02-Data_Ingestion-Hierarchical.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Ingestion – Hierarchical Formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.setup import *\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout err\n",
    "%%R\n",
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JSON"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What JSON Looks Like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 application/json; charset=utf-8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'{\"1\":{\"name\":\"Guido van Rossum\",\"password\":\"unladenswallow\",\"details\":{\"profession\":\"ex-BDFL\"}},\"2\":{\"name\":\"Brendan Eich\",\"password\":\"nontransitiveequality\",\"details\":{\"profession\":\"Mozillan\"}},\"3\":{\"name\":\"Ken Thompson\",\"password\":\"p/q2-q4!\",\"details\":{\"profession\":\"Unix Creator\"}}}'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A response to an HTTP request\n",
    "response = requests.get('http://localhost:3001/users') \n",
    "\n",
    "# Show status code, content-type, and JSON body\n",
    "print(response.status_code, response.headers['Content-Type'])\n",
    "response.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"1\": {\n",
      "    \"name\": \"Guido van Rossum\",\n",
      "    \"password\": \"unladenswallow\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"ex-BDFL\"\n",
      "    }\n",
      "  },\n",
      "  \"2\": {\n",
      "    \"name\": \"Brendan Eich\",\n",
      "    \"password\": \"nontransitiveequality\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"Mozillan\"\n",
      "    }\n",
      "  },\n",
      "  \"3\": {\n",
      "    \"name\": \"Ken Thompson\",\n",
      "    \"password\": \"p/q2-q4!\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"Unix Creator\"\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pprint_json(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/3001.json', 'w') as fh:\n",
    "    fh.write(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"1\": {\n",
      "    \"name\": \"Guido van Rossum\",\n",
      "    \"password\": \"unladenswallow\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"ex-BDFL\"\n",
      "    }\n",
      "  },\n",
      "  \"2\": {\n",
      "    \"name\": \"Brendan Eich\",\n",
      "    \"password\": \"nontransitiveequality\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"Mozillan\"\n",
      "    }\n",
      "  },\n",
      "  \"3\": {\n",
      "    \"name\": \"Ken Thompson\",\n",
      "    \"password\": \"p/q2-q4!\",\n",
      "    \"details\": {\n",
      "      \"profession\": \"Unix Creator\"\n",
      "    }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "jq . data/3001.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  '1': {\n",
      "    name: 'Guido van Rossum',\n",
      "    password: 'unladenswallow',\n",
      "    details: { profession: 'ex-BDFL' }\n",
      "  },\n",
      "  '2': {\n",
      "    name: 'Brendan Eich',\n",
      "    password: 'nontransitiveequality',\n",
      "    details: { profession: 'Mozillan' }\n",
      "  },\n",
      "  '3': {\n",
      "    name: 'Ken Thompson',\n",
      "    password: 'p/q2-q4!',\n",
      "    details: { profession: 'Unix Creator' }\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "js=\"\n",
    "const fs = require('fs');\n",
    "let raw = fs.readFileSync('data/3001.json');\n",
    "let users = JSON.parse(raw);\n",
    "console.log(users);\n",
    "\"\n",
    "echo $js | node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'name': 'Guido van Rossum',\n",
       "  'password': 'unladenswallow',\n",
       "  'details': {'profession': 'ex-BDFL'}},\n",
       " '2': {'name': 'Brendan Eich',\n",
       "  'password': 'nontransitiveequality',\n",
       "  'details': {'profession': 'Mozillan'}},\n",
       " '3': {'name': 'Ken Thompson',\n",
       "  'password': 'p/q2-q4!',\n",
       "  'details': {'profession': 'Unix Creator'}}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/3001.json') as fh:\n",
    "    # Could also call `json.load(fh)` to read file\n",
    "    raw = fh.read()\n",
    "    users = json.loads(raw)\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "$`3`\n",
      "$`3`$name\n",
      "[1] \"Ken Thompson\"\n",
