Pandas_Tutorial.ipynb

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      "cell_type": "markdown",
      "id": "bbc58761",
      "metadata": {
        "id": "bbc58761"
      },
      "source": [
        "# Pandas and Matplotlib Tutorial Using Titanic Dataset\n",
        "Pandas is a powerful Python library used for data manipulation and analysis. It provides flexible and efficient tools for handling structured data."
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "w4nIlfYezETt"
      },
      "id": "w4nIlfYezETt",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "6cb91122",
      "metadata": {
        "id": "6cb91122"
      },
      "source": [
        "## 0. Uploading the Dataset into Google Colab\n",
        "if you have the `titanic.csv` file run below cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d8a351fb",
      "metadata": {
        "id": "d8a351fb"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "# uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if you don't have the `titanic.csv` file just run the below cell."
      ],
      "metadata": {
        "id": "5SY8Sp-w4i3w"
      },
      "id": "5SY8Sp-w4i3w"
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://github.com/payamkelich/fun/raw/main/titanic.csv -O titanic.csv\n",
        "!ls"
      ],
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        "id": "mlpEHEwL4Oeg",
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          "base_uri": "https://localhost:8080/"
        }
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      "id": "mlpEHEwL4Oeg",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-18 16:19:52--  https://github.com/payamkelich/fun/raw/main/titanic.csv\n",
            "Resolving github.com (github.com)... 140.82.116.4\n",
            "Connecting to github.com (github.com)|140.82.116.4|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/payamkelich/fun/main/titanic.csv [following]\n",
            "--2024-07-18 16:19:53--  https://raw.githubusercontent.com/payamkelich/fun/main/titanic.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 60305 (59K) [text/plain]\n",
            "Saving to: ‘titanic.csv’\n",
            "\n",
            "titanic.csv         100%[===================>]  58.89K  --.-KB/s    in 0.007s  \n",
            "\n",
            "2024-07-18 16:19:53 (8.36 MB/s) - ‘titanic.csv’ saved [60305/60305]\n",
            "\n",
            "sample_data  titanic.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c36a5d45",
      "metadata": {
        "id": "c36a5d45"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3bfcb1a",
      "metadata": {
        "id": "a3bfcb1a"
      },
      "source": [
        "## 1. Loading the Titanic Dataset"
      ]
    },
    {
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      "execution_count": null,
      "id": "1842d4ee",
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        "colab": {
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        "id": "1842d4ee",
        "outputId": "4ff1b595-5a70-49e5-dd72-0a899a2f0126"
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              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
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              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name  Gender   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
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              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
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              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4bb0f990-f72e-43a4-92e7-dbca5c4588b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8298a544-db48-4a0b-8756-83a2fa03ff91\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
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              "        document.querySelector('#id_8298a544-db48-4a0b-8756-83a2fa03ff91 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\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\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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": 54
        }
      ],
      "source": [
        "# Load the Titanic dataset\n",
        "df = pd.read_csv('titanic.csv')\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f336a9c8",
      "metadata": {
        "id": "f336a9c8"
      },
      "source": [
        "## 2. Viewing Data\n",
        "### 2.1 Display Summary Statistics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "755a7953",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "755a7953",
        "outputId": "1d2f00ff-5bed-4aa5-96a0-4faac6e520fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1f6e3d98-769b-44f1-8bda-ef45b6bec37c\" 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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      background-color: #434B5C;\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "      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-f4e2f1ec-5531-4a7b-9edc-fea2f5d9294c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 320.8159711429855,\n        \"min\": 1.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          446.0,\n          668.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.8713661874558,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.3838383838383838,\n          1.0,\n          0.4865924542648575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.2523437079694,\n        \"min\": 0.836071240977049,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          2.308641975308642,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 242.9056731818781,\n        \"min\": 0.42,\n        \"max\": 714.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          29.69911764705882,\n          28.0,\n          714.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.4908277465442,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          891.0,\n          0.5230078563411896,\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 314.65971717879,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.38159371492704824,\n          6.0,\n          0.8060572211299483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 330.6256632228578,\n        \"min\": 0.0,\n        \"max\": 891.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32.204207968574636,\n          14.4542,\n          891.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Survived'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NXZoEmEo6Z-j",
        "outputId": "e49d48f5-7277-4366-e830-28c847557823"
      },
      "id": "NXZoEmEo6Z-j",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived\n",
              "0    549\n",
              "1    342\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "a_list = list(range(1,20))\n",
        "a_list, np.median(a_list),np.percentile(a_list,25),np.percentile(a_list,75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfWuuvJx1LJZ",
        "outputId": "ec2c17b8-f7d7-4402-a19b-393655064087"
      },
      "id": "nfWuuvJx1LJZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
              " 10.0,\n",
              " 5.5,\n",
              " 14.5)"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "j6QYBE92Vd9T",
      "metadata": {
        "id": "j6QYBE92Vd9T"
      },
      "source": [
        "### 2.2 Get a Summary of the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0YwlbS3uWM9_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0YwlbS3uWM9_",
        "outputId": "ce3ebac2-b140-47c1-f89b-1a661f70bce4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Gender       891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2de9a110",
      "metadata": {
        "id": "2de9a110"
      },
      "source": [
        "### 2.3 Display the First Few Rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b85f6114",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "b85f6114",
        "outputId": "0eb4fc74-b95e-4b72-ecce-c57548bb7c72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Gender   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ],
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              "\n",
