lec_32_database1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# known import statements\n",
    "from bs4 import BeautifulSoup\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "# let's import sqlite3 module\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warmup 1: Explore this HTML table of volunteer hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr> \n",
    "        <th>Name</th>\n",
    "        <th>Week 1</th>\n",
    "        <th>Week 2</th\n",
    "        ><th>Week 3</th> \n",
    "    </tr>\n",
    "    <tr> \n",
    "        <td>Therese</td>\n",
    "        <td>13</td>\n",
    "        <td>4</td>\n",
    "        <td>5</td> \n",
    "    </tr>\n",
    "    <tr> \n",
    "        <td>Carl</td>\n",
    "        <td>5</td>\n",
    "        <td>7</td>\n",
    "        <td>8</td> \n",
    "    </tr>\n",
    "    <tr> \n",
    "        <td>Marie</td>\n",
    "        <td>2</td>\n",
    "        <td>9</td>\n",
    "        <td>11</td> \n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warmup 2a: Parse \"hours.html\" using BeautifulSoup\n",
    "\n",
    "#### Step 1: Read contents from \"hours.html\" file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = open(\"hours.html\")\n",
    "data = f.read()\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Create a BeautifulSoup object instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "bs_obj = BeautifulSoup(data, 'html.parser')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 3: Parse the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = bs_obj.find(\"table\") # works only if there is 1 table\n",
    "\n",
    "# Q: what method do you need if the HTML has more than 1 table? \n",
    "# A: find_all method and then extract the appropriate table using indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Parse the header\n",
    "- Bonus: Use list comprehension "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name', 'Week 1', 'Week 2', 'Week 3']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th_elements = table.find_all(\"th\")\n",
    "header = [th.get_text() for th in th_elements]\n",
    "header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Parse the data rows and store data into a list of dict\n",
    "- Remember that you need to skip over the first tr (which contains the header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Name': 'Therese', 'Week 1': 13, 'Week 2': 4, 'Week 3': 5},\n",
       " {'Name': 'Carl', 'Week 1': 5, 'Week 2': 7, 'Week 3': 8},\n",
       " {'Name': 'Marie', 'Week 1': 2, 'Week 2': 9, 'Week 3': 11}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find all tr elements\n",
    "tr_elements = bs_obj.find_all(\"tr\")\n",
    "\n",
    "# Skip first tr row (header row)\n",
    "tr_elements = tr_elements[1:]\n",
    "\n",
    "# Initialize empty list\n",
    "work_hours = []\n",
    "\n",
    "# Iterate through the tr elements\n",
    "for tr in tr_elements:\n",
    "    # Find all \"td\" elements in this row\n",
    "    td_elements = tr.find_all(\"td\")\n",
    "    \n",
    "    # Create row dictionary\n",
    "    row_dict = {} # Key: column name (header); Value: cell's value\n",
    "    \n",
    "    # Iterate over indices of td elements\n",
    "    for idx in range(len(td_elements)): # Assumes that td_elements and header have same length\n",
    "        # Extract the td text\n",
    "        td_val = td_elements[idx].get_text()\n",
    "\n",
    "        # Make appropriate type conversions\n",
    "        # Use header instead of hardcoing index\n",
    "        if header[idx] in [\"Week 1\", \"Week 2\", \"Week 3\"]:\n",
    "            td_val = int(td_val)\n",
    "            \n",
    "        # Insert key-value pairs        \n",
    "        row_dict[header[idx]] = td_val\n",
    "        \n",
    "    # Append row dictionary into list\n",
    "    work_hours.append(row_dict)\n",
    "    \n",
    "work_hours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Warmup 3: Use appropriate os module to assert that bus.db in this directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert os.path.exists(\"bus.db\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## April 20: DataBase1\n",
    "\n",
    "### Learning Objectives:\n",
    "\n",
    "- Explain how a database is different from a CSV file or a JSON file\n",
    "- Use SQLite to connect to a database and pandas to query the database\n",
    "- Write basic queries on a database using SELECT, FROM, WHERE, ORDER BY, and LIMIT\n",
    "\n",
    "We will get started with slides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sqlite3.Connection"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the Bus data from 'bus.db'\n",
    "db_name = \"bus.db\"\n",
    "assert os.path.exists(db_name)\n",
    "# Why do we have to assert that database exists?\n",
    "# If the database file does not exist, connect function creates a brand new one!\n",
    "\n",
    "# open a connection object to our database file\n",
    "conn = sqlite3.connect(db_name)\n",
    "\n",
    "# Important note: we need to close 'conn' when we are done, at the end of the notebook file\n",
    "type(conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas has a .read_sql function  `pd.read_sql(query, connection)`\n",
    "- Allows us to process an SQL `query` on a SQL `connection`\n",
    "- stores the result in a Pandas DataFrame\n",
