diff --git a/s24/Louis_Lecture_Notes/39_Final_Exam_Practice/final_exam_practice.ipynb b/s24/Louis_Lecture_Notes/39_Final_Exam_Practice/final_exam_practice.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..73c8f499c673fce88ab5b2b167ba2d948822b1ad
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@@ -0,0 +1,1718 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "227ea98e-6427-4bbf-bc65-7ac92d137b6c",
+ "metadata": {},
+ "source": [
+ "## Fall 2023"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9604c67f-4d2d-46da-b9f5-af6ef234a036",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "employee_data = {\n",
+ "\"Employee_ID\": [101, 102, 103, 104, 105],\n",
+ "\"Name\": [\"Alice\", \"Bob\", \"Charlie\", \"David\", \"Emma\"],\n",
+ "\"Department\": [\"HR\", \"Engineering\", \"Finance\", \"Engineering\", \"HR\"],\n",
+ "\"Salary\": [60000, 80000, 75000, 90000, 65000],\n",
+ "\"Years_Worked\": [2, 5, 3, 7, 1]\n",
+ "}\n",
+ "employees = pd.DataFrame(employee_data)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "56ac506c-3796-4f6f-bf72-9b4e5927b257",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "90000\n",
+ "90000\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Q1\n",
+ "print(employees[\"Salary\"].max())\n",
+ "\n",
+ "print(employees.loc[employees['Salary'].idxmax(),\"Salary\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "5d5ec5ac-8b9a-4f8d-a1b4-2eb4fd2f15f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 HR\n",
+ "1 Engineering\n",
+ "2 Finance\n",
+ "3 Engineering\n",
+ "4 HR\n",
+ "Name: Department, dtype: object\n",
+ "0 Business\n",
+ "1 Tech\n",
+ "2 Business\n",
+ "3 Tech\n",
+ "4 Business\n",
+ "Name: Department, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Q2\n",
+ "print(employees['Department'])\n",
+ "employees[\"Department\"] = employees[\"Department\"].apply(lambda val:\n",
+ "\"Tech\" if val in [\"Engineering\"] else \"Business\")\n",
+ "print(employees['Department'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "383ca6cd-878b-4be8-928e-99153484aae7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>Movie_ID</th>\n",
+ " <th>Title</th>\n",
+ " <th>Genre</th>\n",
+ " <th>Rating</th>\n",
+ " <th>Year</th>\n",
+ " <th>Director</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>The Matrix</td>\n",
+ " <td>Action</td>\n",
+ " <td>4.5</td>\n",
+ " <td>1999</td>\n",
+ " <td>Wachowskis</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>Inception</td>\n",
+ " <td>Sci-Fi</td>\n",
+ " <td>4.8</td>\n",
+ " <td>2010</td>\n",
+ " <td>Christopher Nolan</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>Pulp Fiction</td>\n",
+ " <td>Crime</td>\n",
+ " <td>4.2</td>\n",
+ " <td>1994</td>\n",
+ " <td>Quentin Tarantino</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>The Shawshank Redemption</td>\n",
+ " <td>Drama</td>\n",
+ " <td>4.9</td>\n",
+ " <td>1994</td>\n",
+ " <td>Frank Darabont</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>The Dark Knight</td>\n",
+ " <td>Action</td>\n",
+ " <td>4.7</td>\n",
+ " <td>2008</td>\n",
+ " <td>Christopher Nolan</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Movie_ID Title Genre Rating Year Director\n",
+ "0 1 The Matrix Action 4.5 1999 Wachowskis\n",
+ "1 2 Inception Sci-Fi 4.8 2010 Christopher Nolan\n",
+ "2 3 Pulp Fiction Crime 4.2 1994 Quentin Tarantino\n",
+ "3 4 The Shawshank Redemption Drama 4.9 1994 Frank Darabont\n",
+ "4 5 The Dark Knight Action 4.7 2008 Christopher Nolan"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "movies = pd.DataFrame({\n",
+ "\"Movie_ID\": [1, 2, 3, 4, 5],\n",
+ "\"Title\": [\"The Matrix\", \"Inception\", \"Pulp Fiction\",\n",
+ "\"The Shawshank Redemption\", \"The Dark Knight\"],\n",
+ "\"Genre\": [\"Action\", \"Sci-Fi\", \"Crime\", \"Drama\", \"Action\"],\n",
+ "\"Rating\": [4.5, 4.8, 4.2, 4.9, 4.7],\n",
+ "\"Year\": [1999, 2010, 1994, 1994, 2008],\n",
+ "\"Director\": [\"Wachowskis\", \"Christopher Nolan\", \"Quentin Tarantino\",\n",
+ "\"Frank Darabont\", \"Christopher Nolan\"]\n",
+ "})\n",
+ "movies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "a7db0595-5a8a-48e7-bfe7-75ebac4b20e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "pandas.core.series.Series"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q3\n",
