diff --git a/Day_5_Exercises.ipynb b/Day_5_Exercises.ipynb
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+++ b/Day_5_Exercises.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "02443541",
+      "metadata": {
+        "id": "02443541"
+      },
+      "source": [
+        "# Day 5 Exercises\n",
+        "## Pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "50a99209",
+      "metadata": {
+        "id": "50a99209"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fb86e13d",
+      "metadata": {
+        "id": "fb86e13d"
+      },
+      "source": [
+        "#### 1. Create a pandas DataFrame named `df` using the following table of names, ages, and scores.\n",
+        "\n",
+        "| Name  | Age | Score |\n",
+        "|-------|-----|-------|\n",
+        "| Anna  | 23  | 88    |\n",
+        "| Ben   | 35  | 92    |\n",
+        "| Cara  | 29  | 79    |\n",
+        "| David | 41  | 85    |"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "6bd235d5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 174
+        },
+        "id": "6bd235d5",
+        "outputId": "234f8739-345a-42b5-eb5a-9401123b34b7"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Name  Age  Score\n",
+              "0   Anna   23     88\n",
+              "1    Ben   35     92\n",
+              "2   Cara   29     79\n",
+              "3  David   41     85"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-03b56846-62be-4b16-8a8d-1e185de8b6b4\" 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>Name</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Anna</td>\n",
+              "      <td>23</td>\n",
+              "      <td>88</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ben</td>\n",
+              "      <td>35</td>\n",
+              "      <td>92</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Cara</td>\n",
+              "      <td>29</td>\n",
+              "      <td>79</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>David</td>\n",
+              "      <td>41</td>\n",
+              "      <td>85</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-03b56846-62be-4b16-8a8d-1e185de8b6b4')\"\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-03b56846-62be-4b16-8a8d-1e185de8b6b4 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-03b56846-62be-4b16-8a8d-1e185de8b6b4');\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=\"id_bceffbef-5452-4d41-afae-58f57cfef659\">\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_bceffbef-5452-4d41-afae-58f57cfef659 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\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Ben\",\n          \"David\",\n          \"Anna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 23,\n        \"max\": 41,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35,\n          41,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 79,\n        \"max\": 92,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          92,\n          85,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "df = pd.DataFrame({\n",