    "- First SQL query to always run on a database:\n",
    "```\n",
    "select * from sqlite_master\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
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       "    }\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>type</th>\n",
       "      <th>name</th>\n",
       "      <th>tbl_name</th>\n",
       "      <th>rootpage</th>\n",
       "      <th>sql</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>table</td>\n",
       "      <td>boarding</td>\n",
       "      <td>boarding</td>\n",
       "      <td>2</td>\n",
       "      <td>CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>index</td>\n",
       "      <td>ix_boarding_index</td>\n",
       "      <td>boarding</td>\n",
       "      <td>3</td>\n",
       "      <td>CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>table</td>\n",
       "      <td>routes</td>\n",
       "      <td>routes</td>\n",
       "      <td>55</td>\n",
       "      <td>CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n  \"...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>index</td>\n",
       "      <td>ix_routes_index</td>\n",
       "      <td>routes</td>\n",
       "      <td>57</td>\n",
       "      <td>CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    type               name  tbl_name  rootpage  \\\n",
       "0  table           boarding  boarding         2   \n",
       "1  index  ix_boarding_index  boarding         3   \n",
       "2  table             routes    routes        55   \n",
       "3  index    ix_routes_index    routes        57   \n",
       "\n",
       "                                                 sql  \n",
       "0  CREATE TABLE \"boarding\" (\\n\"index\" INTEGER,\\n ...  \n",
       "1  CREATE INDEX \"ix_boarding_index\"ON \"boarding\" ...  \n",
       "2  CREATE TABLE \"routes\" (\\n\"index\" INTEGER,\\n  \"...  \n",
       "3  CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"in...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This SQL query helps us know the table names, we don't use the other info \n",
    "df = pd.read_sql(\"select * from sqlite_master\", conn)\n",
    "df\n",
    "\n",
    "# Key observation: there are two tables: boarding and routes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Databases are more structured than CSV and JSON files:\n",
    "- all data contained inside one or more tables\n",
    "- all tables must be named, all columns must be named \n",
    "- all values in a column must be the same type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"boarding\" (\n",
      "\"index\" INTEGER,\n",
      "  \"StopID\" INTEGER,\n",
      "  \"Route\" INTEGER,\n",
      "  \"Lat\" REAL,\n",
      "  \"Lon\" REAL,\n",
      "  \"DailyBoardings\" REAL\n",
      ")\n",
      "CREATE INDEX \"ix_boarding_index\"ON \"boarding\" (\"index\")\n",
      "CREATE TABLE \"routes\" (\n",
      "\"index\" INTEGER,\n",
      "  \"OBJECTID\" INTEGER,\n",
      "  \"trips_routes_route_id\" INTEGER,\n",
      "  \"route_short_name\" INTEGER,\n",
      "  \"route_url\" TEXT,\n",
      "  \"ShapeSTLength\" REAL\n",
      ")\n",
      "CREATE INDEX \"ix_routes_index\"ON \"routes\" (\"index\")\n"
     ]
    }
   ],
   "source": [
    "# The SQL queries in sql column of the returned DataFrame show\n",
    "# how database was set up (not part of CS220).\n",
    "\n",
    "# Let's focus on the table names and column names\n",
    "for command in df[\"sql\"]:\n",
    "    print(command)\n",
    "    \n",
    "# Key observation: SQL has its own types (pandas takes care of the type conversions) \n",
    "# and the types are strictly enforced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most basic SQL query\n",
    "```\n",
    "SELECT <Column(s)> \n",
    "FROM <Table name>\n",
    "```\n",
    "- `SELECT` and `FROM` are mandatory clauses in a SQL query\n",
    "- Can use * to mean \"all columns\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>OBJECTID</th>\n",
       "      <th>trips_routes_route_id</th>\n",
       "      <th>route_short_name</th>\n",
       "      <th>route_url</th>\n",
       "      <th>ShapeSTLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>8052</td>\n",
       "      <td>1</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>32379.426524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "      <td>8053</td>\n",
       "      <td>2</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>96906.965571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>8054</td>\n",
       "      <td>3</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>76436.645644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>8055</td>\n",
       "      <td>4</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>64774.133485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>8056</td>\n",
       "      <td>5</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>61216.722662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>120</td>\n",
       "      <td>8109</td>\n",
       "      <td>78</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>95826.277218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>121</td>\n",
       "      <td>8110</td>\n",
       "      <td>80</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>31831.761009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>59</td>\n",