+ "\n",
+ "type(movies[\"Genre\"].value_counts())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "4b47b4cb-d397-48f4-8fae-05429c50aa73",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "KeyError",
+ "evalue": "'Pulp Fiction'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Q4\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m movies\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPulp Fiction\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDirector\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1153\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1150\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 1152\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1393\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[1;32m 1392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1393\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_label(key, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexing.py:1343\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m 1341\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: AxisInt):\n\u001b[1;32m 1342\u001b[0m \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[0;32m-> 1343\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mxs(label, axis\u001b[38;5;241m=\u001b[39maxis)\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/generic.py:4236\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m 4234\u001b[0m new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[1;32m 4235\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4236\u001b[0m loc \u001b[38;5;241m=\u001b[39m index\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m 4238\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m 4239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
+ "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/core/indexes/range.py:418\u001b[0m, in \u001b[0;36mRangeIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 417\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Hashable):\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n\u001b[1;32m 419\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+ "\u001b[0;31mKeyError\u001b[0m: 'Pulp Fiction'"
+ ]
+ }
+ ],
+ "source": [
+ "# Q4\n",
+ "movies.loc[\"Pulp Fiction\"][\"Director\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "d1da11ec-3615-42fd-b7da-f2f4d69709d3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>year</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1994.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2004.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " year\n",
+ "0 1994.0\n",
+ "1 NaN\n",
+ "2 2004.0"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.DataFrame([\n",
+ "{\"year\":1994.0},\n",
+ "{\"year\":None},\n",
+ "{\"year\":2004.0}\n",
+ "])\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "cdd11f49-929a-4966-a82d-679691c2e95b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>year</th>\n",
+ " <th>decade</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1994.0</td>\n",
+ " <td>1990.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2004.0</td>\n",
+ " <td>2000.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " year decade\n",
+ "0 1994.0 1990.0\n",
+ "1 NaN NaN\n",
+ "2 2004.0 2000.0"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q5\n",
+ "df[\"decade\"] = df[\"year\"] // 10 * 10\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "9088d5ba-e391-4c75-abf9-2cc150884368",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 1994\n",
+ "2 2004\n",
+ "Name: year, dtype: int64"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q6\n",
+ "df[\"year\"].dropna().astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "7a178e67-3971-4d5d-b88a-4e1710c8882a",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>year</th>\n",
+ " <th>decade</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1994.0</td>\n",
+ " <td>1990.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>NaN</td>\n",
+ " <td>NaN</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2004.0</td>\n",
