ male   ┆ … ┆ 349220     ┆ 7.8958   ┆ null    ┆ S        │\n",
              "│             ┆        ┆ Ylio            ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 914         ┆ 1      ┆ Flegenheim,     ┆ female ┆ … ┆ PC 17598   ┆ 31.6833  ┆ null    ┆ S        │\n",
              "│             ┆        ┆ Mrs. Alfred     ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│             ┆        ┆ (Antoine…       ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 921         ┆ 3      ┆ Samaan, Mr.     ┆ male   ┆ … ┆ 2662       ┆ 21.6792  ┆ null    ┆ C        │\n",
              "│             ┆        ┆ Elias           ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 925         ┆ 3      ┆ Johnston, Mrs.  ┆ female ┆ … ┆ W./C. 6607 ┆ 23.45    ┆ null    ┆ S        │\n",
              "│             ┆        ┆ Andrew G        ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│             ┆        ┆ (Elizabe…       ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ …           ┆ …      ┆ …               ┆ …      ┆ … ┆ …          ┆ …        ┆ …       ┆ …        │\n",
              "│ 1128        ┆ 1      ┆ Warren, Mr.     ┆ male   ┆ … ┆ 110813     ┆ 75.25    ┆ D37     ┆ C        │\n",
              "│             ┆        ┆ Frank Manley    ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 1197        ┆ 1      ┆ Crosby, Mrs.    ┆ female ┆ … ┆ 112901     ┆ 26.55    ┆ B26     ┆ S        │\n",
              "│             ┆        ┆ Edward Gifford  ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│             ┆        ┆ (Cat…           ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 973         ┆ 1      ┆ Straus, Mr.     ┆ male   ┆ … ┆ PC 17483   ┆ 221.7792 ┆ C55 C57 ┆ S        │\n",
              "│             ┆        ┆ Isidor          ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│ 988         ┆ 1      ┆ Cavendish, Mrs. ┆ female ┆ … ┆ 19877      ┆ 78.85    ┆ C46     ┆ S        │\n",
              "│             ┆        ┆ Tyrell William  ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "│             ┆        ┆ (…              ┆        ┆   ┆            ┆          ┆         ┆          │\n",
              "└─────────────┴────────┴─────────────────┴────────┴───┴────────────┴──────────┴─────────┴──────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (418, 11)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PassengerId</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th></tr><tr><td>i64</td><td>i64</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>i64</td><td>str</td><td>f64</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>902</td><td>3</td><td>&quot;Ilieff, Mr. Yl…</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;349220&quot;</td><td>7.8958</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>914</td><td>1</td><td>&quot;Flegenheim, Mr…</td><td>&quot;female&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;PC 17598&quot;</td><td>31.6833</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>921</td><td>3</td><td>&quot;Samaan, Mr. El…</td><td>&quot;male&quot;</td><td>null</td><td>2</td><td>0</td><td>&quot;2662&quot;</td><td>21.6792</td><td>null</td><td>&quot;C&quot;</td></tr><tr><td>925</td><td>3</td><td>&quot;Johnston, Mrs.…</td><td>&quot;female&quot;</td><td>null</td><td>1</td><td>2</td><td>&quot;W./C. 6607&quot;</td><td>23.45</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>928</td><td>3</td><td>&quot;Roth, Miss. Sa…</td><td>&quot;female&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;342712&quot;</td><td>8.05</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>931</td><td>3</td><td>&quot;Hee, Mr. Ling&quot;</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;1601&quot;</td><td>56.4958</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>933</td><td>1</td><td>&quot;Franklin, Mr. …</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;113778&quot;</td><td>26.55</td><td>&quot;D34&quot;</td><td>&quot;S&quot;</td></tr><tr><td>939</td><td>3</td><td>&quot;Shaughnessy, M…</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;370374&quot;</td><td>7.75</td><td>null</td><td>&quot;Q&quot;</td></tr><tr><td>946</td><td>2</td><td>&quot;Mangiavacchi, …</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;SC/A.3 