Homework 4 Management Information Systems (MIDS 3600).ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MahammadHasanli/MahammadHasanli/blob/main/Homework%204%20Management%20Information%20Systems%20(MIDS%203600).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WDpj6r0qbBu",
        "outputId": "6e152b16-01a1-4e77-a049-f8c424e6a470"
      },
      "source": [
        "!pip install pandasql\n",
        "import pandas as pd\n",
        "import pandasql as psql\n",
        "Course=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Course.xlsx?raw=true\")\n",
        "Faculty=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Faculty.xlsx?raw=true\")\n",
        "Student=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Student.xlsx?raw=true\")\n",
        "Enrollment=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Enrollment.xlsx?raw=true\")\n",
        "Offering=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Offering.xlsx?raw=true\")\n",
        "Faculty_1=pd.read_excel(\"https://github.com/ogut77/University/blob/main/Faculty_1.xlsx?raw=true\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandasql in /usr/local/lib/python3.7/dist-packages (0.7.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.21.6)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.7/dist-packages (from pandasql) (1.4.41)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandasql) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->pandasql) (2022.2.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->pandasql) (1.1.3)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from sqlalchemy->pandasql) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->sqlalchemy->pandasql) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->sqlalchemy->pandasql) (3.8.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hpVErDtq2_T"
      },
      "source": [
        "![image.png](data:image/png;base64,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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zdwdnqKOqxqo",
        "outputId": "8c9cb9c7-2548-48fa-c6f4-8db6b6196a82"
      },
      "source": [
        "#Question 1-List all student related inofrmation if second letter of  student's last name  is O.\n",
        "#Hint -you may need to use but % and _\n",
        "txt='''\n",
        "SELECT *\n",
        "FROM Student\n",
        "WHERE StdLastName LIKE'_O%'\n",
        "'''\n",
        "Q1 =  psql.sqldf(txt)\n",
        "Q1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         StdNo StdFirstName StdLastName  StdCity StdState      StdZip  \\\n",
              "0  124-56-7890          BOB     NORBERT  BOTHELL       WA  98011-2121   \n",
              "1  567-89-0123       MARIAH       DODGE  SEATTLE       WA  98114-0021   \n",
              "2  678-90-1234         TESS       DODGE  REDMOND       WA  98116-2344   \n",
              "3  789-01-2345      ROBERTO     MORALES  SEATTLE       WA  98121-2212   \n",
              "4  876-54-3210   CRISTOPHER       COLAN  SEATTLE       WA  98114-1332   \n",
              "\n",
              "  StdMajor StdClass  StdGPA  \n",
              "0      FIN       JR     2.7  \n",
              "1       IS       JR     3.6  \n",
              "2     ACCT       SO     3.3  \n",
              "3      FIN       JR     2.5  \n",
              "4       IS       SR     4.0  "
            ],
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              "      <th>StdNo</th>\n",
              "      <th>StdFirstName</th>\n",
              "      <th>StdLastName</th>\n",
              "      <th>StdCity</th>\n",
              "      <th>StdState</th>\n",
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              "      <td>124-56-7890</td>\n",
              "      <td>BOB</td>\n",
              "      <td>NORBERT</td>\n",
              "      <td>BOTHELL</td>\n",
              "      <td>WA</td>\n",
              "      <td>98011-2121</td>\n",
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              "      <td>567-89-0123</td>\n",
              "      <td>MARIAH</td>\n",
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              "      <td>SEATTLE</td>\n",
              "      <td>WA</td>\n",
              "      <td>98114-0021</td>\n",
              "      <td>IS</td>\n",
              "      <td>JR</td>\n",
              "      <td>3.6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>678-90-1234</td>\n",
              "      <td>TESS</td>\n",
              "      <td>DODGE</td>\n",
              "      <td>REDMOND</td>\n",
              "      <td>WA</td>\n",
              "      <td>98116-2344</td>\n",
              "      <td>ACCT</td>\n",
              "      <td>SO</td>\n",
              "      <td>3.3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>789-01-2345</td>\n",
              "      <td>ROBERTO</td>\n",
              "      <td>MORALES</td>\n",
              "      <td>SEATTLE</td>\n",
              "      <td>WA</td>\n",
              "      <td>98121-2212</td>\n",
              "      <td>FIN</td>\n",
              "      <td>JR</td>\n",
              "      <td>2.5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>876-54-3210</td>\n",
              "      <td>CRISTOPHER</td>\n",
              "      <td>COLAN</td>\n",
              "      <td>SEATTLE</td>\n",
              "      <td>WA</td>\n",
              "      <td>98114-1332</td>\n",