              "  <div id=\"df-b1c939e5-fbaa-472c-8d20-1907793e82a0\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <td>26.0</td>\n",
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              "      <td>7.9250</td>\n",
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              "      <th>3</th>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>53.1000</td>\n",
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              "    <tr>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>35.0</td>\n",
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              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b1c939e5-fbaa-472c-8d20-1907793e82a0')\"\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-b1c939e5-fbaa-472c-8d20-1907793e82a0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b1c939e5-fbaa-472c-8d20-1907793e82a0');\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",
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              "      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-865da58d-117a-4fe9-a926-fd11cee118e3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\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\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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": 15
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2eff7ca1",
      "metadata": {
        "id": "2eff7ca1"
      },
      "source": [
        "### 2.3 Display the Last Few Rows"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "edbd5141",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "edbd5141",
        "outputId": "8a57b2f6-cd67-4f34-a8f1-ccebe136d3dc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                      Name  \\\n",
              "886          887         0       2                     Montvila, Rev. Juozas   \n",
              "887          888         1       1              Graham, Miss. Margaret Edith   \n",
              "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
              "889          890         1       1                     Behr, Mr. Karl Howell   \n",
              "890          891         0       3                       Dooley, Mr. Patrick   \n",
              "\n",
              "     Gender   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
              "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
              "887  female  19.0      0      0      112053  30.00   B42        S  \n",
              "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
              "889    male  26.0      0      0      111369  30.00  C148        C  \n",
              "890    male  32.0      0      0      370376   7.75   NaN        Q  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-83d71033-a085-4721-a4d8-88531569887d\" 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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Gender</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
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              "      <td>211536</td>\n",
              "      <td>13.00</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.00</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.00</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        document.querySelector('#df-83d71033-a085-4721-a4d8-88531569887d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-83d71033-a085-4721-a4d8-88531569887d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-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",
              "    }\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",
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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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\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\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, Mr. Patrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.354126134736337,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19.0,\n          32.0\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\": 1,\n        \"num_unique_values\": 2,\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\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.4 Display data frame using iloc"
      ],
      "metadata": {
        "id": "lXlw776zzkcM"
      },
      "id": "lXlw776zzkcM"
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[1:5,0:2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "mnE-I2DQzjv3",
        "outputId": "983b4db6-deee-4009-b4ed-ecd2f31dec79"
      },
      "id": "mnE-I2DQzjv3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived\n",
              "1            2         1\n",
              "2            3         1\n",
              "3            4         1\n",
              "4            5         0"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d1a54264-d87c-4086-a524-d5fd6b04cbfe button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d1a54264-d87c-4086-a524-d5fd6b04cbfe');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-36f0338c-200a-4f6b-9edb-538e2b282586')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --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",
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              "      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",
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              "    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-36f0338c-200a-4f6b-9edb-538e2b282586 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e65e07b8",
      "metadata": {
        "id": "e65e07b8"
      },
      "source": [
        "## 3. Handling Missing Values\n",
        "### 3.1 Check for Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bd34fbf",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bd34fbf",
        "outputId": "06478ed0-4122-4a2a-8b1e-ddd83b8a3fd4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Gender           0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8a5467ef",
      "metadata": {
        "id": "8a5467ef"
      },
      "source": [
        "### 3.2 Imputing Missing Age Values with Median\n",
        "missing entries in the 'Age' column were filled using the median age of the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d67b8647",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d67b8647",
        "outputId": "0b00fedc-243d-4cba-adb1-17788d608928"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29.69911764705882\n",
            "29.36158249158249\n"
          ]
        }
      ],
      "source": [
        "print(df['Age'].mean())\n",
        "df['Age'].fillna(df['Age'].median(), inplace=True)\n",
        "print(df['Age'].mean())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e25098c8",
      "metadata": {
        "id": "e25098c8"
      },
      "source": [
        "### 3.3 To be, or not to be.\n",
        "Removing or Keeping Rows with Missing 'Embarked' Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0afb1675",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0afb1675",
        "outputId": "7b14a47a-d795-4c57-9ef4-8355b2c79e39"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                       Name  \\\n",
              "61            62         1       1                        Icard, Miss. Amelie   \n",
              "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
              "\n",
              "     Gender   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
              "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
              "829  female  62.0      0      0  113572  80.0   B28      NaN  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\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>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Gender</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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>62</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Icard, Miss. Amelie</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>B28</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>830</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