2861&quot;</td><td>15.5792</td><td>null</td><td>&quot;C&quot;</td></tr><tr><td>950</td><td>3</td><td>&quot;Davison, Mr. T…</td><td>&quot;male&quot;</td><td>null</td><td>1</td><td>0</td><td>&quot;386525&quot;</td><td>16.1</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>957</td><td>2</td><td>&quot;Corey, Mrs. Pe…</td><td>&quot;female&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;F.C.C. 13534&quot;</td><td>21.0</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>968</td><td>3</td><td>&quot;Miles, Mr. Fra…</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;359306&quot;</td><td>8.05</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1105</td><td>2</td><td>&quot;Howard, Mrs. B…</td><td>&quot;female&quot;</td><td>60.0</td><td>1</td><td>0</td><td>&quot;24065&quot;</td><td>26.0</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>1044</td><td>3</td><td>&quot;Storey, Mr. Th…</td><td>&quot;male&quot;</td><td>60.5</td><td>0</td><td>0</td><td>&quot;3701&quot;</td><td>null</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>1034</td><td>1</td><td>&quot;Ryerson, Mr. A…</td><td>&quot;male&quot;</td><td>61.0</td><td>1</td><td>3</td><td>&quot;PC 17608&quot;</td><td>262.375</td><td>&quot;B57 B59 B63 B6…</td><td>&quot;C&quot;</td></tr><tr><td>1085</td><td>2</td><td>&quot;Lingane, Mr. J…</td><td>&quot;male&quot;</td><td>61.0</td><td>0</td><td>0</td><td>&quot;235509&quot;</td><td>12.35</td><td>null</td><td>&quot;Q&quot;</td></tr><tr><td>894</td><td>2</td><td>&quot;Myles, Mr. Tho…</td><td>&quot;male&quot;</td><td>62.0</td><td>0</td><td>0</td><td>&quot;240276&quot;</td><td>9.6875</td><td>null</td><td>&quot;Q&quot;</td></tr><tr><td>905</td><td>2</td><td>&quot;Howard, Mr. Be…</td><td>&quot;male&quot;</td><td>63.0</td><td>1</td><td>0</td><td>&quot;24065&quot;</td><td>26.0</td><td>null</td><td>&quot;S&quot;</td></tr><tr><td>1006</td><td>1</td><td>&quot;Straus, Mrs. I…</td><td>&quot;female&quot;</td><td>63.0</td><td>1</td><td>0</td><td>&quot;PC 17483&quot;</td><td>221.7792</td><td>&quot;C55 C57&quot;</td><td>&quot;S&quot;</td></tr><tr><td>1071</td><td>1</td><td>&quot;Compton, Mrs. …</td><td>&quot;female&quot;</td><td>64.0</td><td>0</td><td>2</td><td>&quot;PC 17756&quot;</td><td>83.1583</td><td>&quot;E45&quot;</td><td>&quot;C&quot;</td></tr><tr><td>1128</td><td>1</td><td>&quot;Warren, Mr. Fr…</td><td>&quot;male&quot;</td><td>64.0</td><td>1</td><td>0</td><td>&quot;110813&quot;</td><td>75.25</td><td>&quot;D37&quot;</td><td>&quot;C&quot;</td></tr><tr><td>1197</td><td>1</td><td>&quot;Crosby, Mrs. E…</td><td>&quot;female&quot;</td><td>64.0</td><td>1</td><td>1</td><td>&quot;112901&quot;</td><td>26.55</td><td>&quot;B26&quot;</td><td>&quot;S&quot;</td></tr><tr><td>973</td><td>1</td><td>&quot;Straus, Mr. Is…</td><td>&quot;male&quot;</td><td>67.0</td><td>1</td><td>0</td><td>&quot;PC 17483&quot;</td><td>221.7792</td><td>&quot;C55 C57&quot;</td><td>&quot;S&quot;</td></tr><tr><td>988</td><td>1</td><td>&quot;Cavendish, Mrs…</td><td>&quot;female&quot;</td><td>76.0</td><td>1</td><td>0</td><td>&quot;19877&quot;</td><td>78.85</td><td>&quot;C46&quot;</td><td>&quot;S&quot;</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.select(\n",
        "        [\n",
        "            pl.col(\"Pclass\"),\n",
        "            pl.col(\"Name\").str.to_lowercase(),\n",
        "            pl.col(\"Age\").round(2)\n",
        "        ]\n",
        "    )"
      ],
      "metadata": {
        "id": "UOXNvY0fgGez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.select(pl.col(\"Name\")).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "zClBSX71gM9r",
        "outputId": "5ac8c941-ef33-48ed-c1a8-b88c23cd9246"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (5, 1)\n",
              "┌──────────────────────────────────────────────┐\n",
              "│ Name                                         │\n",
              "│ ---                                          │\n",
              "│ str                                          │\n",
              "╞══════════════════════════════════════════════╡\n",
              "│ Kelly, Mr. James                             │\n",
              "│ Wilkes, Mrs. James (Ellen Needs)             │\n",
              "│ Myles, Mr. Thomas Francis                    │\n",
              "│ Wirz, Mr. Albert                             │\n",