      "\n",
      "$`3`$password\n",
      "[1] \"p/q2-q4!\"\n",
      "\n",
      "$`3`$details\n",
      "$`3`$details$profession\n",
      "[1] \"Unix Creator\"\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(rjson)\n",
    "result <- fromJSON(file = \"data/3001.json\")\n",
    "result[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NaN Handling and Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1e+308, inf, 1.2345678901234567]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# An interpreted float, an overflow, and a truncation\n",
    "json_str = \"[1e308, 1e309, 1.2345678901234567890]\"\n",
    "json.loads(json_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 1.000000000000000010979e+308                          Inf\n",
      "[3]  1.234567890123456690432e+00\n"
     ]
    }
   ],
   "source": [
    "%%R -i json_str\n",
    "options(digits = 22)\n",
    "fromJSON(json_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Decimal('1E+308'), Decimal('1E+309'), Decimal('1.2345678901234567890')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplejson.loads(json_str, use_decimal=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[nan, inf, -inf]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "specials = \"[NaN, Infinity, -Infinity]\"\n",
    "vals = json.loads(specials)\n",
    "vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  NaN  Inf -Inf\n",
      "[1] \"R version of 'enhanced JSON':\"\n",
      "[1] \"[\\\"NaN\\\",\\\"Inf\\\",\\\"-Inf\\\"]\"\n"
     ]
    }
   ],
   "source": [
    "%%R -i vals\n",
    "vals = c(NaN, Inf, -Inf)\n",
    "print(vals)\n",
    "print(\"R version of 'enhanced JSON':\")\n",
    "rjson_str = toJSON(vals)  # function from rjson library\n",
    "print(rjson_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Read back in 'enhanced JSON':\"\n",
      "[1] \"NaN\"  \"Inf\"  \"-Inf\"\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "print(\"Read back in 'enhanced JSON':\")\n",
    "fromJSON(rjson_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "undefined:1\n",
      "[NaN, Infinity, -Infinity]\n",
      " ^\n",
      "\n",
      "SyntaxError: Unexpected token N in JSON at position 1\n",
      "    at JSON.parse (<anonymous>)\n",
      "    at [stdin]:1:6\n",
      "    at Script.runInThisContext (vm.js:132:18)\n",
      "    at Object.runInThisContext (vm.js:309:38)\n",
      "    at internal/process/execution.js:77:19\n",
      "    at [stdin]-wrapper:6:22\n",
      "    at evalScript (internal/process/execution.js:76:60)\n",
      "    at internal/main/eval_stdin.js:29:5\n",
      "    at Socket.<anonymous> (internal/process/execution.js:198:5)\n",
      "    at Socket.emit (events.js:327:22)\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "js=\"JSON.parse('[NaN, Infinity, -Infinity]');\"\n",
    "echo $js | node | cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['INVALID', 'INVALID', 'INVALID']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json.loads(\"[NaN, Infinity, -Infinity]\", parse_constant=lambda _: \"INVALID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSONDecodeError\n",
      "Expecting value: line 1 column 2 (char 1)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    json.loads(\"[nan, +Inf, Foobar]\", parse_constant=lambda _: \"INVALID\")\n",
    "except Exception as err:\n",
    "    print_err(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "  null,\n",
      "  1.7976931348623157e+308,\n",
      "  -1.7976931348623157e+308\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "echo \"[NaN, inf, -Infinity]\" | jq ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\t{\"ts\":\"2020-06-18T10:44:13\",\n",
      "\"logged_in\":{\"username\":\"foo\"},\n",
      "\"connection\":{\"addr\":\"1.2.3.4\",\"port\":5678}}\n",
      "2\t{\"ts\":\"2020-06-18T10:44:15\",\n",
      "\"registered\":{\"username\":\"bar\",\"email\":\"bar@example.com\"},\n",
      "\"connection\":{\"addr\":\"2.3.4.5\",\"port\":6789}}\n",
      "3\t{\"ts\":\"2020-06-18T10:44:16\",\n",
      "\"logged_out\":{\"username\":\"foo\"},\n",
      "\"connection\":{\"addr\":\"1.2.3.4\",\"port\":5678}}\n",
      "4\t{\"ts\":\"2020-06-18T10:47:22\",\n",
      "\"registered\":{\"username\":\"baz\",\"email\":\"baz@example.net\"},\n",
