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  {
   "cell_type": "markdown",
   "id": "5967dd2e-c624-4367-9130-3035f1d029f2",
   "metadata": {},
   "source": [
    "# Query Results and Pandas Dataframes\n",
    "\n",
    "#### Pandas is a key Python library for data science. It is built upon NumPy, the library for doing numerical computations with matrices.\n",
    "\n",
    "#### The DataFrame is a principal data structure in the Pandas library:\n",
    "- It is a table with rows and columns.\n",
    "- It is an ideal structure to store query results.\n",
    "- It supports many data analytical functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2769b7-ab08-4ad8-af36-6257935e2508",
   "metadata": {},
   "outputs": [],
   "source": [
    "from DATA225utils import make_connection\n",
    "from pandas import DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0b2e68e-fa5c-4c0c-ab1f-6248d33897b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = make_connection('school.ini')\n",
    "cursor = conn.cursor()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e46252d-f8aa-42e3-adb9-890d4a9d027e",
   "metadata": {},
   "source": [
    "### Who are the students of Mabel Flynn?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5642089a-dcc2-4c67-9eb1-3b218a002e88",
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = ( \"\"\"\n",
    "        SELECT student.first, student.last, subject \n",
    "        FROM student, teacher, class, takes \n",
    "        WHERE teacher.last = 'Flynn' \n",
    "        AND teacher.first = 'Mabel' \n",
    "        AND teacher_id = teacher.id \n",
    "        AND code = class_code \n",
    "        AND student_id = student.id \n",
    "        ORDER BY subject, student.last\n",
    "        \"\"\"\n",
    "      )\n",
    "\n",
    "cursor.execute(sql)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cc7ce8b-b61d-4fd8-b951-383a9c0eab52",
   "metadata": {},
   "outputs": [],
   "source": [
    "rows  = cursor.fetchall()\n",
    "count = cursor.rowcount\n",
    "\n",
    "print(f'count = {count}')\n",
    "print()\n",
    "display(rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f844d8-d8ee-498a-b62b-63a8e078ab6b",
   "metadata": {},
   "source": [
    "### Get the names of the columns using **list comprehension**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83d5aa74-023f-4bc6-a647-a7c2bc8f21a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = cursor.description\n",
    "column_names = [column_info[0] for column_info in columns]\n",
    "\n",
    "columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12546759-9e01-475a-bd63-9e142baa08f9",
   "metadata": {},
   "source": [
    "### Get the names of the columns using a `for` loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4342520-53c0-404b-b7b6-22914c5968d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = []\n",
    "for column_info in columns:\n",
    "    column_names.append(column_info[0])\n",
    "\n",
    "column_names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0cb8f58-a748-4953-934a-6da0d8530703",
   "metadata": {},
   "source": [
    "### Create and display the dataframe if there were rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd795411-b036-4e57-8c91-440e73949243",
   "metadata": {},
   "outputs": [],
   "source": [
    "if count > 0:\n",
    "    df = DataFrame(rows)\n",
    "    df.columns = column_names\n",
    "    display(df)\n",
    "    \n",
    "else:\n",
    "    print('No students.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae113741-af4a-4336-93cb-1ad2709d15b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor.close()\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d048de8-c8dd-46e7-9466-c6f0463bfee9",
   "metadata": {},
   "source": [
    "#### Fetching query results into a dataframe is a common operation we can add as another function in our Python database utilities file **`DATA225utils.py`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "266ccf4b-320f-4e8d-8b3c-9b51810eabc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright (c) 2023 by Ronald Mak"
   ]
  }
 ],
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