+ " <td>2000.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>2008.0</td>\n",
+ " <td>2000.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " year decade\n",
+ "0 1994.0 1990.0\n",
+ "1 NaN NaN\n",
+ "2 2004.0 2000.0\n",
+ "3 2008.0 2000.0"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.DataFrame([\n",
+ "{\"year\":1994, \"decade\": 1990},\n",
+ "{\"year\":None, \"decade\": None},\n",
+ "{\"year\":2004, \"decade\": 2000},\n",
+ "{\"year\":2008, \"decade\": 2000}\n",
+ "])\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "3d4d135e-06a0-42c4-90a5-48fa2a76d978",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q7\n",
+ "df[\"decade\"].value_counts().iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8922116e-5f13-4815-a71e-65e9cc77a315",
+ "metadata": {},
+ "source": [
+ "##### Q8\n",
+ "`<a href=\"https://www.google.com\">link</a>`<br>\n",
+ "<a href=\"https://www.google.com\">link</a>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "313994b5-6991-439b-b2d3-3c1886ce4cb5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Not 200\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Q9\n",
+ "import requests\n",
+ "\n",
+ "def fetch_data(api_url):\n",
+ " try:\n",
+ " response = requests.get(api_url)\n",
+ " status_code = response.status_code\n",
+ " except requests.HTTPError as e:\n",
+ " return None\n",
+ " if status_code == 200:\n",
+ " return \"Success\"\n",
+ " else:\n",
+ " return \"Not 200\"\n",
+ "\n",
+ "result = fetch_data(\"https://example.com/abc123\")\n",
+ "print(result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "4b047fab-12e4-4874-a395-53431e263fe4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Q10\n",
+ "import requests\n",
+ "\n",
+ "def save_html(url):\n",
+ " try:\n",
+ " response = requests.get(url)\n",
+ " response.raise_for_status()\n",
+ " file = open(\"web_page.html\", \"w\", encoding=\"utf-8\")\n",
+ " file.write(response.text) ###SOLUTION LINE ????\n",
+ " file.close()\n",
+ " except requests.HTTPError as e:\n",
+ " print(\"WARNING! Could not fetch page\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "15991384-1a60-4a5c-aa09-e88670e6bee2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Q11\n",
+ "from bs4 import BeautifulSoup\n",
+ "import os\n",
+ "\n",
+ "if os.path.exists(\"web_page.html\"):\n",
+ " file = open(\"web_page.html\")\n",
+ " html_content = file.read() ###SOLUTION LINE ????\n",
+ " soup = BeautifulSoup(html_content, \"html.parser\")\n",
+ " link = soup.find(\"a\")\n",
+ " file.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "0b64e8e4-0e7f-4e3c-aa81-1d8fdfac4b46",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "220"
+ ]
+ },
+ "execution_count": 31,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q12\n",
+ "\n",
+ "# import requests\n",
+ "# response = requests.get(\"https://www.example.com/data.json\")\n",
+ "# data_text = response.text\n",
+ "# data_json = response.json()\n",
+ "\n",
+ "import json\n",
+ "\n",
+ "data_text = '''{\"john\": 150, \"emma\": 220, \"mike\": 180}'''\n",
+ "data_json = json.loads(data_text)\n",
+ "\n",
+ "data_json[\"emma\"] ###SOLUTION LINE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "8873033e-97e0-47e0-84e1-461b3ff0b0f6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>index</th>\n",
+ " <th>name</th>\n",
+ " <th>industry</th>\n",
+ " <th>headquarters</th>\n",
+ " <th>established</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2</td>\n",
+ " <td>Toyota</td>\n",
+ " <td>Automotive</td>\n",
+ " <td>Japan</td>\n",
+ " <td>1937</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>6</td>\n",
+ " <td>Volkswagen</td>\n",
+ " <td>Automotive</td>\n",
+ " <td>Germany</td>\n",
+ " <td>1937</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index name industry headquarters established\n",
+ "0 2 Toyota Automotive Japan 1937\n",
+ "1 6 Volkswagen Automotive Germany 1937"
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q13\n",
+ "\n",
+ "import sqlite3\n",
+ "import pandas as pd\n",
+ "\n",
+ "# companies=pd.DataFrame({\n",
+ "# 'name':['Walmart','Samsung Electronics','Toyota','Amazon','Mercedes Benz','Apple','Volkswagen'],\n",