+        "    'Name': ['Anna', 'Ben', 'Cara', 'David'],\n",
+        "    'Age': [23, 35, 29, 41],\n",
+        "    'Score': [88, 92, 79, 85]\n",
+        "})\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f534cd41",
+      "metadata": {
+        "id": "f534cd41"
+      },
+      "source": [
+        "#### 2. Use pandas methods on `df` to find the dataset’s dimensions (number of rows and columns) and calculate the average score."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "aa67fd6c",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aa67fd6c",
+        "outputId": "1f16b7c2-9b04-4b48-d526-131b9f783e7e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(4, 3)\n",
+            "86.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(df.shape)\n",
+        "print(df['Score'].mean())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "961531b1",
+      "metadata": {
+        "id": "961531b1"
+      },
+      "source": [
+        "#### 3. Filter the DataFrame `df` to keep only rows where the score is greater than or equal to 85 **and** the age is greater than 30.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e01aca88",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 112
+        },
+        "id": "e01aca88",
+        "outputId": "3343d113-f5f2-458a-a83d-6b75616314dc"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Name  Age  Score\n",
+              "1    Ben   35     92\n",
+              "3  David   41     85"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-d0b54687-4b37-45fc-8352-89bb14ba2045\" 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>Name</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Score</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ben</td>\n",
+              "      <td>35</td>\n",
+              "      <td>92</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>David</td>\n",
+              "      <td>41</td>\n",
+              "      <td>85</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-d0b54687-4b37-45fc-8352-89bb14ba2045')\"\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-d0b54687-4b37-45fc-8352-89bb14ba2045 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-d0b54687-4b37-45fc-8352-89bb14ba2045');\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=\"id_818963af-c4a8-43af-b568-b1d65723e713\">\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('filtered_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_818963af-c4a8-43af-b568-b1d65723e713 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('filtered_df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "filtered_df",