              "      <td>female</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113572</td>\n",
              "      <td>80.0</td>\n",
              "      <td>B28</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cdf71a00-b244-4bd4-8521-7ad2b1dd9da3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cdf71a00-b244-4bd4-8521-7ad2b1dd9da3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cdf71a00-b244-4bd4-8521-7ad2b1dd9da3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fd54f925-3bab-4a31-8fd3-e1efc4cc99d6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fd54f925-3bab-4a31-8fd3-e1efc4cc99d6')\"\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-fd54f925-3bab-4a31-8fd3-e1efc4cc99d6 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",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "df[df['Embarked'].isnull()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "724b08a3",
      "metadata": {
        "id": "724b08a3"
      },
      "outputs": [],
      "source": [
        "# Drop rows with missing 'Embarked' values\n",
        "df.dropna(subset=['Embarked'], inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb1c1cc4",
      "metadata": {
        "id": "eb1c1cc4"
      },
      "source": [
        "## 4. Data Filtering and Information Retrieval\n",
        "### 4.1 Filter Passengers Younger than 18 Years Old"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = {\n",
        "    'Name': ['Alice', 'Bob', 'Charlie'],\n",
        "    'Age': [25, 30, 35],\n",
        "    'City': ['New York', 'Los Angeles', 'Chicago']\n",
        "}\n",
        "\n",
        "\n",
        "df_test = pd.DataFrame(data)\n",
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "MwOrJxCohWl9",
        "outputId": "add44dd1-5859-42d1-c90c-9709acd34f7c"
      },
      "id": "MwOrJxCohWl9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Name  Age         City\n",
              "0    Alice   25     New York\n",
              "1      Bob   30  Los Angeles\n",
              "2  Charlie   35      Chicago"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d82840e-d6f4-42ca-9505-45e3dca0a3f8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
              "      <th>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alice</td>\n",
              "      <td>25</td>\n",
              "      <td>New York</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bob</td>\n",
              "      <td>30</td>\n",
              "      <td>Los Angeles</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Charlie</td>\n",
              "      <td>35</td>\n",
              "      <td>Chicago</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7d82840e-d6f4-42ca-9505-45e3dca0a3f8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7d82840e-d6f4-42ca-9505-45e3dca0a3f8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e1ec624f-72ee-40e7-8a9f-0b799741b370\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e1ec624f-72ee-40e7-8a9f-0b799741b370')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e1ec624f-72ee-40e7-8a9f-0b799741b370 button');\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_test');\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_test",
              "summary": "{\n  \"name\": \"df_test\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Alice\",\n          \"Bob\",\n          \"Charlie\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 25,\n        \"max\": 35,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          25,\n          30,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"New York\",\n          \"Los Angeles\",\n          \"Chicago\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
          "execution_count": 21
        }
      ]
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        "df_test[[False,False, False]]"
      ],
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        "id": "G_3N0DdQh-xr",
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      "id": "G_3N0DdQh-xr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Name, Age, City]\n",
              "Index: []"
            ],
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-949d64d4-0105-4e2d-8eba-1560f0f21326');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
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        }
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    {
      "cell_type": "code",
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      "id": "10527bca",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('children')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "children",
              "summary": "{\n  \"name\": \"children\",\n  \"rows\": 113,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 281,\n        \"min\": 8,\n        \"max\": 876,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          684,\n          17,\n          267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"Goodwin, Mr. Charles Edward\",\n          \"Rice, Master. Eugene\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.030407695354197,\n        \"min\": 0.42,\n        \"max\": 17.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          1.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          1\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\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 80,\n        \"samples\": [\n          \"SOTON/OQ 392089\",\n          \"349909\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.53809246982951,\n        \"min\": 7.0542,\n        \"max\": 211.3375,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          20.525,\n          21.075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"C70\",\n          \"E121\"\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": 23
        }
      ],
      "source": [
        "children = df[df['Age'] < 18]\n",
        "children"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "645873f3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "645873f3",
        "outputId": "f6e340fd-3c7b-4f48-9dab-8ae39cf6c787"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                                     Name  \\\n",
              "7              8         0       3           Palsson, Master. Gosta Leonard   \n",
              "9             10         1       2      Nasser, Mrs. Nicholas (Adele Achem)   \n",
              "10            11         1       3          Sandstrom, Miss. Marguerite Rut   \n",
              "14            15         0       3     Vestrom, Miss. Hulda Amanda Adolfina   \n",
              "16            17         0       3                     Rice, Master. Eugene   \n",
              "..           ...       ...     ...                                      ...   \n",
              "850          851         0       3  Andersson, Master. Sigvard Harald Elias   \n",
              "852          853         0       3                  Boulos, Miss. Nourelain   \n",
              "853          854         1       1                Lines, Miss. Mary Conover   \n",
              "869          870         1       3          Johnson, Master. Harold Theodor   \n",
              "875          876         1       3         Najib, Miss. Adele Kiamie \"Jane\"   \n",
              "\n",
              "     Gender   Age  SibSp  Parch    Ticket     Fare Cabin Embarked  \n",
              "7      male   2.0      3      1    349909  21.0750   NaN        S  \n",
              "9    female  14.0      1      0    237736  30.0708   NaN        C  \n",
              "10   female   4.0      1      1   PP 9549  16.7000    G6        S  \n",
              "14   female  14.0      0      0    350406   7.8542   NaN        S  \n",
              "16     male   2.0      4      1    382652  29.1250   NaN        Q  \n",
              "..      ...   ...    ...    ...       ...      ...   ...      ...  \n",
              "850    male   4.0      4      2    347082  31.2750   NaN        S  \n",
              "852  female   9.0      1      1      2678  15.2458   NaN        C  \n",