+ "# 'industry':['Retail','Electronics','Automotive','Retail','Automotive','Electronics','Automotive'],\n",
+ "# 'headquarters':['USA','South Korea','Japan','USA','Germany','USA','Germany'],\n",
+ "# 'established':[1962,1969,1937,1994,1926,1976,1937]\n",
+ "# })\n",
+ "conn = sqlite3.connect(\"companies.db\")\n",
+ "# companies.to_sql(\"companies\",conn)\n",
+ "\n",
+ "\n",
+ "\n",
+ "companies = pd.read_sql(\"SELECT * FROM companies\", conn)\n",
+ "\n",
+ "\n",
+ "query = \"SELECT * FROM companies WHERE established = 1937\" ### SOLUTION\n",
+ "pd.read_sql(query, conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "bd78ea9b-55db-4e55-a548-8b7b3ff80330",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>index</th>\n",
+ " <th>name</th>\n",
+ " <th>industry</th>\n",
+ " <th>headquarters</th>\n",
+ " <th>established</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>3</td>\n",
+ " <td>Amazon</td>\n",
+ " <td>Retail</td>\n",
+ " <td>USA</td>\n",
+ " <td>1994</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>5</td>\n",
+ " <td>Apple</td>\n",
+ " <td>Electronics</td>\n",
+ " <td>USA</td>\n",
+ " <td>1976</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0</td>\n",
+ " <td>Walmart</td>\n",
+ " <td>Retail</td>\n",
+ " <td>USA</td>\n",
+ " <td>1962</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index name industry headquarters established\n",
+ "0 3 Amazon Retail USA 1994\n",
+ "1 5 Apple Electronics USA 1976\n",
+ "2 0 Walmart Retail USA 1962"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q14\n",
+ "pd.read_sql(\"\"\"\n",
+ "SELECT * FROM companies WHERE headquarters = \"USA\" ORDER BY name ASC\n",
+ "\"\"\",conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "f34d5232-d7d2-4b67-9a8c-f58374c2e4fa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>headquarters</th>\n",
+ " <th>num_companies</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>USA</td>\n",
+ " <td>3</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " headquarters num_companies\n",
+ "0 USA 3"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q15\n",
+ "query = \"\"\"\n",
+ "SELECT headquarters, COUNT(*) AS num_companies\n",
+ "FROM companies\n",
+ "GROUP BY headquarters\n",
+ "HAVING num_companies > 2\n",
+ "ORDER BY num_companies DESC\n",
+ "\"\"\"\n",
+ "output = pd.read_sql(query, conn)\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "ade68c8c-b236-4017-903f-056fcd81f88b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>index</th>\n",
+ " <th>name</th>\n",
+ " <th>industry</th>\n",
+ " <th>headquarters</th>\n",
+ " <th>established</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0</td>\n",
+ " <td>Walmart</td>\n",
+ " <td>Retail</td>\n",
+ " <td>USA</td>\n",
+ " <td>1962</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index name industry headquarters established\n",
+ "0 0 Walmart Retail USA 1962"
+ ]
+ },
+ "execution_count": 43,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q16\n",
+ "pd.read_sql(\"\"\"\n",
+ "SELECT * FROM companies WHERE headquarters = \"USA\" AND industry = \"Retail\" ORDER BY established ASC Limit 1\n",
+ "\"\"\",conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "96ddf943-dec1-426a-b480-1d280dfdfd59",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " established num_companies\n",
+ "0 1937 2\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "1"
+ ]
+ },
+ "execution_count": 45,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q17\n",
+ "question_df = pd.read_sql(\"\"\"\n",
+ "SELECT established, COUNT(*) AS num_companies\n",
+ "FROM companies\n",
+ "GROUP BY established\n",
+ "HAVING num_companies > 1\n",
+ "\"\"\", conn)\n",
+ "print(question_df)\n",
+ "len(question_df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "4d9f7b22-bff3-433e-9c33-115fb507dfe3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>index</th>\n",
+ " <th>name</th>\n",
+ " <th>industry</th>\n",
+ " <th>headquarters</th>\n",
+ " <th>established</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>Samsung Electronics</td>\n",
+ " <td>Electronics</td>\n",
+ " <td>South Korea</td>\n",