              "      <td>IS</td>\n",
              "      <td>SR</td>\n",
              "      <td>4.0</td>\n",
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              "      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",
              "    [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-4887636b-608e-4e3a-bee2-077cf7fd04d0 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-4887636b-608e-4e3a-bee2-077cf7fd04d0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "07PGCvAdsnXG",
        "outputId": "3ed6b64e-e645-49b9-e825-ec2c53e292db"
      },
      "source": [
        "#Question 2-List City names where Student or Faculty live. Name the column AS City.\n",
        "txt='''\n",
        "SELECT StdCity AS City\n",
        "FROM Student\n",
        "UNION\n",
        "SELECT FacCity AS City\n",
        "FROM Faculty\n",
        "'''\n",
        "Q2 =  psql.sqldf(txt)\n",
        "Q2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       City\n",
              "0  BELLEVUE\n",
              "1   BOTHELL\n",
              "2   REDMOND\n",
              "3   SEATTLE\n",
              "4    TACOMA"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41b624c2-226d-47fe-b54f-a23a8a2d9e9a\">\n",
              "    <div 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>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BELLEVUE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BOTHELL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>REDMOND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SEATTLE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TACOMA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41b624c2-226d-47fe-b54f-a23a8a2d9e9a')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #174EA6;\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-41b624c2-226d-47fe-b54f-a23a8a2d9e9a 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-41b624c2-226d-47fe-b54f-a23a8a2d9e9a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "EsK6PLgLtVw8",
        "outputId": "ee443d53-865a-4cfc-9396-44b1d3a9101c"
      },
      "source": [
        "#Question 3-List City names where both  Student and Faculty live in .\n",
        "txt='''\n",
        "SELECT StdCity AS City\n",
        "FROM Student\n",
        "INTERSECT\n",
        "SELECT FacCity AS City\n",
        "FROM Faculty\n",
        "'''\n",
        "Q3 =  psql.sqldf(txt)\n",
        "Q3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      City\n",
              "0  BOTHELL\n",
              "1  SEATTLE"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dee5de6e-459a-4abd-90ce-3bc8dbd6f6e1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        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>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BOTHELL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SEATTLE</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dee5de6e-459a-4abd-90ce-3bc8dbd6f6e1')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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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              "      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",
              "    [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-dee5de6e-459a-4abd-90ce-3bc8dbd6f6e1 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-dee5de6e-459a-4abd-90ce-3bc8dbd6f6e1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "NLrYlVpBtyNR",
        "outputId": "ef02d8c5-ed4e-43a5-e146-2118d53c2781"
      },
      "source": [
        "#Question 4-List  City names where Student lives but  Faculty does not live in .\n",
        "txt='''\n",
        "SELECT StdCity AS City\n",
        "FROM Student\n",
        "EXCEPT\n",
        "SELECT FacCity AS City\n",
        "FROM Faculty\n",
        "'''\n",
        "Q4 =  psql.sqldf(txt)\n",
        "Q4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      City\n",
              "0  REDMOND\n",
              "1   TACOMA"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2dc10026-2e08-425c-bdb2-21278d62ba04\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\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>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>REDMOND</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TACOMA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2dc10026-2e08-425c-bdb2-21278d62ba04')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-2dc10026-2e08-425c-bdb2-21278d62ba04 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-2dc10026-2e08-425c-bdb2-21278d62ba04');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "CrZASrIzuBAs",
        "outputId": "3002eebf-e20e-444b-9946-9e50ee314a3e"
      },
      "source": [
        "#Question 5-List City names where Faculty lives but  Student does not live in .\n",
        "txt='''\n",
        "SELECT FacCity AS City\n",
        "FROM Faculty\n",
        "EXCEPT\n",
        "SELECT StdCity AS City\n",
        "FROM Student\n",
        "'''\n",
        "Q5 =  psql.sqldf(txt)\n",
        "Q5"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       City\n",
              "0  BELLEVUE"
            ],
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              "      <th></th>\n",