              "│ Hirvonen, Mrs. Alexander (Helga E Lindqvist) │\n",
              "└──────────────────────────────────────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (5, 1)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Name</th></tr><tr><td>str</td></tr></thead><tbody><tr><td>&quot;Kelly, Mr. James&quot;</td></tr><tr><td>&quot;Wilkes, Mrs. James (Ellen Needs)&quot;</td></tr><tr><td>&quot;Myles, Mr. Thomas Francis&quot;</td></tr><tr><td>&quot;Wirz, Mr. Albert&quot;</td></tr><tr><td>&quot;Hirvonen, Mrs. Alexander (Helga E Lindqvist)&quot;</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split string values in column s in exactly 2 parts and assign each part to a new column.\n",
        "pl_df.with_columns(\n",
        "    [\n",
        "        pl.col(\"Name\")\n",
        "        .str.splitn(\" \", 2)\n",
        "        .struct.rename_fields([\"Last Name\", \"Second part\"])\n",
        "        .alias(\"fields\"),\n",
        "    ]\n",
        ").unnest(\"fields\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VAr5b4YuhdF7",
        "outputId": "8b93ceb0-0f97-42fa-d14c-ab9b1dfcdf67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (418, 13)\n",
              "┌─────────────┬────────┬───────────────┬────────┬───┬───────┬──────────┬───────────┬───────────────┐\n",
              "│ PassengerId ┆ Pclass ┆ Name          ┆ Sex    ┆ … ┆ Cabin ┆ Embarked ┆ Last Name ┆ Second part   │\n",
              "│ ---         ┆ ---    ┆ ---           ┆ ---    ┆   ┆ ---   ┆ ---      ┆ ---       ┆ ---           │\n",
              "│ i64         ┆ i64    ┆ str           ┆ str    ┆   ┆ str   ┆ str      ┆ str       ┆ str           │\n",
              "╞═════════════╪════════╪═══════════════╪════════╪═══╪═══════╪══════════╪═══════════╪═══════════════╡\n",
              "│ 892         ┆ 3      ┆ Kelly, Mr.    ┆ male   ┆ … ┆ null  ┆ Q        ┆ Kelly,    ┆ Mr. James     │\n",
              "│             ┆        ┆ James         ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 893         ┆ 3      ┆ Wilkes, Mrs.  ┆ female ┆ … ┆ null  ┆ S        ┆ Wilkes,   ┆ Mrs. James    │\n",
              "│             ┆        ┆ James (Ellen  ┆        ┆   ┆       ┆          ┆           ┆ (Ellen Needs) │\n",
              "│             ┆        ┆ Needs)        ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 894         ┆ 2      ┆ Myles, Mr.    ┆ male   ┆ … ┆ null  ┆ Q        ┆ Myles,    ┆ Mr. Thomas    │\n",
              "│             ┆        ┆ Thomas        ┆        ┆   ┆       ┆          ┆           ┆ Francis       │\n",
              "│             ┆        ┆ Francis       ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 895         ┆ 3      ┆ Wirz, Mr.     ┆ male   ┆ … ┆ null  ┆ S        ┆ Wirz,     ┆ Mr. Albert    │\n",
              "│             ┆        ┆ Albert        ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ …           ┆ …      ┆ …             ┆ …      ┆ … ┆ …     ┆ …        ┆ …         ┆ …             │\n",
              "│ 1306        ┆ 1      ┆ Oliva y       ┆ female ┆ … ┆ C105  ┆ C        ┆ Oliva     ┆ y Ocana,      │\n",
              "│             ┆        ┆ Ocana, Dona.  ┆        ┆   ┆       ┆          ┆           ┆ Dona. Fermina │\n",
              "│             ┆        ┆ Fermina       ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 1307        ┆ 3      ┆ Saether, Mr.  ┆ male   ┆ … ┆ null  ┆ S        ┆ Saether,  ┆ Mr. Simon     │\n",
              "│             ┆        ┆ Simon         ┆        ┆   ┆       ┆          ┆           ┆ Sivertsen     │\n",
              "│             ┆        ┆ Sivertsen     ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 1308        ┆ 3      ┆ Ware, Mr.     ┆ male   ┆ … ┆ null  ┆ S        ┆ Ware,     ┆ Mr. Frederick │\n",
              "│             ┆        ┆ Frederick     ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "│ 1309        ┆ 3      ┆ Peter,        ┆ male   ┆ … ┆ null  ┆ C        ┆ Peter,    ┆ Master.       │\n",
              "│             ┆        ┆ Master.       ┆        ┆   ┆       ┆          ┆           ┆ Michael J     │\n",
              "│             ┆        ┆ Michael J     ┆        ┆   ┆       ┆          ┆           ┆               │\n",