              "853  female  16.0      0      1  PC 17592  39.4000   D28        S  \n",
              "869    male   4.0      1      1    347742  11.1333   NaN        S  \n",
              "875  female  15.0      0      0      2667   7.2250   NaN        C  \n",
              "\n",
              "[113 rows x 12 columns]"
            ],
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              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>852</th>\n",
              "      <td>853</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Boulos, Miss. Nourelain</td>\n",
              "      <td>female</td>\n",
              "      <td>9.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>2678</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>853</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Lines, Miss. Mary Conover</td>\n",
              "      <td>female</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0</td>\n",
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              "      <th>869</th>\n",
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              "      <td>S</td>\n",
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              "      <th>875</th>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
              "      <td>female</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2667</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df['Age'] < 18]\",\n  \"rows\": 113,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 281,\n        \"min\": 8,\n        \"max\": 876,\n        \"num_unique_values\": 113,\n        \"samples\": [\n          684,\n          17,\n          267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 113,\n        \"samples\": [\n          \"Goodwin, Mr. Charles Edward\",\n          \"Rice, Master. Eugene\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.030407695354197,\n        \"min\": 0.42,\n        \"max\": 17.0,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          1.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          1\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\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 80,\n        \"samples\": [\n          \"SOTON/OQ 392089\",\n          \"349909\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.53809246982951,\n        \"min\": 7.0542,\n        \"max\": 211.3375,\n        \"num_unique_values\": 69,\n        \"samples\": [\n          20.525,\n          21.075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"C70\",\n          \"E121\"\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": 24
        }
      ],
      "source": [
        "df[df['Age'] < 18]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a2c4c19a",
      "metadata": {
        "id": "a2c4c19a"
      },
      "source": [
        "### 4.2 Find All Female Passengers Who Survived"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8d8b22ac",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8d8b22ac",
        "outputId": "1de2ebf1-31e5-497f-b19f-83da13e975a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "8              9         1       3   \n",
              "9             10         1       2   \n",
              "..           ...       ...     ...   \n",
              "874          875         1       2   \n",
              "875          876         1       3   \n",
              "879          880         1       1   \n",
              "880          881         1       2   \n",
              "887          888         1       1   \n",
              "\n",
              "                                                  Name  Gender   Age  SibSp  \\\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
              "9                  Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "874              Abelson, Mrs. Samuel (Hannah Wizosky)  female  28.0      1   \n",
              "875                   Najib, Miss. Adele Kiamie \"Jane\"  female  15.0      0   \n",
              "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
              "880       Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "8        2            347742  11.1333   NaN        S  \n",
              "9        0            237736  30.0708   NaN        C  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "874      0         P/PP 3381  24.0000   NaN        C  \n",
              "875      0              2667   7.2250   NaN        C  \n",
              "879      1             11767  83.1583   C50        C  \n",
              "880      1            230433  26.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "\n",
              "[231 rows x 12 columns]"
            ],
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <th>8</th>\n",
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              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>874</th>\n",
              "      <td>875</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Abelson, Mrs. Samuel (Hannah Wizosky)</td>\n",
              "      <td>female</td>\n",
              "      <td>28.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>P/PP 3381</td>\n",
              "      <td>24.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>875</th>\n",
              "      <td>876</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
              "      <td>female</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2667</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>879</th>\n",
              "      <td>880</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)</td>\n",
              "      <td>female</td>\n",
              "      <td>56.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>11767</td>\n",
              "      <td>83.1583</td>\n",
              "      <td>C50</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>880</th>\n",
              "      <td>881</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
              "      <td>female</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>230433</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>231 rows × 12 columns</p>\n",
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              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('female_survivors')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('female_survivors');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "female_survivors",
              "summary": "{\n  \"name\": \"female_survivors\",\n  \"rows\": 231,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 253,\n        \"min\": 2,\n        \"max\": 888,\n        \"num_unique_values\": 231,\n        \"samples\": [\n          854,\n          270,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\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          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 231,\n        \"samples\": [\n          \"Lines, Miss. Mary Conover\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.88795890567533,\n        \"min\": 0.75,\n        \"max\": 63.0,\n        \"num_unique_values\": 54,\n        \"samples\": [\n          23.0\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\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\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\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 187,\n        \"samples\": [\n          \"2667\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64.32669671522726,\n        \"min\": 7.225,\n        \"max\": 512.3292,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          24.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"B49\"\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          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "female_survivors = df[(df['Gender'] == 'female') & (df['Survived'] == 1)]\n",
        "female_survivors"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ad1f7c98",
      "metadata": {
        "id": "ad1f7c98"
      },
      "source": [
        "### 4.3 Filter Data by Passenger Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a0f9f517",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "a0f9f517",