+              "summary": "{\n  \"name\": \"filtered_df\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"David\",\n          \"Ben\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 35,\n        \"max\": 41,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          41,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 85,\n        \"max\": 92,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          85,\n          92\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ],
+      "source": [
+        "filtered_df = df[(df['Score'] >= 85) & (df['Age'] > 30)]\n",
+        "filtered_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "629eec9c",
+      "metadata": {
+        "id": "629eec9c"
+      },
+      "source": [
+        "#### 4. Create a new column `Score_decimal` by dividing the `Score` column by 100."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "f3c17ed7",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 174
+        },
+        "id": "f3c17ed7",
+        "outputId": "036894fc-280e-4df4-84e6-cd0c2661127e"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Name  Age  Score  Score_decimal\n",
+              "0   Anna   23     88           0.88\n",
+              "1    Ben   35     92           0.92\n",
+              "2   Cara   29     79           0.79\n",
+              "3  David   41     85           0.85"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-5a07f193-6439-4c63-8371-9bbd07ca913a\" 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>Name</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Score</th>\n",
+              "      <th>Score_decimal</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Anna</td>\n",
+              "      <td>23</td>\n",
+              "      <td>88</td>\n",
+              "      <td>0.88</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Ben</td>\n",
+              "      <td>35</td>\n",
+              "      <td>92</td>\n",
+              "      <td>0.92</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Cara</td>\n",
+              "      <td>29</td>\n",
+              "      <td>79</td>\n",
+              "      <td>0.79</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>David</td>\n",
+              "      <td>41</td>\n",
+              "      <td>85</td>\n",