              "└─────────────┴────────┴───────────────┴────────┴───┴───────┴──────────┴───────────┴───────────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (418, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>PassengerId</th><th>Pclass</th><th>Name</th><th>Sex</th><th>Age</th><th>SibSp</th><th>Parch</th><th>Ticket</th><th>Fare</th><th>Cabin</th><th>Embarked</th><th>Last Name</th><th>Second part</th></tr><tr><td>i64</td><td>i64</td><td>str</td><td>str</td><td>f64</td><td>i64</td><td>i64</td><td>str</td><td>f64</td><td>str</td><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>892</td><td>3</td><td>&quot;Kelly, Mr. James&quot;</td><td>&quot;male&quot;</td><td>34.5</td><td>0</td><td>0</td><td>&quot;330911&quot;</td><td>7.8292</td><td>null</td><td>&quot;Q&quot;</td><td>&quot;Kelly,&quot;</td><td>&quot;Mr. James&quot;</td></tr><tr><td>893</td><td>3</td><td>&quot;Wilkes, Mrs. James (Ellen Needs)&quot;</td><td>&quot;female&quot;</td><td>47.0</td><td>1</td><td>0</td><td>&quot;363272&quot;</td><td>7.0</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Wilkes,&quot;</td><td>&quot;Mrs. James (Ellen Needs)&quot;</td></tr><tr><td>894</td><td>2</td><td>&quot;Myles, Mr. Thomas Francis&quot;</td><td>&quot;male&quot;</td><td>62.0</td><td>0</td><td>0</td><td>&quot;240276&quot;</td><td>9.6875</td><td>null</td><td>&quot;Q&quot;</td><td>&quot;Myles,&quot;</td><td>&quot;Mr. Thomas Francis&quot;</td></tr><tr><td>895</td><td>3</td><td>&quot;Wirz, Mr. Albert&quot;</td><td>&quot;male&quot;</td><td>27.0</td><td>0</td><td>0</td><td>&quot;315154&quot;</td><td>8.6625</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Wirz,&quot;</td><td>&quot;Mr. Albert&quot;</td></tr><tr><td>896</td><td>3</td><td>&quot;Hirvonen, Mrs. Alexander (Helga E Lindqvist)&quot;</td><td>&quot;female&quot;</td><td>22.0</td><td>1</td><td>1</td><td>&quot;3101298&quot;</td><td>12.2875</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Hirvonen,&quot;</td><td>&quot;Mrs. Alexander (Helga E Lindqvist)&quot;</td></tr><tr><td>897</td><td>3</td><td>&quot;Svensson, Mr. Johan Cervin&quot;</td><td>&quot;male&quot;</td><td>14.0</td><td>0</td><td>0</td><td>&quot;7538&quot;</td><td>9.225</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Svensson,&quot;</td><td>&quot;Mr. Johan Cervin&quot;</td></tr><tr><td>898</td><td>3</td><td>&quot;Connolly, Miss. Kate&quot;</td><td>&quot;female&quot;</td><td>30.0</td><td>0</td><td>0</td><td>&quot;330972&quot;</td><td>7.6292</td><td>null</td><td>&quot;Q&quot;</td><td>&quot;Connolly,&quot;</td><td>&quot;Miss. Kate&quot;</td></tr><tr><td>899</td><td>2</td><td>&quot;Caldwell, Mr. Albert Francis&quot;</td><td>&quot;male&quot;</td><td>26.0</td><td>1</td><td>1</td><td>&quot;248738&quot;</td><td>29.0</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Caldwell,&quot;</td><td>&quot;Mr. Albert Francis&quot;</td></tr><tr><td>900</td><td>3</td><td>&quot;Abrahim, Mrs. Joseph (Sophie Halaut Easu)&quot;</td><td>&quot;female&quot;</td><td>18.0</td><td>0</td><td>0</td><td>&quot;2657&quot;</td><td>7.2292</td><td>null</td><td>&quot;C&quot;</td><td>&quot;Abrahim,&quot;</td><td>&quot;Mrs. Joseph (Sophie Halaut Easu)&quot;</td></tr><tr><td>901</td><td>3</td><td>&quot;Davies, Mr. John Samuel&quot;</td><td>&quot;male&quot;</td><td>21.0</td><td>2</td><td>0</td><td>&quot;A/4 48871&quot;</td><td>24.15</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Davies,&quot;</td><td>&quot;Mr. John Samuel&quot;</td></tr><tr><td>902</td><td>3</td><td>&quot;Ilieff, Mr. Ylio&quot;</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;349220&quot;</td><td>7.8958</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Ilieff,&quot;</td><td>&quot;Mr. Ylio&quot;</td></tr><tr><td>903</td><td>1</td><td>&quot;Jones, Mr. Charles Cresson&quot;</td><td>&quot;male&quot;</td><td>46.0</td><td>0</td><td>0</td><td>&quot;694&quot;</td><td>26.0</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Jones,&quot;</td><td>&quot;Mr. Charles Cresson&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>1298</td><td>2</td><td>&quot;Ware, Mr. William Jeffery&quot;</td><td>&quot;male&quot;</td><td>23.0</td><td>1</td><td>0</td><td>&quot;28666&quot;</td><td>10.5</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Ware,&quot;</td><td>&quot;Mr. William Jeffery&quot;</td></tr><tr><td>1299</td><td>1</td><td>&quot;Widener, Mr. George