        "outputId": "345c2ac6-25a2-47f0-e2ff-a0f5d651af08"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "1              2         1       1   \n",
              "3              4         1       1   \n",
              "6              7         0       1   \n",
              "11            12         1       1   \n",
              "23            24         1       1   \n",
              "..           ...       ...     ...   \n",
              "871          872         1       1   \n",
              "872          873         0       1   \n",
              "879          880         1       1   \n",
              "887          888         1       1   \n",
              "889          890         1       1   \n",
              "\n",
              "                                                  Name  Gender   Age  SibSp  \\\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "6                              McCarthy, Mr. Timothy J    male  54.0      0   \n",
              "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
              "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "871   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)  female  47.0      1   \n",
              "872                           Carlsson, Mr. Frans Olof    male  33.0      0   \n",
              "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "\n",
              "     Parch    Ticket     Fare        Cabin Embarked  \n",
              "1        0  PC 17599  71.2833          C85        C  \n",
              "3        0    113803  53.1000         C123        S  \n",
              "6        0     17463  51.8625          E46        S  \n",
              "11       0    113783  26.5500         C103        S  \n",
              "23       0    113788  35.5000           A6        S  \n",
              "..     ...       ...      ...          ...      ...  \n",
              "871      1     11751  52.5542          D35        S  \n",
              "872      0       695   5.0000  B51 B53 B55        S  \n",
              "879      1     11767  83.1583          C50        C  \n",
              "887      0    112053  30.0000          B42        S  \n",
              "889      0    111369  30.0000         C148        C  \n",
              "\n",
              "[214 rows x 12 columns]"
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              "      <td>47.0</td>\n",
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              "      <th>872</th>\n",
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              "      <th>887</th>\n",
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              "<p>214 rows × 12 columns</p>\n",
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              "type": "dataframe",
              "variable_name": "first_class_passengers",
              "summary": "{\n  \"name\": \"first_class_passengers\",\n  \"rows\": 214,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245,\n        \"min\": 2,\n        \"max\": 890,\n        \"num_unique_values\": 214,\n        \"samples\": [\n          36,\n          816,\n          310\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\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\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 214,\n        \"samples\": [\n          \"Holverson, Mr. Alexander Oskar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.142849672481463,\n        \"min\": 0.92,\n        \"max\": 80.0,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          38.0\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\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\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\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 146,\n        \"samples\": [\n          \"112059\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78.74645710762526,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          29.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 132,\n        \"samples\": [\n          \"T\"\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          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "first_class_passengers = df[df['Pclass'] == 1]\n",
        "first_class_passengers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ea9271c5",
      "metadata": {
        "id": "ea9271c5"
      },
      "source": [
        "### 4.4 Calculate Average Fare by Passenger Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "25d9b90e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25d9b90e",
        "outputId": "67b2de06-39b4-45f1-df36-e046b424decd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    84.193516\n",
              "2    20.662183\n",
              "3    13.675550\n",
              "Name: Fare, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "average_fare_by_class = df.groupby('Pclass')['Fare'].mean()\n",
        "average_fare_by_class"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "o-YsZSRtchBu",
      "metadata": {
        "id": "o-YsZSRtchBu"
      },
      "source": [
        "  ### 4.5 Calculate the Survival Rate by Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2KVKYr1lcgFI",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KVKYr1lcgFI",
        "outputId": "7820ab6c-2e65-48b6-e0a9-d881b49e1fa4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pclass\n",
              "1    0.626168\n",
              "2    0.472826\n",
              "3    0.242363\n",
              "Name: Survived, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "survival_rate_by_class = df.groupby('Pclass')['Survived'].mean()\n",
        "survival_rate_by_class"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4.6 Using loc"
      ],
      "metadata": {
        "id": "b1F9sUpAz_Gy"
      },
      "id": "b1F9sUpAz_Gy"
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df['Survived'] == 1, ['Name','Age']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "qFSTxo180kOL",
        "outputId": "5db0d6ad-b7c2-4e2f-b20c-11526a91c782"
      },
      "id": "qFSTxo180kOL",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  Name   Age\n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0\n",
              "2                               Heikkinen, Miss. Laina  26.0\n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0\n",
              "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  27.0\n",
              "9                  Nasser, Mrs. Nicholas (Adele Achem)  14.0\n",
              "..                                                 ...   ...\n",
              "875                   Najib, Miss. Adele Kiamie \"Jane\"  15.0\n",
              "879      Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  56.0\n",
              "880       Shelley, Mrs. William (Imanita Parrish Hall)  25.0\n",
              "887                       Graham, Miss. Margaret Edith  19.0\n",
              "889                              Behr, Mr. Karl Howell  26.0\n",
              "\n",
              "[342 rows x 2 columns]"
            ],
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              "        const element = document.querySelector('#df-d6db1ef7-d08b-46e5-9b46-519b7b092e16');\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 342,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 342,\n        \"samples\": [\n          \"Homer, Mr. Harry (\\\"Mr E Haven\\\")\",\n          \"Ryerson, Miss. Emily Borie\",\n          \"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.950951984140428,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          62.0,\n          51.0,\n          38.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a6f1783b",
      "metadata": {
        "id": "a6f1783b"
      },
      "source": [
        "## 5. Data Manipulation\n",
        "### 5.1 Add a New Column\n",
        "Let's add a new column to the dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3020717a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3020717a",
        "outputId": "67111969-c6cf-43ef-f650-157dc99bbe11"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Gender   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