      "\"connection\":{\"addr\":\"3.4.5.6\",\"port\":7890}}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cat -n data/jsonlines.log | fmt -w55 | tr -d \" \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"username\":\"bar\",\"email\":\"bar@example.com\"}\n",
      "{\"username\":\"baz\",\"email\":\"baz@example.net\"}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# Extract registrations\n",
    "grep \"registered\" data/jsonlines.log |\n",
    "    sed 's/^.*registered\"://' |\n",
    "    sed 's/}.*/}/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"username\": \"bar\",\n",
      "  \"email\": \"bar@example.com\"\n",
      "}\n",
      "{\n",
      "  \"username\": \"baz\",\n",
      "  \"email\": \"baz@example.net\"\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "jq '.registered | select(.username != null)' data/jsonlines.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'username': 'bar', 'email': 'bar@example.com'}\n",
      "{'username': 'baz', 'email': 'baz@example.net'}\n"
     ]
    }
   ],
   "source": [
    "with open('data/jsonlines.log') as log:\n",
    "    for line in log:\n",
    "        record = json.loads(line)\n",
    "        if 'registered' in record:\n",
    "            user = record['registered']\n",
    "            if 'username' in user:\n",
    "                print(user)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GeoJSON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['type', 'features'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/gz_2010_us_050_00_20m.json', encoding='ISO-8859-1') as fh:\n",
    "    counties = json.load(fh)\n",
    "\n",
    "counties.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('FeatureCollection', list, 3221)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties['type'], type(counties['features']), len(counties['features'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Feature',\n",
       " 'properties': {'GEO_ID': '0500000US19153',\n",
       "  'STATE': '19',\n",
       "  'COUNTY': '153',\n",
       "  'NAME': 'Polk',\n",
       "  'LSAD': 'County',\n",
       "  'CENSUSAREA': 573.795},\n",
       " 'geometry': {'type': 'Polygon',\n",
       "  'coordinates': [[[-93.328614, 41.507824],\n",
       "    [-93.328486, 41.49134],\n",
       "    [-93.328407, 41.490921],\n",
       "    [-93.41226, 41.505549],\n",
       "    [-93.790612, 41.511916],\n",
       "    [-93.814282, 41.600448],\n",
       "    [-93.815527, 41.863419],\n",
       "    [-93.698032, 41.86337],\n",
       "    [-93.347933, 41.863104],\n",
       "    [-93.348681, 41.600999],\n",
       "    [-93.328614, 41.507824]]]}}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties['features'][999]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "patches, colors, ncolor = [], [], 8\n",
    "\n",
    "for n, county in enumerate(counties['features']):\n",
    "    # Only use first polygon if multiple discontiguous regions\n",
    "    poly = np.array(county['geometry']['coordinates'][0])\n",
    "    poly = poly.reshape(-1, 2)\n",
    "    polygon = Polygon(poly)\n",
    "    patches.append(polygon)\n",
    "    colors.append(n % ncolor)\n",
    "\n",
    "p = PatchCollection(patches, cmap=cm.get_cmap('Greys', ncolor))\n",
    "p.set_array(np.array(colors))\n",
    "ax.add_collection(p)\n",
    "\n",
    "ax.set_ylim(24, 50)\n",
    "ax.set_ylabel(\"Latitude\")\n",
    "ax.set_xlim(-126, -67)\n",
    "ax.set_xlabel(\"Longitude\")\n",
    "ax.set_title(\"Counties of the United States\");\n",
    "plt.savefig(\"img/(Ch02)Counties of the United States.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidy Geography"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Name</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>FIPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Guam</td>\n",
       "      <td>GU</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Northern Mariana Islands</td>\n",
       "      <td>MP</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>PR</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Virgin Islands</td>\n",
       "      <td>VI</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Name Postal Code  FIPS\n",