+ " <td>1969</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>Toyota</td>\n",
+ " <td>Automotive</td>\n",
+ " <td>Japan</td>\n",
+ " <td>1937</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>4</td>\n",
+ " <td>Mercedes Benz</td>\n",
+ " <td>Automotive</td>\n",
+ " <td>Germany</td>\n",
+ " <td>1926</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>6</td>\n",
+ " <td>Volkswagen</td>\n",
+ " <td>Automotive</td>\n",
+ " <td>Germany</td>\n",
+ " <td>1937</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " index name industry headquarters established\n",
+ "0 1 Samsung Electronics Electronics South Korea 1969\n",
+ "1 2 Toyota Automotive Japan 1937\n",
+ "2 4 Mercedes Benz Automotive Germany 1926\n",
+ "3 6 Volkswagen Automotive Germany 1937"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q18 (the != is what is being tested)\n",
+ "non_usa = pd.read_sql(\"\"\"\n",
+ "SELECT *\n",
+ "FROM companies\n",
+ "WHERE headquarters != \"USA\"\n",
+ "\"\"\", conn)\n",
+ "non_usa"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b731e73f-dc21-441b-9591-a5df1f313c05",
+ "metadata": {},
+ "source": [
+ "#### Q19\n",
+ "**Question asking about differences between `.loc` and `.iloc`**\n",
+ "\n",
+ "Answer: loc is primarily used with row and column labels, while iloc is used with\n",
+ "integer positions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "9f559dae-aba9-455d-b4ab-ca0845c9eb44",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: xlabel='A', ylabel='C'>"
+ ]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Q20 - Answer: C A scatter plot with dots that are all the same color\n",
+ "\n",
+ "df = pd.DataFrame({\"A\":[1,2,3,4], \"B\":[2,7,5,8], \"C\":[3,6,9,12]})\n",
+ "df.plot.scatter(x=\"A\", y=\"C\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "id": "b50e53cf-5998-4914-95c0-d9bd813bd825",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Axes: ylabel='A'>"
+ ]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Q21 Pandas DOES support pie plots\n",
+ "df.plot.pie(y='A')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "id": "f2cb95c4-fd6d-4429-8ca9-ad9395df4018",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 0, 'Hello World')"
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Q22\n",
+ "ax = df.plot.bar()\n",
+ "ax.set_xlabel(\"Hello World\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "id": "0bac7315-9dc6-403e-953f-f37cc7749f5d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Q23\n",
+ "\n",
+ "import os\n",
+ "\n",
+ "def get_all_paths_in(directory):\n",
+ " paths = []\n",
+ " files = os.listdir(directory)\n",
+ " for file in files:\n",
+ " if file.startswith(\".\"):\n",
+ " continue\n",
+ " path = os.path.join(directory, file)\n",
+ " if os.path.isfile(path):\n",
+ " paths.append(path)\n",
+ " elif os.path.isdir(path):\n",
+ " paths.extend(get_all_paths_in(path)) ###SOLUTION ????\n",
+ " return sorted(paths, reverse=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "id": "09b189dc-9de7-4a90-9828-ce42cc6ac5fd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Red Giant': ['11 UMi', '14 And'],\n",
+ " 'White Dwarf': ['mu2 Sco'],\n",
+ " 'Neutron Star': ['HD 96127']}"
+ ]
+ },
+ "execution_count": 54,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q24\n",
+ "\n",
+ "stars_dict = {\n",
+ "\"11 UMi\": {\"Stellar Mass\": 2.78, \"Stellar Age\": 1.560},\n",
+ "\"14 And\": {\"Stellar Mass\": 1.78, \"Stellar Age\": 4.500},\n",
+ "\"CD Cet\": {\"Stellar Mass\": 0.16, \"Stellar Age\": 3.000},\n",
+ "\"mu2 Sco\": {\"Stellar Mass\": 9.1, \"Stellar Age\": 0.020},\n",
+ "\"HD 96127\": {\"Stellar Mass\": 12.94, \"Stellar Age\": 4.067}\n",
+ "}\n",
+ "\n",
+ "star_classes = {\n",
+ "\"Red Giant\": [],\n",
+ "\"White Dwarf\": [],\n",
+ "\"Neutron Star\": []\n",
+ "}\n",
+ "\n",
+ "# SOLUTION: < < <= < >=\n",
+ "for star in stars_dict:\n",
+ " star_info = stars_dict[star]\n",
+ " mass = star_info[\"Stellar Mass\"]\n",
+ " if mass == None:\n",
+ " continue\n",
+ " if 0.5 < mass < 8:\n",
+ " star_classes[\"Red Giant\"].append(star)\n",
+ " elif 8 <= mass < 12:\n",
+ " star_classes[\"White Dwarf\"].append(star)\n",
+ " elif mass >= 12:\n",