Dunton&quot;</td><td>&quot;male&quot;</td><td>50.0</td><td>1</td><td>1</td><td>&quot;113503&quot;</td><td>211.5</td><td>&quot;C80&quot;</td><td>&quot;C&quot;</td><td>&quot;Widener,&quot;</td><td>&quot;Mr. George Dunton&quot;</td></tr><tr><td>1300</td><td>3</td><td>&quot;Riordan, Miss. Johanna Hannah&quot;&quot;&quot;</td><td>&quot;female&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;334915&quot;</td><td>7.7208</td><td>null</td><td>&quot;Q&quot;</td><td>&quot;Riordan,&quot;</td><td>&quot;Miss. Johanna Hannah&quot;&quot;&quot;</td></tr><tr><td>1301</td><td>3</td><td>&quot;Peacock, Miss. Treasteall&quot;</td><td>&quot;female&quot;</td><td>3.0</td><td>1</td><td>1</td><td>&quot;SOTON/O.Q. 3101315&quot;</td><td>13.775</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Peacock,&quot;</td><td>&quot;Miss. Treasteall&quot;</td></tr><tr><td>1302</td><td>3</td><td>&quot;Naughton, Miss. Hannah&quot;</td><td>&quot;female&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;365237&quot;</td><td>7.75</td><td>null</td><td>&quot;Q&quot;</td><td>&quot;Naughton,&quot;</td><td>&quot;Miss. Hannah&quot;</td></tr><tr><td>1303</td><td>1</td><td>&quot;Minahan, Mrs. William Edward (Lillian E Thorpe)&quot;</td><td>&quot;female&quot;</td><td>37.0</td><td>1</td><td>0</td><td>&quot;19928&quot;</td><td>90.0</td><td>&quot;C78&quot;</td><td>&quot;Q&quot;</td><td>&quot;Minahan,&quot;</td><td>&quot;Mrs. William Edward (Lillian E Thorpe)&quot;</td></tr><tr><td>1304</td><td>3</td><td>&quot;Henriksson, Miss. Jenny Lovisa&quot;</td><td>&quot;female&quot;</td><td>28.0</td><td>0</td><td>0</td><td>&quot;347086&quot;</td><td>7.775</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Henriksson,&quot;</td><td>&quot;Miss. Jenny Lovisa&quot;</td></tr><tr><td>1305</td><td>3</td><td>&quot;Spector, Mr. Woolf&quot;</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;A.5. 3236&quot;</td><td>8.05</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Spector,&quot;</td><td>&quot;Mr. Woolf&quot;</td></tr><tr><td>1306</td><td>1</td><td>&quot;Oliva y Ocana, Dona. Fermina&quot;</td><td>&quot;female&quot;</td><td>39.0</td><td>0</td><td>0</td><td>&quot;PC 17758&quot;</td><td>108.9</td><td>&quot;C105&quot;</td><td>&quot;C&quot;</td><td>&quot;Oliva&quot;</td><td>&quot;y Ocana, Dona. Fermina&quot;</td></tr><tr><td>1307</td><td>3</td><td>&quot;Saether, Mr. Simon Sivertsen&quot;</td><td>&quot;male&quot;</td><td>38.5</td><td>0</td><td>0</td><td>&quot;SOTON/O.Q. 3101262&quot;</td><td>7.25</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Saether,&quot;</td><td>&quot;Mr. Simon Sivertsen&quot;</td></tr><tr><td>1308</td><td>3</td><td>&quot;Ware, Mr. Frederick&quot;</td><td>&quot;male&quot;</td><td>null</td><td>0</td><td>0</td><td>&quot;359309&quot;</td><td>8.05</td><td>null</td><td>&quot;S&quot;</td><td>&quot;Ware,&quot;</td><td>&quot;Mr. Frederick&quot;</td></tr><tr><td>1309</td><td>3</td><td>&quot;Peter, Master. Michael J&quot;</td><td>&quot;male&quot;</td><td>null</td><td>1</td><td>1</td><td>&quot;2668&quot;</td><td>22.3583</td><td>null</td><td>&quot;C&quot;</td><td>&quot;Peter,&quot;</td><td>&quot;Master. Michael J&quot;</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2uo7VqgiiCf",
        "outputId": "dbf5d638-bb8d-4a9b-d857-18cbd85a74e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['PassengerId',\n",
              " 'Survived',\n",
              " 'Pclass',\n",
              " 'Name',\n",
              " 'Sex',\n",
              " 'Age',\n",
              " 'SibSp',\n",
              " 'Parch',\n",
              " 'Ticket',\n",
              " 'Fare',\n",
              " 'Cabin',\n",
              " 'Embarked']"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.group_by([\"Survived\",\"Pclass\"]).agg(pl.col(\"PassengerId\").count().alias(\"counts\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "nlw3jjVshvhh",
        "outputId": "a4b78f8c-53a7-4f76-a7a0-c1d81b919cf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (6, 3)\n",
              "┌──────────┬────────┬────────┐\n",
              "│ Survived ┆ Pclass ┆ counts │\n",
              "│ ---      ┆ ---    ┆ ---    │\n",
              "│ i64      ┆ i64    ┆ u32    │\n",
              "╞══════════╪════════╪════════╡\n",
              "│ 1        ┆ 1      ┆ 136    │\n",
              "│ 0        ┆ 2      ┆ 97     │\n",
              "│ 0        ┆ 3      ┆ 372    │\n",
              "│ 1        ┆ 3      ┆ 119    │\n",