+              "      <td>0.85</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-5a07f193-6439-4c63-8371-9bbd07ca913a')\"\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-5a07f193-6439-4c63-8371-9bbd07ca913a 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-5a07f193-6439-4c63-8371-9bbd07ca913a');\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=\"id_d1af1ce0-060d-498b-a4a2-86746701a1e8\">\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_d1af1ce0-060d-498b-a4a2-86746701a1e8 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\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Ben\",\n          \"David\",\n          \"Anna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 23,\n        \"max\": 41,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35,\n          41,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 79,\n        \"max\": 92,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          92,\n          85,\n          88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score_decimal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.054772255750516606,\n        \"min\": 0.79,\n        \"max\": 0.92,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.92,\n          0.85,\n          0.88\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "df['Score_decimal'] = df['Score'] / 100\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "395545f2",
+      "metadata": {
+        "id": "395545f2"
+      },
+      "source": [
+        "## Matplotlib"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1a5439b9",
+      "metadata": {
+        "id": "1a5439b9"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b4171d8c",
+      "metadata": {
+        "id": "b4171d8c"
+      },
+      "source": [
+        "#### 1. **Bar Plot.** Create a bar chart showing each person’s Name on the x-axis and their Score on the y-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "0b4bc824",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "0b4bc824",
+        "outputId": "dd69adb7-ea47-4d85-f742-99612f5dbadc"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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6VNnZ2YqIiJCrq6tyc3M1evRodevWTZKUkZEhSQoKCnK4XlBQkH3b3+Xk5CgnJ8d+OTs7+zpNDwAAnM2pZ2QWLlyoefPmaf78+dq2bZvmzJmj119/XXPmzCn0MceOHSs/Pz/7V2hoaBFODAAAihOnhswzzzyjoUOHqkuXLoqMjFSPHj00ePBgjR07VpIUHBwsScrMzHS4XmZmpn3b3yUkJCgrK8v+lZ6efn3vBAAAcBqnhsyZM2fk4uI4gqurq/Ly8iRJYWFhCg4OVnJysn17dna2Nm3apOjo6Ese093dXb6+vg5fAADg5uTU18i0a9dOo0ePVvny5VWjRg199913mjhxoh555BFJks1m06BBgzRq1CiFh4crLCxMw4YNU7ly5RQXF+fM0QEYquLQz5w9wi1r/7g2zh4BNyGnhsyUKVM0bNgwPfHEEzp8+LDKlSunRx99VC+99JJ9n2effVanT59W//79deLECTVu3FgrVqyQh4eHEycHAADFgVNDxsfHR5MmTdKkSZMuu4/NZtPIkSM1cuTIGzcYAAAwAp+1BAAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjOT1kfvvtN3Xv3l0BAQHy9PRUZGSkvv32W/t2y7L00ksvKSQkRJ6enoqJidGePXucODEAACgunBoyx48fV6NGjVSiRAktX75cu3fv1oQJE1SmTBn7Pq+++qomT56s6dOna9OmTfLy8lJsbKzOnj3rxMkBAEBx4ObMGx8/