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              "   Parch            Ticket     Fare Cabin Embarked  Family_Size  \n",
              "0      0         A/5 21171   7.2500   NaN        S            1  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 889,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 256,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 889,\n        \"samples\": [\n          282,\n          436,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 889,\n        \"samples\": [\n          \"Olsson, Mr. Nils Johan Goransson\",\n          \"Carter, Miss. Lucile Polk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.984932293690775,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\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\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 680,\n        \"samples\": [\n          \"11774\",\n          \"29105\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.697504316707956,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 146,\n        \"samples\": [\n          \"C125\",\n          \"C101\"\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      \"column\": \"Family_Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "df['Family_Size'] = df['SibSp'] + df['Parch']\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43d518d1",
      "metadata": {
        "id": "43d518d1"
      },
      "source": [
        "### 5.2 Rename Columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c6f1ca7f",
      "metadata": {
        "id": "c6f1ca7f"
      },
      "outputs": [],
      "source": [
        "# df = df.rename(columns={'Passenger_Class': 'Passenger_Class', 'Ticket_Fare': 'Ticket_Fare'})\n",
        "# df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5df92fa5",
      "metadata": {
        "id": "5df92fa5"
      },
      "source": [
        "### 5.3 Modify an Existing Column\n",
        "Suppose we want to adjust the Fare column to reflect today's values. \\$1 in 1912 is worth \\$32.39 today"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ed4c745a",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ed4c745a",
        "outputId": "0dedd9cb-727f-48a6-c9f6-a2486444be65"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Fare  todays_Fare\n",
              "0   7.2500   234.827500\n",
              "1  71.2833  2308.866087\n",
              "2   7.9250   256.690750\n",
              "3  53.1000  1719.909000\n",
              "4   8.0500   260.739500"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fare</th>\n",
              "      <th>todays_Fare</th>\n",
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              "      <th>0</th>\n",
              "      <td>7.2500</td>\n",
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              "      <th>1</th>\n",
              "      <td>71.2833</td>\n",
              "      <td>2308.866087</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.9250</td>\n",
              "      <td>256.690750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>53.1000</td>\n",
              "      <td>1719.909000</td>\n",
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              "      <th>4</th>\n",
              "      <td>8.0500</td>\n",
              "      <td>260.739500</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d708416a-f803-49a4-a37d-8ef926e8ebfe');\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",
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              "            title=\"Suggest charts\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --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",
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              "    fill: var(--button-hover-fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['Fare', 'todays_Fare']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.5100288352535,\n        \"min\": 7.25,\n        \"max\": 71.2833,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          71.2833,\n          8.05,\n          7.925\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"todays_Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 988.2198339738608,\n        \"min\": 234.82750000000001,\n        \"max\": 2308.866087,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2308.866087,\n          260.7395,\n          256.69075\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "df['todays_Fare'] = df['Fare'] * 32.39\n",
        "df[['Fare', 'todays_Fare']].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ddfa2a5d",
      "metadata": {
        "id": "ddfa2a5d"
      },
      "source": [
        "### 5.4 Drop a Column\n",
        "Let's drop the Cabin column from the DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0719dc75",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0719dc75",
        "outputId": "0e28e4cd-816d-4ba8-e025-54e41de7046d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name  Gender   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Embarked  Family_Size  todays_Fare  \n",
              "0      0         A/5 21171   7.2500        S            1   234.827500  \n",
              "1      0          PC 17599  71.2833        C            1  2308.866087  \n",
              "2      0  STON/O2. 3101282   7.9250        S            0   256.690750  \n",
              "3      0            113803  53.1000        S            1  1719.909000  \n",
              "4      0            373450   8.0500        S            0   260.739500  "
            ],
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      background-color: #434B5C;\n",
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              "        const element = document.querySelector('#df-c295ad02-0a5f-4ccc-ba30-4c107afac109');\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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              "      --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-573c54d4-3fd0-4d73-bc0b-eb1be025a9fb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 889,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 256,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 889,\n        \"samples\": [\n          282,\n          436,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\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          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 889,\n        \"samples\": [\n          \"Olsson, Mr. Nils Johan Goransson\",\n          \"Carter, Miss. Lucile Polk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.984932293690775,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SibSp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\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\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 680,\n        \"samples\": [\n          \"11774\",\n          \"29105\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.697504316707956,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          11.2417,\n          51.8625\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      \"column\": \"Family_Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          7,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"todays_Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1609.7021648181708,\n        \"min\": 0.0,\n        \"max\": 16594.342788,\n        \"num_unique_values\": 247,\n        \"samples\": [\n          364.118663,\n          1679.8263749999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "df = df.drop(columns=['Cabin'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "acb2ecc7",
      "metadata": {
        "id": "acb2ecc7"
      },
      "source": [
        "### 5.5 Sort the DataFrame\n",
        "Sort the DataFrame by Age in ascending order."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "39556b13",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "39556b13",
        "outputId": "c3f3bf29-770c-408e-cd0d-3f0b443dea51"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Name   Age\n",
              "803  Thomas, Master. Assad Alexander  0.42\n",
              "755        Hamalainen, Master. Viljo  0.67\n",