       "0                    Alabama          AL     1\n",
       "1                     Alaska          AK     2\n",
       "2                    Arizona          AZ     4\n",
       "3                   Arkansas          AR     5\n",
       "..                       ...         ...   ...\n",
       "51                      Guam          GU    66\n",
       "52  Northern Mariana Islands          MP    69\n",
       "53               Puerto Rico          PR    72\n",
       "54            Virgin Islands          VI    78\n",
       "\n",
       "[55 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fips = pd.read_csv('data/FIPS.tsv', sep='\\t')\n",
    "fips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FIPS\n",
       "1                      Alabama\n",
       "2                       Alaska\n",
       "4                      Arizona\n",
       "5                     Arkansas\n",
       "                ...           \n",
       "66                        Guam\n",
       "69    Northern Mariana Islands\n",
       "72                 Puerto Rico\n",
       "78              Virgin Islands\n",
       "Name: Name, Length: 55, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fips_map = fips.set_index('FIPS').Name\n",
    "fips_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extremes(coords):\n",
    "    lat, lon = [], []\n",
    "    # Expect a list of lists of lists\n",
    "    for region in coords:\n",
    "        for point in region:\n",
    "            lat.append(point[1])\n",
    "            lon.append(point[0])\n",
    "    # We are assuming western hemisphere here\n",
    "    north = max(lat)\n",
    "    south = min(lat)\n",
    "    east = max(lon)\n",
    "    west = min(lon)\n",
    "    return north, south, east, west"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def county_summary(features):\n",
    "    geo_id = []\n",
    "    state, county_name, area = [], [], []\n",
    "    north, south, east, west = [], [], [], []\n",
    "\n",
    "    for county in features:\n",
    "        props = county['properties']\n",
    "        polys = county['geometry']['coordinates']\n",
    "        geo_id.append(props['GEO_ID'])\n",
    "        # District of Columbia not US state (default to None)\n",
    "        state_name = fips_map.get(int(props['STATE']), None)\n",
    "        state.append(state_name)\n",
    "        county_name.append(props['NAME'])\n",
    "        area.append(props['CENSUSAREA'] * 2.59)\n",
    "        n, s, e, w = extremes(polys)\n",
    "        north.append(n)\n",
    "        south.append(s)\n",
    "        east.append(e)\n",
    "        west.append(w)\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "            'geo_id': geo_id,\n",
    "            'state': state,\n",
    "            'county': county_name,\n",
    "            'area': area,\n",
    "            'northmost': north,\n",
    "            'southmost': south,\n",
    "            'eastmost': east,\n",
    "            'westmost': west\n",
    "        })\n",
    "    return df.set_index('geo_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_counties(df):\n",
    "    assert (df.northmost > df.southmost).all()\n",
    "    assert (df.westmost < df.eastmost).all()\n",
    "    assert (df.area > 0).all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>area</th>\n",
       "      <th>northmost</th>\n",
       "      <th>southmost</th>\n",
       "      <th>eastmost</th>\n",
       "      <th>westmost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0500000US01001</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>1539.58924</td>\n",
       "      <td>32.7074</td>\n",
       "      <td>32.3408</td>\n",
       "      <td>-86.4112</td>\n",
       "      <td>-86.9176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01009</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>1669.96984</td>\n",
       "      <td>34.2593</td>\n",
       "      <td>33.7653</td>\n",
       "      <td>-86.3035</td>\n",
       "      <td>-86.9634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01017</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chambers</td>\n",
       "      <td>1545.01529</td>\n",
       "      <td>33.1081</td>\n",
       "      <td>32.7285</td>\n",