frxCQ0M1e/Zs+1pYWJj9e8uyNGnSJL344otq3769JOn9999XUFCQkpKS1KVLlxs+MwAAKD6cekZmyZIlqlevnh566CEFBgbqzjvvVGJion37vn37lJGRoZiYGPuan5+f6tevr40bN17ymDk5OcrOznb4AgAANyenhszPP/+sadOmKTw8XCtXrtTjjz+ugQMHas6cOZKkjIwMSVJQUJDD9YKCguzb/m7s2LHy8/Ozf4WGhl7fOwEAAJzGqSGTl5enunXrasyYMbrzzjvVv39/9evXT9OnTy/0MRMSEpSVlWX/Sk9PL8KJAQBAceLUkAkJCVH16tUd1qpVq6YDBw5IkoKDgyVJmZmZDvtkZmbat/2du7u7fH19Hb4AAMDNyakh06hRI6Wmpjqs/fTTT6pQoYKkP1/4GxwcrOTkZPv27Oxsbdq0SdHR0Td0VgAAUPw49V1LgwcPVsOGDTVmzBh16tRJmzdv1jvvvKN33nlHkmSz2TRo0CCNGjVK4eHhCgsL07Bhw1SuXDnFxcU5c3QAAFAMODVk7rrrLi1evFgJCQkaOXKkwsLCNGnSJHXr1s2+z7PPPqvTp0+rf//+OnHihBo3bqwVK1bIw8PDiZMDAIDiwKkhI0lt27ZV27ZtL7vdZrNp5MiRGjly5A2cCgAAmMDpH1EAAABQWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFjXFDLnzp1TamqqLly4UFTzAAAAXLFChcyZM2fUt29flSpVSjVq1NCBAwckSQMGDNC4ceOKdEAAAIDLKVTIJCQkaMeOHVqzZo08PDzs6zExMVqwYEGRDQcAAFAQt8JcKSkpSQsWLFCDBg1ks9ns6zVq1FBaWlqRDQcAAFCQQp2ROXLkiAIDA/Otnz592iFsAAAArqdChUy9evX02Wef2S9fjJd3331X0dHRRTMZAADAPyjUU0tjxoxRq1attHv3bl24cEFvvvmmdu/erQ0bNmjt2rVFPSMAAMAlFeqMTOPGjbVjxw5duHBBkZGR+t///qfAwEBt3LhRUVFRRT0jAADAJV31GZnz58/r0Ucf1bBhw5SYmHg9ZgIAALgiV31GpkSJElq0aNH1mAUAAOCqFOqppbi4OCUlJRXxKAAAAFenUC/2DQ8P18iRI7V+/XpFRUXJy8vLYfvAgQOLZDgAAICCFCpkZs6cqdKlS2vr1q3aunWrwzabzUbIAACAG6JQIbNv376ingMAAOCqXdOnX0uSZVmyLKsoZgEAALgqhQ6Z999/X5GRkfL09JSnp6dq1aqluXPnFuVsAAAABSrUU0sTJ07UsGHD9OSTT6pRo0aSpHXr1umxxx7T0aNHNXjw4CIdEgAA4FIKFTJTpkzRtGnT1LNnT/vav//9b9WoUUMvv/wyIQMAAG6IQj21dOjQITVs2DDfesOGDXXo0KFrHgoAAOBKFCpkqlSpooULF+ZbX7BggcLDw695KAAAgCtRqKeWRowYoc6dO+urr76yv0Zm/fr1Sk5OvmTgAAAAXA+FOiPTsWNHbdq0SWXLllVSUpKSkpJUtmxZbd68WQ888EBRzwgAAHBJhTojI0lRUVH64IMPinIWAACAq1KoMzKff/65Vq5cmW995cqVWr58+TUPBQAAcCUKFTJDhw5Vbm5uvnXLsjR06NBrHgoAAOBKFCpk9uzZo+rVq+dbj4iI0N69e695KAAAgCtRqJDx8/PTzz//nG9979698vLyuuahAAAArkShQqZ9+/YaNGiQ0tLS7Gt79+7Vf//7X/373/8usuEAAAAKUqiQefXVV+Xl5aWIiAiFhYUpLCxMERERCggI0Ouvv17UMwIAAFxSod5+7efnpw0bNuiLL77Qjh075Onpqdq1a+uee+4p6vkAAAAu66rOyGzcuFHLli2TJNlsNrVs2VKBgYF6/fXX1bFjR/Xv3185OTnXZVAAAIC/u6qQGTlypHbt2mW/vHPnTvXr108tWrTQ0KFDtXTpUo0dO7bIhwQAALiUqwqZ7du367777rNf/uijj3T33XcrMTFRQ4YM0eTJk/msJQAAcMNcVcgcP35cQUFB9str165Vq1at7JfvuusupaenF910AAAABbiqkAkKCtK+ffskSefOndO2bdvUoEED+/aTJ0+qRIkSRTshAADAZVxVyLRu3VpDhw7V119/rYSEBJUqVcrhnUrff/+9KleuXORDAgAAXMpVvf36lVdeUYcOHXTvvffK29tbc+bMUcmSJe3bZ82apZYtWxb5kAAAAJdyVSFTtmxZffXVV8rKypK3t7dcXV0dtn/88cfy9vYu0gEBAAAup9B/EO9S/P39r2kYAACAq1GojygAAAAoDggZAABgLEIGAAAYi5ABAADGImQAAICxik3IjBs3TjabTYMGDbKvnT17VvHx8QoICJC3t7c6duyozMxM5w0JAACKlWIRMlu2bNGMGTNUq1Yth/XBgwdr6dKl+vjjj7V27VodPHhQHTp0cNKUAACguHF6yJw6dUrdunVTYmKiypQpY1/PysrSzJkzNXHiRDVv3lxRUVGaPXu2NmzYoG+++caJEwMAgOLC6SETHx+vNm3aKCYmxmF969atOn/+vMN6RESEypcvr40bN97oMQEAQDFUqL/sW1Q++ugjbdu2TVu2bMm3LSMjQyVLllTp0qUd1oOCgpSRkXHZY+bk5CgnJ8d+OTs7u8jmBQAAxYvTzsikp6frqaee0rx58+Th