       "      <td>122</td>\n",
       "      <td>8111</td>\n",
       "      <td>81</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>26536.800591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>123</td>\n",
       "      <td>8112</td>\n",
       "      <td>82</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>23287.980173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>124</td>\n",
       "      <td>8113</td>\n",
       "      <td>84</td>\n",
       "      <td>http://www.cityofmadison.com/Metro/schedules/R...</td>\n",
       "      <td>20681.958334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index  OBJECTID  trips_routes_route_id  route_short_name  \\\n",
       "0       0        63                   8052                 1   \n",
       "1       1        64                   8053                 2   \n",
       "2       2        65                   8054                 3   \n",
       "3       3        66                   8055                 4   \n",
       "4       4        67                   8056                 5   \n",
       "..    ...       ...                    ...               ...   \n",
       "57     57       120                   8109                78   \n",
       "58     58       121                   8110                80   \n",
       "59     59       122                   8111                81   \n",
       "60     60       123                   8112                82   \n",
       "61     61       124                   8113                84   \n",
       "\n",
       "                                            route_url  ShapeSTLength  \n",
       "0   http://www.cityofmadison.com/Metro/schedules/R...   32379.426524  \n",
       "1   http://www.cityofmadison.com/Metro/schedules/R...   96906.965571  \n",
       "2   http://www.cityofmadison.com/Metro/schedules/R...   76436.645644  \n",
       "3   http://www.cityofmadison.com/Metro/schedules/R...   64774.133485  \n",
       "4   http://www.cityofmadison.com/Metro/schedules/R...   61216.722662  \n",
       "..                                                ...            ...  \n",
       "57  http://www.cityofmadison.com/Metro/schedules/R...   95826.277218  \n",
       "58  http://www.cityofmadison.com/Metro/schedules/R...   31831.761009  \n",
       "59  http://www.cityofmadison.com/Metro/schedules/R...   26536.800591  \n",
       "60  http://www.cityofmadison.com/Metro/schedules/R...   23287.980173  \n",
       "61  http://www.cityofmadison.com/Metro/schedules/R...   20681.958334  \n",
       "\n",
       "[62 rows x 6 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas continues to be an awesome tool\n",
    "# pandas allows us to write a SQL query and create a DataFrame\n",
    "pd.read_sql(\"select * from routes\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>index</th>\n",
       "      <th>StopID</th>\n",
       "      <th>Route</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>DailyBoardings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1163</td>\n",
       "      <td>27</td>\n",
       "      <td>43.073655</td>\n",
       "      <td>-89.385427</td>\n",
       "      <td>1.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1163</td>\n",
       "      <td>47</td>\n",
       "      <td>43.073655</td>\n",
       "      <td>-89.385427</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1163</td>\n",
       "      <td>75</td>\n",
       "      <td>43.073655</td>\n",
       "      <td>-89.385427</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1164</td>\n",
       "      <td>6</td>\n",
       "      <td>43.106465</td>\n",
       "      <td>-89.340021</td>\n",
       "      <td>10.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1167</td>\n",
       "      <td>3</td>\n",
       "      <td>43.077867</td>\n",
       "      <td>-89.369993</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3967</th>\n",
       "      <td>3967</td>\n",
       "      <td>6533</td>\n",
       "      <td>67</td>\n",
       "      <td>43.057329</td>\n",
       "      <td>-89.510756</td>\n",
       "      <td>16.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3968</th>\n",
       "      <td>3968</td>\n",
       "      <td>6539</td>\n",
       "      <td>15</td>\n",
       "      <td>43.064361</td>\n",
       "      <td>-89.517233</td>\n",
       "      <td>15.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969</th>\n",
       "      <td>3969</td>\n",
       "      <td>6541</td>\n",
       "      <td>3</td>\n",
       "      <td>43.049934</td>\n",
       "      <td>-89.478167</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3970</th>\n",
       "      <td>3970</td>\n",
       "      <td>6543</td>\n",
       "      <td>70</td>\n",
       "      <td>43.093289</td>\n",
       "      <td>-89.501726</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3971</th>\n",
       "      <td>3971</td>\n",
       "      <td>6543</td>\n",
       "      <td>71</td>\n",
       "      <td>43.093289</td>\n",
       "      <td>-89.501726</td>\n",
       "      <td>6.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3972 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      index  StopID  Route        Lat        Lon  DailyBoardings\n",
       "0         0    1163     27  43.073655 -89.385427            1.03\n",
       "1         1    1163     47  43.073655 -89.385427            0.11\n",
       "2         2    1163     75  43.073655 -89.385427            0.34\n",
       "3         3    1164      6  43.106465 -89.340021           10.59\n",
       "4         4    1167      3  43.077867 -89.369993            3.11\n",
       "...     ...     ...    ...        ...        ...             ...\n",
       "3967   3967    6533     67  43.057329 -89.510756           16.88\n",
       "3968   3968    6539     15  43.064361 -89.517233           15.53\n",
       "3969   3969    6541      3  43.049934 -89.478167            2.56\n",
       "3970   3970    6543     70  43.093289 -89.501726            0.11\n",
       "3971   3971    6543     71  43.093289 -89.501726            6.73\n",
       "\n",
       "[3972 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Now write a SQL query for displaying all columns from boarding table\n",
    "pd.read_sql(\"select * from boarding\", conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optional SQL clauses\n",
    "- WHERE: filters rows based on a column condition\n",
    "- ORDER BY: sorting (`ASC` or `DESC` after the column name specify the ordering)\n",
    "- LIMIT: simplistic filter (similar to slicing / head/tail functions in pandas DataFrames)"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202021-11-23%20at%201.43.54%20PM.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Screen%20Shot%202021-11-23%20at%201.43.54%20PM.png](attachment:Screen%20Shot%202021-11-23%20at%201.43.54%20PM.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are all the details of route 80 bus stops?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>StopID</th>\n",
       "      <th>Route</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>DailyBoardings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>732</td>\n",
       "      <td>2007</td>\n",
       "      <td>80</td>\n",
       "      <td>43.076436</td>\n",
       "      <td>-89.424388</td>\n",
       "      <td>72.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>733</td>\n",
       "      <td>2014</td>\n",
       "      <td>80</td>\n",
       "      <td>43.089239</td>\n",
       "      <td>-89.433760</td>\n",
       "      <td>99.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>735</td>\n",
       "      <td>2018</td>\n",
       "      <td>80</td>\n",
       "      <td>43.086293</td>\n",
       "      <td>-89.435043</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>737</td>\n",
       "      <td>2023</td>\n",
       "      <td>80</td>\n",
       "      <td>43.078800</td>\n",
       "      <td>-89.429795</td>\n",
       "      <td>100.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>738</td>\n",
       "      <td>2026</td>\n",
       "      <td>80</td>\n",
       "      <td>43.086248</td>\n",
       "      <td>-89.436661</td>\n",
       "      <td>18.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>739</td>\n",
       "      <td>2027</td>\n",
       "      <td>80</td>\n",
       "      <td>43.080259</td>\n",
       "      <td>-89.428067</td>\n",
       "      <td>4.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>740</td>\n",
       "      <td>2034</td>\n",
       "      <td>80</td>\n",
       "      <td>43.086445</td>\n",
       "      <td>-89.433772</td>\n",
       "      <td>120.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>741</td>\n",
       "      <td>2039</td>\n",
       "      <td>80</td>\n",
       "      <td>43.089158</td>\n",
       "      <td>-89.438057</td>\n",
       "      <td>86.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>742</td>\n",
       "      <td>2041</td>\n",
       "      <td>80</td>\n",
       "      <td>43.084252</td>\n",
       "      <td>-89.433487</td>\n",
       "      <td>1.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>743</td>\n",
       "      <td>2048</td>\n",
       "      <td>80</td>\n",
       "      <td>43.084386</td>\n",
       "      <td>-89.433784</td>\n",
       "      <td>83.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>744</td>\n",
       "      <td>2050</td>\n",
       "      <td>80</td>\n",
       "      <td>43.080886</td>\n",
       "      <td>-89.428351</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>745</td>\n",
       "      <td>2053</td>\n",
       "      <td>80</td>\n",
       "      <td>43.077045</td>\n",
       "      <td>-89.424906</td>\n",
       "      <td>3.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>746</td>\n",
       "      <td>2054</td>\n",
       "      <td>80</td>\n",
       "      <td>43.086668</td>\n",
       "      <td>-89.441612</td>\n",
       "      <td>177.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>747</td>\n",
       "      <td>2061</td>\n",
       "      <td>80</td>\n",
       "      <td>43.089784</td>\n",
       "      <td>-89.437007</td>\n",
       "      <td>57.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>749</td>\n",
       "      <td>2071</td>\n",
       "      <td>80</td>\n",
       "      <td>43.090501</td>\n",
       "      <td>-89.435587</td>\n",
       "      <td>32.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>750</td>\n",
       "      <td>2076</td>\n",
       "      <td>80</td>\n",
       "      <td>43.079006</td>\n",
       "      <td>-89.429203</td>\n",
       "      <td>41.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>751</td>\n",
       "      <td>2082</td>\n",
       "      <td>80</td>\n",
       "      <td>43.086328</td>\n",