              "│ 0        ┆ 1      ┆ 80     │\n",
              "│ 1        ┆ 2      ┆ 87     │\n",
              "└──────────┴────────┴────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (6, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Survived</th><th>Pclass</th><th>counts</th></tr><tr><td>i64</td><td>i64</td><td>u32</td></tr></thead><tbody><tr><td>1</td><td>1</td><td>136</td></tr><tr><td>0</td><td>2</td><td>97</td></tr><tr><td>0</td><td>3</td><td>372</td></tr><tr><td>1</td><td>3</td><td>119</td></tr><tr><td>0</td><td>1</td><td>80</td></tr><tr><td>1</td><td>2</td><td>87</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl_df.group_by([\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")\n",
        "      ).with_columns(\n",
        "        100 * (pl.col(\"counts\")/ pl.col(\"counts\").sum().over(\"Pclass\").alias(\"% Survived\"\n",
        "        )\n",
        "        )\n",
        "      ).sort([\"Pclass\", \"Survived\"],descending=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "a1xh0NSEirmG",
        "outputId": "19eb3bc6-8109-4880-b669-e3d022b04338"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (6, 4)\n",
              "┌──────────┬────────┬────────┬───────────┐\n",
              "│ Survived ┆ Pclass ┆ counts ┆ literal   │\n",
              "│ ---      ┆ ---    ┆ ---    ┆ ---       │\n",
              "│ i64      ┆ i64    ┆ u32    ┆ f64       │\n",
              "╞══════════╪════════╪════════╪═══════════╡\n",
              "│ 1        ┆ 3      ┆ 119    ┆ 24.236253 │\n",
              "│ 0        ┆ 3      ┆ 372    ┆ 75.763747 │\n",
              "│ 1        ┆ 2      ┆ 87     ┆ 47.282609 │\n",
              "│ 0        ┆ 2      ┆ 97     ┆ 52.717391 │\n",
              "│ 1        ┆ 1      ┆ 136    ┆ 62.962963 │\n",
              "│ 0        ┆ 1      ┆ 80     ┆ 37.037037 │\n",
              "└──────────┴────────┴────────┴───────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (6, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Survived</th><th>Pclass</th><th>counts</th><th>literal</th></tr><tr><td>i64</td><td>i64</td><td>u32</td><td>f64</td></tr></thead><tbody><tr><td>1</td><td>3</td><td>119</td><td>24.236253</td></tr><tr><td>0</td><td>3</td><td>372</td><td>75.763747</td></tr><tr><td>1</td><td>2</td><td>87</td><td>47.282609</td></tr><tr><td>0</td><td>2</td><td>97</td><td>52.717391</td></tr><tr><td>1</td><td>1</td><td>136</td><td>62.962963</td></tr><tr><td>0</td><td>1</td><td>80</td><td>37.037037</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl.scan_csv(file).group_by(\n",
        "    [\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "n6zzHzEmjWdW",
        "outputId": "609384eb-eea1-4552-856d-38730153e536"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<LazyFrame [3 cols, {\"Survived\": Int64 … \"counts\": UInt32}] at 0x79BA4DCC7310>"
            ],
            "text/html": [
              "<h4>NAIVE QUERY PLAN</h4><p>run <b>LazyFrame.show_graph()</b> to see the optimized version</p><?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
              "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
              " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
              "<!-- Generated by graphviz version 2.43.0 (0)\n",
              " -->\n",
              "<!-- Title: polars_query Pages: 1 -->\n",
              "<svg width=\"362pt\" height=\"150pt\"\n",
              " viewBox=\"0.00 0.00 362.00 150.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
              "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 146)\">\n",
              "<title>polars_query</title>\n",
              "<polygon fill=\"white\" stroke=\"transparent\" points=\"-4,4 -4,-146 358,-146 358,4 -4,4\"/>\n",
              "<!-- AGG [col(&quot;PassengerId&quot;).count().alias(&quot;counts&quot;)]\n",
              "BY\n",
              "[col(&quot;Survived&quot;),col(&quot;Pclass&quot;)] [(0, 0)] [(0, 0)] -->\n",
              "<g id=\"node1\" class=\"node\">\n",
              "<title>AGG [col(&quot;PassengerId&quot;).count().alias(&quot;counts&quot;)]\n",
              "BY\n",
              "[col(&quot;Survived&quot;),col(&quot;Pclass&quot;)] [(0, 0)] [(0, 0)]</title>\n",