4VFkxx07dqz8/PzsX6GhoUV2bAAAULw4LWS2bt2qw4cPq27dunJzc5Obm5vWrl2ryZMny83NTUFBQTp37pxOnDjhcL3MzEwFBwdf9rgJCQnKysqyf6Wnp1/newIAAJzFaU8t3Xfffdq5c6fDWp8+fRQREaHnnntOoaGhKlGihJKTk9WxY0dJUmpqqg4cOKDo6OjLHtfd3V3u7u7XdXYAAFA8OC1kfHx8VLNmTYc1Ly8vBQQE2Nf79u2rIUOGyN/fX76+vhowYICio6PVoEEDZ4wMAACKGae+2PefvPHGG3JxcVHHjh2Vk5Oj2NhYvf32284eCwAAFBPFKmTWrFnjcNnDw0NTp07V1KlTnTMQAAAo1pz+d2QAAAAKi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMZyasiMHTtWd911l3x8fBQYGKi4uDilpqY67HP27FnFx8crICBA3t7e6tixozIzM500MQAAKE6cGjJr165VfHy8vvnmG33xxRc6f/68WrZsqdOnT9v3GTx4sJYuXaqPP/5Ya9eu1cGDB9WhQwcnTg0AAIoLN2fe+IoVKxwuv/feewoMDNTWrVvVpEkTZWVlaebMmZo/f76aN28uSZo9e7aqVaumb775Rg0aNHDG2AAAoJgoVq+RycrKkiT5+/tLkrZu3arz588rJibGvk9ERITKly+vjRs3XvIYOTk5ys7OdvgCAAA3p2ITMnl5eRo0aJAaNWqkmjVrSpIyMjJUsmRJlS5d2mHfoKAgZWRkXPI4Y8eOlZ+fn/0rNDT0eo8OAACcpNiETHx8vH744Qd99NFH13SchIQEZWVl2b/S09OLaEIAAFDcOPU1Mhc9+eSTWrZsmb766iv961//sq8HBwfr3LlzOnHihMNZmczMTAUHB1/yWO7u7nJ3d7/eIwMAgGLAqWdkLMvSk08+qcWLF2vVqlUKCwtz2B4VFaUSJUooOTnZvpaamqoDBw4oOjr6Ro8LAACKGaeekYmPj9f8+fP16aefysfHx/66Fz8/P3l6esrPz099+/bVkCFD5O/vL19fXw0YMEDR0dG8YwkAADg3ZKZNmyZJatq0qcP67Nmz1bt3b0nSG2+8IRcXF3Xs2FE5OTmKjY3V22+/fYMnBQAAxZFTQ8ayrH/cx8PDQ1OnTtXUqVNvwEQAAMAkxeZdSwAAAFeLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGMuIkJk6daoqVqwoDw8P1a9fX5s3b3b2SAAAoBgo9iGzYMECDRkyRMOHD9e2bdtUu3ZtxcbG6vDhw84eDQAAOFmxD5mJEyeqX79+6tOnj6pXr67p06erVKlSmjVrlrNHAwAATlasQ+bcuXPaunWrYmJi7GsuLi6KiYnRxo0bnTgZAAAoDtycPUBBjh49qtzcXAUFBTmsBwUF6ccff7zkdXJycpSTk2O/nJWVJUnKzs4u8vnycs4U+TFxZa7H4/lXPLbOw2N787qejy2Pq/Ncr8f14nEtyypwv2IdMoUxduxYjRgxIt96aGioE6bB9eI3ydkT4Hrhsb158djenK7343ry5En5+flddnuxDpmyZcvK1dVVmZmZDuuZmZkKDg6+5HUSEhI0ZMgQ++W8vDz9/vvvCggIkM1mu67zmiQ7O1uhoaFKT0+Xr6+vs8dBEeKxvTnxuN68eGwvzbIsnTx5UuXKlStwv2IdMiVLllRUVJSSk5MVFxcn6c8wSU5O1pNPPnnJ67i7u8vd3d1hrXTp0td5UnP5+vryi3OT4rG9OfG43rx4bPMr6EzMRcU6ZCRpyJAh6tWrl+rVq6e7775bkyZN0unTp9WnTx9njwYAAJys2IdM586ddeTIEb300kvKyMhQnTp1tGLFinwvAAYAALeeYh8ykvTkk09e9qkkFI67u7uGDx+e72k4mI/H9ubE43rz4rG9Njbrn97XBAAAUEwV6z+IBwAAUBBCBgAAGIuQAQAAxiJkAAAoxvbv3y+bzabt27dfdp81a9bIZrPpxIkTN2yu4oKQMdzGjRvl6uqqNm3aOHsUXEe9e/eWzWazfwUEBOj+++/X999/7+zRUEgZGRkaMGCAKlWqJHd3d4WGhqpdu3ZKTk529mi4Qn/9vSxRooSCgoLUokULzZo1S3l5eUV2O6GhoTp06JBq1qxZZMe8mRAyhps5c6YGDBigr776SgcPHnT2OLiO7r//fh06dEiHDh1ScnKy3Nzc1LZtW2ePhULYv3+/oqKitGrVKr322mvauXOnVqxYoWbNmik+Pr5Qx8zNzS3S/3jiylz8vdy/f7+WL1+uZs2a6amnnlLbtm114cKFIrkNV1dXBQcHy83NiL+YcsMRMgY7deqUFixYoMcff1xt2rTRe++9Z9928TRjcnKy6tWrp1KlSqlhw4ZKTU217/Pyyy+rTp06mjt3ripWrCg/Pz916dJFJ0+etO+zYsUKNW7cWKVLl1ZAQIDatm2rtLS0G3k38f9xd3dXcHCwgoODVadOHQ0dOlTp6ek6cuSIJCk9PV2dOnVS6dKl5e/vr/bt22v//v326/fu3VtxcXF6/fXXFRISooCAAMXHx+v8+fNOuke3rieeeEI2m02bN29Wx44dVbVqVdWoUUNDhgzRN998I0maOHGiIiMj5eXlpdDQUD3xxBM6deqU/RjvvfeeSpcurSVLlqh69epyd3fXgQMHtGXLFrVo0UJly5aVn5+f7r33Xm3bts1Zd/Wmd/H38vbbb1fdunX1/PPP69NPP9Xy5cvt/yYX9FhmZ2fL09NTy5cvdzju4sWL5ePjozNnzlzyqaXPP/9cVatWlaenp5o1a+bwu36rIWQMtnDhQkVEROiOO+5Q9+7dNWvWrHwfd/7CCy9owoQJ+vbbb+Xm5qZHHnnEYXtaWpqSkpK0bNkyLVu2TGvXrtW4cePs20+fPq0hQ4bo22+/VXJyslxcXPTAAw/wf35OdurUKX3wwQeqUqWKAgICdP78ecXGxsrHx0dff/211q9fL29vb91///06d+6c/XqrV69WWlqaVq9erTlz5ui9995zCGBcf7///rtWrFih+Ph4eXl55dt+8bPhXFxcNHnyZO3atUtz5szRqlWr9Oyzzzrse+bMGY0fP17vvvuudu3apcDAQJ08eVK9evXSunXr9M033yg8PFytW7d2+B8UXF/NmzdX7dq19cknn0gq+LH09fVV27ZtNX/+fIdjzJs3T3FxcSpVqlS+46enp6tDhw5q166dtm/frv/85z8aOnTo9b9jxZUFYzVs2NCaNGmSZVmWdf78eats2bLW6tWrLcuyrNWrV1uSrC+//NK+/2effWZJsv744w/Lsixr+PDhVqlSpazs7Gz7Ps8884xVv379y97mkSNHLEnWzp07r8M9wuX06tXLcnV1tby8vCwvLy9LkhUSEmJt3brVsizLmjt3rnXHHXdYeXl59uvk5ORYnp6e1sqVK+3HqFChgnXhwgX7Pg899JDVuXPnG3tnbnGbNm2yJFmffPLJVV3v448/tgICAuyXZ8+ebUmytm/fXuD1cnNzLR8fH2vp0qWFmheX16tXL6t9+/aX3Na5c2erWrVql9z298dy8eLFlre3t3X69GnLsiwrKyvL8vDwsJYvX25ZlmXt27fPkmR99913lmVZVkJCglW9enWHYz733HOWJOv48ePXdqcMxBkZQ6Wmpmrz5s3q2rWrJMnNzU2dO3fWzJkzHfarVauW/fuQkBBJ0uHDh+1rFStWlI+Pj8M+f92+Z88ede3aVZUqVZKvr68qVqwoSTpw4ECR3ycUrFmzZtq+fbu2b9+uzZs3KzY2Vq1atdIvv/yiHTt2aO/evfLx8ZG3t7e8vb3l7++vs2fPOjwVWKNGDbm6utov//3xxvVnXeEfU//yyy9133336fbbb5ePj4969OihY8eO6cyZM/Z9SpYs6fA7LkmZmZnq16+fwsPD5efnJ19fX506dYrf2RvMsizZbDZJ//xYtm7dWiVKlNCSJUskSYsWLZKvr69iYmIueeyUlBTVr1/fYS06Ovo63pvijVcOGWrmzJm6cOGCypUrZ1+zLEvu7u5666237GslSpSwf3/xl+qvTwv9dfvFff66vV27dqpQoYISExNVrlw55eXlqWbNmg5PV+DG8PLyUpUqVeyX3333Xfn5+SkxMVGnTp1SVFSU5s2bl+96t912m/37f3q8cf2Fh4fLZrPpxx9/vOw++/fvV9u2bfX4449r9OjR8vf317p169S3b1+dO3fO/nSDp6en/ff6ol69eunYsWN68803VaFCBbm7uys6Oprf2RssJSVFYWFhV/RYlixZUg8++KDmz5+vLl26aP78+ercuTMv7r1C/JQMdOHCBb3//vuaMGGCWrZs6bAtLi5OH374oSIiIq75do4dO6bU1FQlJibqnnvukSStW7fumo+LomGz2eTi4qI//vhDdevW1YIFCxQYGChfX19nj4YC+Pv7KzY2VlOnTtXAgQPzvU7mxIkT2rp1q/Ly8jRhwgS5uPx54nzhwoVXdPz169fr7bffVuvWrSX9+XqKo0ePFu2dQIFWrVqlnTt3avDgwVf8WHbr1k0tWrTQrl27tGrVKo0aNeqyx69WrZr97M1FF18kfiviqSUDLVu2TMePH1ffvn1Vs2ZNh6+OHTvme3qpsMqUKaOAgAC988472rt3r1atWqUhQ4YUybFx9XJycpSRkaGMjAylpKRowIABOnXqlNq1a6du3bqpbNmyat++vb7++mvt27dPa9as0cCBA/Xrr786e3T8zdSpU5Wbm6u7775bixYt0p49e5SSkqLJkycrOjpaVapU0fnz5zVlyhT9/PPPmjt3rqZPn35Fxw4PD9fcuXOVkpKiTZs2qVu3bvL09LzO9+jWdfH38rffftO2bds0ZswYtW/fXm3btlXPnj2v+LFs0qSJgoOD1a1bN4WFheV76uivHnvsMe3Zs0fPPPOMUlNTNX/+/Fv6RfuEjIFmzpypmJgY+fn55dvWsWNHffvtt0Xyh9JcXFz00UcfaevWrapZs6YGDx6s11577ZqPi8JZsWKFQkJCFBISovr162vLli36+OOP1bRpU5UqVUpfffWVypcvrw4dOqhatWrq27evzp49yxmaYqhSpUratm2bmjVrpv/+97+qWbOmWrRooeTkZE2bNk21a9fWxIkTNX78eNWsWVPz5s3T2LFjr+jYM2fO1PHjx1W3bl316NFDAwcOVGBg4HW+R7eui7+XFStW1P3336/Vq1dr8uTJ+vTTT+Xq6nrFj6XNZlPXrl21Y8cOdevWrcDbLF++vBYtWqSkpCTVrl1b06dP15gxY67XXSz2bNaVvvIMAACgmOGMDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAyAG653796y2WwaN26cw3pSUlK+D0EEgIIQMgCcwsPDQ+PHj9fx48edPQoAgxEyAJwiJiZGwcHBl/0MoWPHjqlr1666/fbbVapUKUVGRurDDz902Kdp06YaMGCABg0apDJlyigoKEiJiYk6ffq0+vTpIx8fH1WpUkXLly93uN4PP/ygVq1aydvbW0FBQerRowefEA0YipAB4BSurq4aM2aMpkyZcslP6D579qyioqL02Wef6YcfflD//v3Vo0cPbd682WG/OXPmqGzZstq8ebMGDBigxx9/XA899JAaNmyobdu2qWXLlurRo4fOnDkjSTpx4oSaN2+uO++8U99++61WrFihzMxMderU6YbcbwBFiw+NBHDD9e7dWydOnFBSUpKio6NVvXp1zZw5U0lJSXrggQd0uX+W2rZtq4iICL3++uuS/jwjk5ubq6+//lqSlJubKz8/P3Xo0EHvv/++JCkjI0MhISHauHGjGjRooFGjRunrr7/WypUr7cf99ddfFRoaqtTUVFWtWvU633sARcnN2QMAuLWNHz9ezZs319NPP+2wnpubqzFjxmjhwoX67bffdO7cOeXk5KhUqVIO+9WqVcv+vaurqwICAhQZGWlfCwoKkiQdPnxYkrRjxw6tXr1a3t7e+WZJS0sjZADDEDIAnKpJkyaKjY1VQkKCevfubV9/7bXX9Oabb2rSpEmKjIyUl5eXBg0apHPnzjlcv0SJEg6XbTabw9rFd0Hl5eVJkk6dOqV27dpp/Pjx+WYJCQkpqrsF4AYhZAA43bhx41SnTh3dcccd9rX169erffv26t69u6Q/Q+Snn35S9erVr+m26tatq0WLFqlixYpyc+OfQMB0vNgXgNNFRkaqW7dumjx5sn0tPDxcX3zxhTZs2KCUlBQ9+uijyszMvObbio+P1++//66uXbtqy5YtSktL08qVK9WnTx/l5uZe8/EB3FiEDIBiYeTIkfanfyTpxRdfVN26dRUbG6umTZsqODhYcXFx13w75cqV0/r165Wbm6uWLVsqMjJSgwYNUunSpeXiwj+JgGl41xIAADAW//sBAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAw1v8DdVQNvNxGd+UAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "plt.bar(df['Name'], df['Score'])\n",
+        "plt.xlabel('Name')\n",
+        "plt.ylabel('Score')\n",
+        "plt.title('Name vs Score')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "d492c785",
+      "metadata": {
+        "id": "d492c785"
+      },
+      "source": [
+        "#### 2. **Scatter Plot.** Create a scatter plot for df showing the relationship between `Age ` and `Score`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "4fcedfcd",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "4fcedfcd",
+        "outputId": "7ff7a53b-03d6-48c5-c4f3-668e40f2ea1c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.scatter(df['Age'], df['Score'])\n",
+        "plt.xlabel('Age')\n",
+        "plt.ylabel('Score')\n",
+        "plt.title('Age vs Score')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eeaccb13",
+      "metadata": {
+        "id": "eeaccb13"
+      },
+      "source": [
+        "#### 3. **Histogram**. Create a histogram to show the distribution of the `Score` column."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "6f01be80",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "6f01be80",
+        "outputId": "23c8c9e1-52e4-4704-f830-4463538ac870"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.hist(df['Score'])\n",
+        "plt.xlabel('Score')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.title('Score Distribution')\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "python-env",
+      "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": "3.11.13"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file