              "      <th>City</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BELLEVUE</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-de26e359-11a5-43a9-a553-079487811481');\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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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
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          "height": 81
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        "id": "ddmnJfVwvOv5",
        "outputId": "55718f9d-c6dc-4bd0-9352-dd97ff147288"
      },
      "source": [
        "#Question 6-What is the mininimum, maximum and average GPA of Student?\n",
        "#How many students are there in student table?\n",
        "txt='''\n",
        "SELECT MIN(StdGPA), MAX(StdGPA), AVG(StdGPA), Count(*)\n",
        "FROM Student\n",
        "'''\n",
        "Q6 =  psql.sqldf(txt)\n",
        "Q6"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MIN(StdGPA)  MAX(StdGPA)  AVG(StdGPA)  Count(*)\n",
              "0          2.2          4.0     3.145455        11"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MIN(StdGPA)</th>\n",
              "      <th>MAX(StdGPA)</th>\n",
              "      <th>AVG(StdGPA)</th>\n",
              "      <th>Count(*)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.145455</td>\n",
              "      <td>11</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-2ab254b8-0e57-44ab-8409-90622ebe48f5 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-2ab254b8-0e57-44ab-8409-90622ebe48f5');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UjibYqBuwDnv",
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          "height": 175
        },
        "outputId": "3c4327af-1ba5-40b5-bf9e-51afa3ecabf0"
      },
      "source": [
        "#Question 7-Summarize minimum and maximium and average  GPA of student by StdClass. \n",
        "#How many student are there in each class?\n",
        "txt='''\n",
        "SELECT StdClass, MIN(StdGPA), MAX(StdGPA), AVG(StdGPA), Count(*)\n",
        "FROM Student\n",
        "GROUP BY StdClass\n",
        "'''\n",
        "Q7 =  psql.sqldf(txt)\n",
        "Q7"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  StdClass  MIN(StdGPA)  MAX(StdGPA)  AVG(StdGPA)  Count(*)\n",
              "0       FR          3.0          3.0        3.000         1\n",
              "1       JR          2.5          3.6        3.075         4\n",
              "2       SO          3.3          3.8        3.550         2\n",
              "3       SR          2.2          4.0        3.050         4"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StdClass</th>\n",
              "      <th>MIN(StdGPA)</th>\n",
              "      <th>MAX(StdGPA)</th>\n",
              "      <th>AVG(StdGPA)</th>\n",
              "      <th>Count(*)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FR</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>JR</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.6</td>\n",
              "      <td>3.075</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SO</td>\n",
              "      <td>3.3</td>\n",
              "      <td>3.8</td>\n",
              "      <td>3.550</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SR</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.050</td>\n",
              "      <td>4</td>\n",
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              "  </svg>\n",
              "      </button>\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",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd973c59-a99d-49e1-bc37-aaee40678f0b');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "KhMiBJbKxiL0",
        "outputId": "45f6b426-20c0-44bb-e1ed-da691dbd53f7"
      },
      "source": [
        "#Question 8-List the  the average StdGPA of student based on StdCity.\n",
        "#how many students lives in each city. Order the city names in descending order. \n",
        "txt='''\n",
        "SELECT StdCity, AVG(StdGPA), COUNT(*)\n",
        "FROM Student\n",
        "GROUP BY StdCity\n",
        "ORDER BY StdCity DESC\n",
        "'''\n",
        "Q8 =  psql.sqldf(txt)\n",
        "Q8"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   StdCity  AVG(StdGPA)  COUNT(*)\n",
              "0   TACOMA     3.500000         1\n",
              "1  SEATTLE     3.042857         7\n",
              "2  REDMOND     3.300000         1\n",
              "3  BOTHELL     3.250000         2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3dae7329-f320-4c60-a23c-88b410cb4440\">\n",
              "    <div 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>StdCity</th>\n",
              "      <th>AVG(StdGPA)</th>\n",
              "      <th>COUNT(*)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>TACOMA</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SEATTLE</td>\n",
              "      <td>3.042857</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>REDMOND</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BOTHELL</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3dae7329-f320-4c60-a23c-88b410cb4440')\"\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 0 24 24\"\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-3dae7329-f320-4c60-a23c-88b410cb4440 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-3dae7329-f320-4c60-a23c-88b410cb4440');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "xkfAOTJX4fqv",
        "outputId": "7eb806cb-7900-4083-abda-38ab669404bc"