+ " star_classes[\"Neutron Star\"].append(star)\n",
+ "\n",
+ "star_classes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "id": "ead522cf-fc2c-4888-8c83-17cd2173a04c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4.067\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Q25\n",
+ "print(stars_dict[star_classes[\"Neutron Star\"][0]][\"Stellar Age\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "391c03c4-3fe7-404a-a484-d8b2bbd175ed",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Q26\n",
+ "import csv\n",
+ "import json\n",
+ "import os\n",
+ "\n",
+ "def process_csv(filename):\n",
+ " with open(filename) as file:\n",
+ " csv_reader = csv.reader(file)\n",
+ " list_data = list(csv_reader)\n",
+ " return list_data\n",
+ " \n",
+ "def read_json(path):\n",
+ " with open(path, encoding=\"utf-8\") as f:\n",
+ " return json.load(f)\n",
+ " \n",
+ "def get_planets_data(planet_file, mapping_file):\n",
+ " mapping_dict = read_json(mapping_file)\n",
+ " planets_csv = process_csv(planet_file)\n",
+ " planets_header = planets_csv[0]\n",
+ " planets_rows = planets_csv[1:]\n",
+ " return (planets_header, planets_rows, mapping_dict)\n",
+ "\n",
+ "# Root\n",
+ "# |\n",
+ "# |-- data\n",
+ "# | |-- planets1.csv\n",
+ "# | |-- mappings\n",
+ "# | |-- mapping1.json\n",
+ "# |\n",
+ "# |-- other_folder\n",
+ "\n",
+ "#SOLUTION\n",
+ "# get_planets_data(os.path.join(\"data\", \"planets1.csv\"),\n",
+ "# os.path.join(\"data\", \"mappings\", \"mapping1.json\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "352ffe18-be4b-4b34-a9ca-2ea46701ec48",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['Column 1', 'Column 2']"
+ ]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q27\n",
+ "data = \"\"\"\n",
+ "<table>\n",
+ "<thead>\n",
+ "<tr>\n",
+ "<th>Column 1</th>\n",
+ "<th>Column 2</th>\n",
+ "</tr>\n",
+ "</thead>\n",
+ "<tbody>\n",
+ "<tr>\n",
+ "<td>Row 1, Cell 1</td>\n",
+ "<td>Row 1, Cell 2</td>\n",
+ "</tr>\n",
+ "<tr>\n",
+ "<td>Row 2, Cell 1</td>\n",
+ "<td>Row 2, Cell 2</td>\n",
+ "</tr>\n",
+ "</tbody>\n",
+ "</table>\n",
+ "\"\"\"\n",
+ "\n",
+ "f=open(\"2023.html\",'w')\n",
+ "f.write(data)\n",
+ "f.close()\n",
+ "\n",
+ "from bs4 import BeautifulSoup\n",
+ "import pandas as pd\n",
+ "\n",
+ "with open(\"2023.html\", \"r\", encoding=\"utf-8\") as file:\n",
+ " html_content = file.read()\n",
+ "\n",
+ "soup = BeautifulSoup(html_content, \"html.parser\")\n",
+ "table = soup.find(\"table\")\n",
+ "headers = [val.get_text().strip() for val in table.find(\"thead\").find_all(\"th\")]\n",
+ "headers"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "15c56c7c-e2a9-4bf1-a593-bb7f0ed9fa86",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>Column 1</th>\n",
+ " <th>Column 2</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Row 1, Cell 1</td>\n",
+ " <td>Row 1, Cell 2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Row 2, Cell 1</td>\n",
+ " <td>Row 2, Cell 2</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Column 1 Column 2\n",
+ "0 Row 1, Cell 1 Row 1, Cell 2\n",
+ "1 Row 2, Cell 1 Row 2, Cell 2"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q28\n",
+ "table = soup.find(\"table\")\n",
+ "headers = [\"Column 1\", \"Column 2\"]\n",
+ "data = []\n",
+ "rows = table.find_all(\"tr\")\n",
+ "for row in rows[1:]:\n",
+ " cols = row.find_all(\"td\")\n",
+ " row_data = {}\n",
+ " for i in range(len(headers)):\n",
+ " row_data[headers[i]] = cols[i].get_text() ###SOLUTION ????\n",
+ " data.append(row_data)\n",
+ "\n",
+ "df = pd.DataFrame(data)\n",
+ "df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "d6048328-0f8e-469c-9250-924bbe8e66d0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None []\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Q29\n",
+ "print(soup.find(\"a\"), soup.find_all(\"a\"))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "47b5bddd-b827-4f6e-af43-af09b26bc7c7",
+ "metadata": {},
+ "source": [
+ "#### Q30 Primary Difference between `.find()` and `.find_all()`\n",
+ "answer: .find() returns the first match, while .find all() returns a list of\n",
+ "all matches"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "06521e9d-a527-45fb-8a8e-8f3b502e3a75",