              "469    Baclini, Miss. Helene Barbara  0.75\n",
              "644           Baclini, Miss. Eugenie  0.75\n",
              "831  Richards, Master. George Sibley  0.83"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Age</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>Thomas, Master. Assad Alexander</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>Hamalainen, Master. Viljo</td>\n",
              "      <td>0.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>Baclini, Miss. Helene Barbara</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>644</th>\n",
              "      <td>Baclini, Miss. Eugenie</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>831</th>\n",
              "      <td>Richards, Master. George Sibley</td>\n",
              "      <td>0.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-967eac54-c28c-45fc-a0fb-54c6c7260488 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-967eac54-c28c-45fc-a0fb-54c6c7260488');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1e88ab1a-2704-4bbe-b4c7-c4a203928432\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1e88ab1a-2704-4bbe-b4c7-c4a203928432')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1e88ab1a-2704-4bbe-b4c7-c4a203928432 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_sorted_by_age[['Name', 'Age']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Hamalainen, Master. Viljo\",\n          \"Richards, Master. George Sibley\",\n          \"Baclini, Miss. Helene Barbara\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1580506248010428,\n        \"min\": 0.42,\n        \"max\": 0.83,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.67,\n          0.83,\n          0.42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "df_sorted_by_age = df.sort_values(by='Age')\n",
        "df_sorted_by_age[['Name', 'Age']].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "atDIsyKYjNsx",
      "metadata": {
        "id": "atDIsyKYjNsx"
      },
      "source": [
        "## 6. Convert Pandas to other data types"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "LEonrB9cjNzo",
      "metadata": {
        "id": "LEonrB9cjNzo"
      },
      "source": [
        "#### 6.1 to a NumPy array:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "K0hIauHNjOF0",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0hIauHNjOF0",
        "outputId": "4ed6928f-9081-4988-d80f-b8e28236fee6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 7.25  , 22.    ],\n",
              "       [71.2833, 38.    ],\n",
              "       [ 7.925 , 26.    ],\n",
              "       ...,\n",
              "       [23.45  , 28.    ],\n",
              "       [30.    , 26.    ],\n",
              "       [ 7.75  , 32.    ]])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "df[['Fare','Age']].values\n",
        "df[['Fare','Age']].to_numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WqYTl1k4p6_U",
      "metadata": {
        "id": "WqYTl1k4p6_U"
      },
      "source": [
        "#### 6.2 to a list:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "arLtYMKzqPOV",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arLtYMKzqPOV",
        "outputId": "9ee1fe54-e1a8-4ca9-d946-1a64b29aae4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 13.0,\n",
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              " 9.8458,\n",
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              " 7.8958,\n",
              " 83.1583,\n",
              " 26.0,\n",
              " 7.8958,\n",
              " 10.5167,\n",
              " 10.5,\n",
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              " 30.0,\n",
              " 23.45,\n",
              " 30.0,\n",
              " 7.75]"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "list(df['Fare'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "kEsQCS3qqVzF",
      "metadata": {
        "id": "kEsQCS3qqVzF"
      },
      "source": [
        "#### 6.3 to a set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cBqo6NMrprN4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cBqo6NMrprN4",
        "outputId": "efbea6eb-7146-4fa0-bf23-5706a50c760e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'female', 'male'}"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "set(df['Gender'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "yq2VxqVIqmVq",
      "metadata": {
        "id": "yq2VxqVIqmVq"
      },
      "source": [
        "#### 6.4 to a tuple:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "srOUT71mqmd_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srOUT71mqmd_",
        "outputId": "e32962a0-7edb-4a2a-daae-614a37e00769"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
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              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'female',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male',\n",
              " 'female',\n",
              " 'male',\n",
              " 'female',\n",
              " 'female',\n",
              " 'male',\n",
              " 'male')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "tuple(df['Gender'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8ac7ccd1",
      "metadata": {
        "id": "8ac7ccd1"
      },
      "source": [
        "## 7. Data Visualization\n",
        "### 7.1 Visualize the Distribution of Passengers' Ages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4b7ec333",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4b7ec333",
        "outputId": "847eb674-72f9-4b81-e51a-b3d18a6aa8c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(df['Age'], bins=10, edgecolor='black', alpha=0.7)\n",
        "plt.title('Age Distribution of Passengers')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e315f42f",
      "metadata": {
        "id": "e315f42f"
      },
      "source": [
        "### 7.2 Visualize the Survival Rate by Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c95c9849",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "c95c9849",
        "outputId": "de181c5d-958f-446e-db0a-cd1a0edb22e1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "survival_rate_by_class = df.groupby('Pclass')['Survived'].mean()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(survival_rate_by_class.index, survival_rate_by_class.values, color='skyblue')\n",
        "plt.title('Survival Rate by Class')\n",
        "plt.xlabel('Class')\n",
        "plt.ylabel('Survival Rate')\n",
        "plt.xticks(rotation=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Pn-DCvHYvXa5",
      "metadata": {
        "id": "Pn-DCvHYvXa5"
      },
      "source": [
        "## 7.3 Data Corelation\n",
        "#### A heatmap of the correlation coefficients between the columns 'Age', 'Fare', 'Survived', and 'Pclass'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "HyPiO2_mvXrR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "HyPiO2_mvXrR",
        "outputId": "00df19c3-7175-49c2-eac6-8f855b0fb671"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "correlation_matrix_sample = df[['Age', 'Fare', 'Survived','Pclass']].corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix_sample, annot=True, fmt=\".2f\", cmap='coolwarm', cbar_kws={'label': 'Correlation coefficient'})\n",
        "plt.title('Correlation Matrix for Sample Titanic Dataset')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "jKFk0qXwyYYR",
      "metadata": {
        "id": "jKFk0qXwyYYR"
      },
      "source": [
        "## 8. More pandas and ploting"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if you  have the `Math_Grades_Dataset.xlsx` file run the below cell."
      ],
      "metadata": {
        "id": "v4W3JINm6sGR"
      },
      "id": "v4W3JINm6sGR"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "G83_pS6aykaO",
      "metadata": {
        "id": "G83_pS6aykaO"
      },
      "outputs": [],
      "source": [
        "from google.colab import files\n",
        "# uploaded = files.upload()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "if you don't have the `Math_Grades_Dataset.xlsx` file just run the below cell."