      },
      "source": [
        "#Query 9-List the  the average StdGPA of student based on StdCity if there\n",
        "#are more than 1 student lives in the city .Order the city names in ascending order. \n",
        "#Hint: you will use group by and having\n",
        "txt='''\n",
        "SELECT StdCity, AVG(StdGPA), Count(*) AS Number\n",
        "FROM Student\n",
        "GROUP BY StdCity\n",
        "HAVING Number>1\n",
        "ORDER BY StdCity ASC\n",
        "'''\n",
        "Q9=  psql.sqldf(txt)\n",
        "Q9"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   StdCity  AVG(StdGPA)  Number\n",
              "0  BOTHELL     3.250000       2\n",
              "1  SEATTLE     3.042857       7"
            ],
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              "\n",
              "  <div id=\"df-a269845e-8422-446a-8cc2-2438b787c375\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StdCity</th>\n",
              "      <th>AVG(StdGPA)</th>\n",
              "      <th>Number</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>BOTHELL</td>\n",
              "      <td>3.250000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SEATTLE</td>\n",
              "      <td>3.042857</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a269845e-8422-446a-8cc2-2438b787c375')\"\n",
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              "              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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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\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-a269845e-8422-446a-8cc2-2438b787c375 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-a269845e-8422-446a-8cc2-2438b787c375');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0lRD-tV31F8h",
        "outputId": "7cbdc310-41b7-4082-8f2c-d2db5a007a74"
      },
      "source": [
        "#Query 10-How many faculty membes are there in each department?\n",
        "#Calculate the  the average salary of faculty in each department?\n",
        "txt='''\n",
        "SELECT FacDept, AVG(FacSalary), Count(*) AS Number\n",
        "FROM Faculty\n",
        "GROUP BY FacDept\n",
        "'''\n",
        "Q10 =  psql.sqldf(txt)\n",
        "Q10"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  FacDept  AVG(FacSalary)  Number\n",
              "0     FIN         70000.0       2\n",
              "1      MS         66250.0       4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4698c59d-e03f-476f-9267-9c6a197f3c5b\">\n",
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              "</style>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>FacDept</th>\n",
              "      <th>AVG(FacSalary)</th>\n",
              "      <th>Number</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FIN</td>\n",
              "      <td>70000.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MS</td>\n",
              "      <td>66250.0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4698c59d-e03f-476f-9267-9c6a197f3c5b')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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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              "      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",
              "    [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-4698c59d-e03f-476f-9267-9c6a197f3c5b 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-4698c59d-e03f-476f-9267-9c6a197f3c5b');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "uCrcBOHB5HJc",
        "outputId": "892462b7-1ea6-4652-f154-27534e9bace5"
      },
      "source": [
        "##Query 11-Get All student related information of student with lowest GPA.  \n",
        "txt='''\n",
        "SELECT *\n",
        "FROM Student\n",
        "ORDER BY StdGPA ASC\n",
        "LIMIT 1\n",
        "'''\n",
        "Q11 =  psql.sqldf(txt)\n",
        "Q11"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         StdNo StdFirstName StdLastName  StdCity StdState      StdZip  \\\n",
              "0  890-12-3456         LUKE      BRAZZI  SEATTLE       WA  98116-0021   \n",
              "\n",
              "  StdMajor StdClass  StdGPA  \n",
              "0       IS       SR     2.2  "
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>StdNo</th>\n",
              "      <th>StdFirstName</th>\n",
              "      <th>StdLastName</th>\n",
              "      <th>StdCity</th>\n",
              "      <th>StdState</th>\n",
              "      <th>StdZip</th>\n",
              "      <th>StdMajor</th>\n",
              "      <th>StdClass</th>\n",
              "      <th>StdGPA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>890-12-3456</td>\n",
              "      <td>LUKE</td>\n",
              "      <td>BRAZZI</td>\n",
              "      <td>SEATTLE</td>\n",
              "      <td>WA</td>\n",
              "      <td>98116-0021</td>\n",
              "      <td>IS</td>\n",
              "      <td>SR</td>\n",
              "      <td>2.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-51b0efd9-422f-46c0-bd17-8ad3e5c8a8b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-51b0efd9-422f-46c0-bd17-8ad3e5c8a8b1 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-51b0efd9-422f-46c0-bd17-8ad3e5c8a8b1');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    }
  ]
}