+ "metadata": {},
+ "source": [
+ "#### Q31\n",
+ "Assume that all necessary imports have already been made. Please select the answer that\n",
+ "correctly replaces all of the ???? spaces (in top-to-bottom order) as they appear below to\n",
+ "prepare the rankings database:\n",
+ "```\n",
+ "var1 = \"rankings.db\"\n",
+ "var2 = \"rankings\"\n",
+ "var3 = \"rankings.json\"\n",
+ "\n",
+ "rankings = pd.read_json(????)\n",
+ "conn = sqlite3.connect(????)\n",
+ "rankings.to_sql(????, conn, if_exists=\"replace\", index=False)\n",
+ "```\n",
+ "\n",
+ "Answer: E. var3, var1, var2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "id": "256c4df3-4090-4c54-a588-53a5f1c70587",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 65,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# SETUP FOR Q32 and beyond\n",
+ "df = pd.DataFrame({\n",
+ " 'Year':[2022,2022,2022,2021],\n",
+ " 'Institution Name':['University of Tokyo','University of Munich','Kyoto University','Wisconsin-Madison'],\n",
+ " 'Country':['Japan','Germany','Japan','USA'],\n",
+ " 'Fac Student':[7.2, 14.3, 6.5, 15.2],\n",
+ " 'Cit Per Fac':[97.5, 97.2, 96.8, 96.1],\n",
+ " 'International':[99.7, 91.5, 98.9, 83.4]\n",
+ "})\n",
+ "df.to_sql(\"rankings\",conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "id": "ac8b5ac6-a9c0-478c-81b6-10d58376dbbf",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>Institution Name</th>\n",
+ " <th>Fac Student</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: [Institution Name, Fac Student]\n",
+ "Index: []"
+ ]
+ },
+ "execution_count": 67,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q32\n",
+ "pd.read_sql(\"\"\"\n",
+ "SELECT \"Institution Name\", \"Fac Student\"\n",
+ "FROM rankings\n",
+ "WHERE Year = 2023 AND Country = \"Brazil\"\n",
+ "ORDER BY \"Fac Student\" DESC\n",
+ "LIMIT 10\n",
+ "\"\"\",conn)\n",
+ "\n",
+ "# NOTE: Prefer double quotes for quoting identifiers, such as column or table names. It's the SQL standard.\n",
+ "# Backticks also work, but they're only supported for MySQL syntax compatibility. Single quotes are for string literals,\n",
+ "# not identifiers. Aug 5, 2014\n",
+ "# https://stackoverflow.com/questions/25141090/use-backticks-or-double-quotes-with-python-and-sqlite#:~:text=Prefer%20double%20quotes%20for%20quoting,for%20string%20literals%2C%20not%20identifiers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "id": "d66c5428-7fc1-4ffa-a095-0399d93bfafa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.7715279808433153"
+ ]
+ },
+ "execution_count": 73,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q33\n",
+ "qry = \"\"\"\n",
+ "SELECT \"International\", \"Cit Per Fac\" FROM rankings\n",
+ "\n",
+ "\"\"\" # took out this so some data in qry: WHERE \"year\" = 2020 AND \"Country\" = \"Germany\"\n",
+ "pd.read_sql(qry, conn).corr().loc[\"International\"].loc[\"Cit Per Fac\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "id": "ca82e3ba-f6b3-4a75-b73d-88658b101aef",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<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>Country</th>\n",
+ " <th>Num Institutions</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ "Empty DataFrame\n",
+ "Columns: [Country, Num Institutions]\n",
+ "Index: []"
+ ]
+ },
+ "execution_count": 74,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Q34\n",
+ "qry = \"\"\"SELECT Country, COUNT(*) AS \"Num Institutions\" FROM rankings\n",
+ "WHERE Year = 2020 GROUP BY Country ORDER BY \"Num Institutions\" DESC\n",
+ "LIMIT 5\"\"\"\n",
+ "pd.read_sql(qry,conn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "id": "dc95d1ed-5375-459a-85d7-df63a51222a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Q35\n",
+ "qry = \"\"\"\n",
+ "SELECT (\"Fac Student\" * \"Cit Per Fac\") AS \"Cit Per Student\"\n",
+ "\"\"\"\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "bc08b646-d7df-435c-b2c0-a9f0bc518014",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "conn.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16530e6a-83fd-47b0-a52b-819224f2ce8c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}