      ],
      "metadata": {
        "id": "hUdVieDu67By"
      },
      "id": "hUdVieDu67By"
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate https://github.com/payamkelich/fun/raw/main/Math_Grades_Dataset.xlsx -O Math_Grades_Dataset.xlsx\n",
        "!ls"
      ],
      "metadata": {
        "id": "3Ql2OAuk5cmU",
        "outputId": "93d6fd3e-6ba9-488f-e5ad-52febd3679d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "3Ql2OAuk5cmU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-18 16:22:11--  https://github.com/payamkelich/fun/raw/main/Math_Grades_Dataset.xlsx\n",
            "Resolving github.com (github.com)... 140.82.116.3\n",
            "Connecting to github.com (github.com)|140.82.116.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/payamkelich/fun/main/Math_Grades_Dataset.xlsx [following]\n",
            "--2024-07-18 16:22:11--  https://raw.githubusercontent.com/payamkelich/fun/main/Math_Grades_Dataset.xlsx\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 11358 (11K) [application/octet-stream]\n",
            "Saving to: ‘Math_Grades_Dataset.xlsx’\n",
            "\n",
            "Math_Grades_Dataset 100%[===================>]  11.09K  --.-KB/s    in 0s      \n",
            "\n",
            "2024-07-18 16:22:11 (25.5 MB/s) - ‘Math_Grades_Dataset.xlsx’ saved [11358/11358]\n",
            "\n",
            "Math_Grades_Dataset.xlsx  sample_data  titanic.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PRFkvBubykdB",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "PRFkvBubykdB",
        "outputId": "2184a509-577a-4a19-ae38-9ac88f0231ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            January  February  March  April  May  June  July  August  \\\n",
              "Student ID                                                             \n",
              "S1                4         0      3      3    3     1     3       2   \n",
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              "S20               0         1      1      0    3     0     3       0   \n",
              "\n",
              "            September  October  November  December  \n",
              "Student ID                                          \n",
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              "S3                  1        1         1         0  \n",
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            ],
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              "    <tr>\n",
              "      <th>S20</th>\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' 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-9501d953-1c50-44e7-9a72-647a1d806926\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9501d953-1c50-44e7-9a72-647a1d806926')\"\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-9501d953-1c50-44e7-9a72-647a1d806926 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f8c6ff16-ef8d-4ca0-9452-3a004de37866\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f8c6ff16-ef8d-4ca0-9452-3a004de37866 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Student ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"S1\",\n          \"S18\",\n          \"S16\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"January\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"February\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"March\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"April\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"May\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"June\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"July\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          4,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"August\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4,\n          3,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"September\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"October\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"November\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"December\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "df = pd.read_excel('Math_Grades_Dataset.xlsx')\n",
        "df.set_index('Student ID', inplace=True)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bpLVb98H4BK0",
      "metadata": {
        "id": "bpLVb98H4BK0"
      },
      "source": [
        "#### 8.1 Line Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8YE8W8TRykfv",
      "metadata": {
        "id": "8YE8W8TRykfv"
      },
      "outputs": [],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(df.columns, df.loc['S5'], label='S5')\n",
        "plt.title('Grades Over Months for Student 5')\n",
        "plt.xlabel('Month')\n",
        "plt.xticks(rotation=45)\n",
        "plt.ylabel('Grade')\n",
        "plt.legend(title='Student ID', bbox_to_anchor=(1.05, 1), loc='upper left')\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "t4VCrGN94KFv",
      "metadata": {
        "id": "t4VCrGN94KFv"
      },
      "source": [
        "#### 8.2. Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "-nsn-54Rykid",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "-nsn-54Rykid",
        "outputId": "4c72f2a5-4658-412b-a514-cdfb5f6b28f7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(df, annot=True, fmt=\"d\", cmap='YlGnBu', cbar_kws={'label': 'Grade'})\n",
        "plt.title('Heatmap of Grades')\n",
        "plt.xlabel('Month')\n",
        "plt.ylabel('Student ID')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "PJZelkZv4PlR",
      "metadata": {
        "id": "PJZelkZv4PlR"
      },
      "source": [
        "#### 8.3. Boxplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5dsyEJSkyklC",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "5dsyEJSkyklC",
        "outputId": "289b7f61-a8db-42c0-e278-b5946e3400c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "box = plt.boxplot(df.loc['S5'], patch_artist=True, boxprops=dict(facecolor='lightblue', color='blue'),\n",
        "                  whiskerprops=dict(color='blue'), capprops=dict(color='blue'), medianprops=dict(color='red'))\n",
        "\n",
        "plt.title('Box Plot of Grades for Student S5')\n",
        "plt.xlabel('Student S5')\n",
        "plt.ylabel('Grade')\n",
        "plt.xticks([1], ['S5'])\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QmmDxeiNx8U8",
      "metadata": {
        "id": "QmmDxeiNx8U8"
      },
      "source": [
        "# Object Oriented Programming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "kwM1h8Sxvn1C",
      "metadata": {
        "id": "kwM1h8Sxvn1C"
      },
      "outputs": [],
      "source": [
        "class Car:\n",
        "    def __init__(self, color, mileage):\n",
        "        self.color = color\n",
        "        self.mileage = mileage\n",
        "\n",
        "    def describe(self):\n",
        "        return f\"A {self.color} car with {self.mileage} miles.\"\n",
        "\n",
        "    def drive(self, miles):\n",
        "        self.mileage += miles"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oaetOJnAprtc"
      },
      "id": "oaetOJnAprtc",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "Author Name"
      }
    ],
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.10"
    },
    "title": "Pandas Tutorial with Titanic Dataset"
  },
  "nbformat": 4,
  "nbformat_minor": 5
}