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              "<text text-anchor=\"middle\" x=\"177\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">AGG [col(&quot;PassengerId&quot;).count().alias(&quot;counts&quot;)]</text>\n",
              "<text text-anchor=\"middle\" x=\"177\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">BY</text>\n",
              "<text text-anchor=\"middle\" x=\"177\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">[col(&quot;Survived&quot;),col(&quot;Pclass&quot;)] [(0, 0)]</text>\n",
              "</g>\n",
              "<!-- Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv;\n",
              "π */12;\n",
              "σ &#45; [(0, 1)] -->\n",
              "<g id=\"node2\" class=\"node\">\n",
              "<title>Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv;\n",
              "π */12;\n",
              "σ &#45; [(0, 1)]</title>\n",
              "<polygon fill=\"none\" stroke=\"black\" points=\"354,-53 0,-53 0,0 354,0 354,-53\"/>\n",
              "<text text-anchor=\"middle\" x=\"177\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv;</text>\n",
              "<text text-anchor=\"middle\" x=\"177\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">π */12;</text>\n",
              "<text text-anchor=\"middle\" x=\"177\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">σ &#45;</text>\n",
              "</g>\n",
              "<!-- AGG [col(&quot;PassengerId&quot;).count().alias(&quot;counts&quot;)]\n",
              "BY\n",
              "[col(&quot;Survived&quot;),col(&quot;Pclass&quot;)] [(0, 0)] [(0, 0)]&#45;&#45;Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv;\n",
              "π */12;\n",
              "σ &#45; [(0, 1)] -->\n",
              "<g id=\"edge1\" class=\"edge\">\n",
              "<title>AGG [col(&quot;PassengerId&quot;).count().alias(&quot;counts&quot;)]\n",
              "BY\n",
              "[col(&quot;Survived&quot;),col(&quot;Pclass&quot;)] [(0, 0)] [(0, 0)]&#45;&#45;Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv;\n",
              "π */12;\n",
              "σ &#45; [(0, 1)]</title>\n",
              "<path fill=\"none\" stroke=\"black\" d=\"M177,-88.87C177,-77.64 177,-64.49 177,-53.25\"/>\n",
              "</g>\n",
              "</g>\n",
              "</svg>\n"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl.scan_csv(file).group_by([\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")).explain()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "iRH-GsA4lasz",
        "outputId": "6c2efab2-e8b9-4ff2-be4f-42e69209be7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'AGGREGATE\\n\\t[col(\"PassengerId\").count().alias(\"counts\")] BY [col(\"Survived\"), col(\"Pclass\")] FROM\\n\\n    Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv\\n    PROJECT 3/12 COLUMNS'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl.scan_csv(file).filter(pl.col(\"Age\") > 50).groupby([\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")\n",
        "    ).explain()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "VmCmVLbFnRYj",
        "outputId": "666d290c-f7b7-44cb-d5a1-4f11a4a71ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-117-0e9b579059a8>:1: DeprecationWarning: `groupby` is deprecated. It has been renamed to `group_by`.\n",
            "  pl.scan_csv(file).filter(pl.col(\"Age\") > 50).groupby([\"Survived\",\"Pclass\"]).agg(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'AGGREGATE\\n\\t[col(\"PassengerId\").count().alias(\"counts\")] BY [col(\"Survived\"), col(\"Pclass\")] FROM\\n\\n    Csv SCAN /content/drive/MyDrive/dataset_polars/titanic.csv\\n    PROJECT 4/12 COLUMNS\\n    SELECTION: [(col(\"Age\")) > (50.0)]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl.scan_csv(file).filter(pl.col(\"Age\") > 50).group_by([\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")\n",
        "    ).collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "HoA5yD8cncKp",
        "outputId": "683101c1-9f47-4b26-9bab-b220f4a62fcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (6, 3)\n",
              "┌──────────┬────────┬────────┐\n",
              "│ Survived ┆ Pclass ┆ counts │\n",
              "│ ---      ┆ ---    ┆ ---    │\n",
              "│ i64      ┆ i64    ┆ u32    │\n",
              "╞══════════╪════════╪════════╡\n",
              "│ 0        ┆ 1      ┆ 21     │\n",
              "│ 0        ┆ 3      ┆ 9      │\n",
              "│ 1        ┆ 3      ┆ 1      │\n",
              "│ 1        ┆ 1      ┆ 18     │\n",
              "│ 1        ┆ 2      ┆ 3      │\n",
              "│ 0        ┆ 2      ┆ 12     │\n",
              "└──────────┴────────┴────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (6, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Survived</th><th>Pclass</th><th>counts</th></tr><tr><td>i64</td><td>i64</td><td>u32</td></tr></thead><tbody><tr><td>0</td><td>1</td><td>21</td></tr><tr><td>0</td><td>3</td><td>9</td></tr><tr><td>1</td><td>3</td><td>1</td></tr><tr><td>1</td><td>1</td><td>18</td></tr><tr><td>1</td><td>2</td><td>3</td></tr><tr><td>0</td><td>2</td><td>12</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pl.scan_csv(file).filter(pl.col(\"Age\") > 50).group_by([\"Survived\",\"Pclass\"]).agg(\n",
        "        pl.col(\"PassengerId\").count().alias(\"counts\")\n",
        "    ).fetch(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "_-sJlemGn4uq",
        "outputId": "4e53b250-7867-46d6-954a-d7250f360ae7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "shape: (3, 3)\n",
              "┌──────────┬────────┬────────┐\n",
              "│ Survived ┆ Pclass ┆ counts │\n",
              "│ ---      ┆ ---    ┆ ---    │\n",
              "│ i64      ┆ i64    ┆ u32    │\n",
              "╞══════════╪════════╪════════╡\n",
              "│ 0        ┆ 1      ┆ 1      │\n",
              "│ 1        ┆ 1      ┆ 1      │\n",
              "│ 1        ┆ 2      ┆ 1      │\n",
              "└──────────┴────────┴────────┘"
            ],
            "text/html": [
              "<div><style>\n",
              ".dataframe > thead > tr,\n",
              ".dataframe > tbody > tr {\n",
              "  text-align: right;\n",
              "  white-space: pre-wrap;\n",
              "}\n",
              "</style>\n",
              "<small>shape: (3, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>Survived</th><th>Pclass</th><th>counts</th></tr><tr><td>i64</td><td>i64</td><td>u32</td></tr></thead><tbody><tr><td>0</td><td>1</td><td>1</td></tr><tr><td>1</td><td>1</td><td>1</td></tr><tr><td>1</td><td>2</td><td>1</td></tr></tbody></table></div>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "age = pl_df.sort(\"Age\").tail(6)"
      ],
      "metadata": {
        "id": "iHWExR-Xzz4e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n"
      ],
      "metadata": {
        "id": "zCgTs7Hr0Z5i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.barh(y=pl_df[\"Sex\"],\n",
        "        width= pl_df[\"Age\"])\n",
        "ax.set_xlabel(\"Edad\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "d4K4-yR1z_lb",
        "outputId": "42059433-860f-4b53-b0bd-917d2cf490b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Edad')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "4Q7tm0ta0tbk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(data=(pl_df.with_columns(\n",
        "            [\n",
        "                pl.col(pl.Float64).log(),\n",
        "                pl.col(\"Pclass\").cast(pl.Utf8)\n",
        "            ]\n",
        "        ).to_pandas()\n",
        "    ),\n",
        "    x=\"Age\",\n",
        "    y=\"Fare\",\n",
        "    hue=\"Pclass\",\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "zkN1_WVh0yBG",
        "outputId": "5f34b551-c771-4032-c377-b796eb40aa43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7bf7656c58a0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Probemos el dataset largo de ~5GB"
      ],
      "metadata": {
        "id": "xiCpzVCOn_yO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "large_file=\"/content/drive/MyDrive/dataset_polars/202304.csv\""
      ],
      "metadata": {
        "id": "iHSKKR0xoWdl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "large_polars_df = pl.read_csv(large_file)"
      ],
      "metadata": {
        "id": "ipp0grRdodhV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "large_pandas_df = pd.read_csv(large_file)"
      ],
      "metadata": {
        "id": "G1na0i_aowxm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "large_polars_df = pl.scan_csv(large_file)"
      ],
      "metadata": {
        "id": "mOK1rIRWo42_"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}