       "      <td>-85.1234</td>\n",
       "      <td>-85.5932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01021</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chilton</td>\n",
       "      <td>1794.49186</td>\n",
       "      <td>33.0719</td>\n",
       "      <td>32.6617</td>\n",
       "      <td>-86.375</td>\n",
       "      <td>-87.0192</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51021</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Bland</td>\n",
       "      <td>926.50775</td>\n",
       "      <td>37.2935</td>\n",
       "      <td>36.9524</td>\n",
       "      <td>-80.8546</td>\n",
       "      <td>-81.4622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51027</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>1302.15617</td>\n",
       "      <td>37.5378</td>\n",
       "      <td>37.0417</td>\n",
       "      <td>-81.7384</td>\n",
       "      <td>-82.3059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51037</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1230.95189</td>\n",
       "      <td>37.2488</td>\n",
       "      <td>36.6979</td>\n",
       "      <td>-78.4433</td>\n",
       "      <td>-78.9046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51041</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Chesterfield</td>\n",
       "      <td>1096.33923</td>\n",
       "      <td>37.5626</td>\n",
       "      <td>37.2227</td>\n",
       "      <td>-77.2442</td>\n",
       "      <td>-77.8551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3221 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state        county        area northmost southmost  \\\n",
       "geo_id                                                                   \n",
       "0500000US01001   Alabama       Autauga  1539.58924   32.7074   32.3408   \n",
       "0500000US01009   Alabama        Blount  1669.96984   34.2593   33.7653   \n",
       "0500000US01017   Alabama      Chambers  1545.01529   33.1081   32.7285   \n",
       "0500000US01021   Alabama       Chilton  1794.49186   33.0719   32.6617   \n",
       "...                  ...           ...         ...       ...       ...   \n",
       "0500000US51021  Virginia         Bland   926.50775   37.2935   36.9524   \n",
       "0500000US51027  Virginia      Buchanan  1302.15617   37.5378   37.0417   \n",
       "0500000US51037  Virginia     Charlotte  1230.95189   37.2488   36.6979   \n",
       "0500000US51041  Virginia  Chesterfield  1096.33923   37.5626   37.2227   \n",
       "\n",
       "               eastmost westmost  \n",
       "geo_id                            \n",
       "0500000US01001 -86.4112 -86.9176  \n",
       "0500000US01009 -86.3035 -86.9634  \n",
       "0500000US01017 -85.1234 -85.5932  \n",
       "0500000US01021  -86.375 -87.0192  \n",
       "...                 ...      ...  \n",
       "0500000US51021 -80.8546 -81.4622  \n",
       "0500000US51027 -81.7384 -82.3059  \n",
       "0500000US51037 -78.4433 -78.9046  \n",
       "0500000US51041 -77.2442 -77.8551  \n",
       "\n",
       "[3221 rows x 7 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_counties = county_summary(counties['features'])\n",
    "\n",
    "# Sanity checks (if no assertion violated, we are happy)\n",
    "test_counties(census_counties)\n",
    "census_counties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSON Schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Everything is Happy!\n"
     ]
    }
   ],
   "source": [
    "response = requests.get('https://geojson.org/schema/GeoJSON.json')\n",
    "geojson_schema = json.loads(response.text)\n",
    "\n",
    "if msg := not_valid(counties, geojson_schema):\n",
    "    print(msg)\n",
    "else:\n",
    "    print(\"Everything is Happy!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_schema = json.loads(\"\"\"\n",
    "{\n",
    "  \"$schema\": \"http://json-schema.org/draft-07/schema#\",\n",
    "  \"$id\": \"http://kdm.training/user-schema.json\",\n",
    "  \"title\": \"User\",\n",
    "  \"description\": \"A User of Our Computer System\",\n",
    "  \"type\" : \"object\",\n",
    "  \"required\": [\"name\", \"password\"],\n",
    "  \"properties\" : {\n",
    "     \"name\" : {\"type\" : \"string\"},\n",
    "     \"password\": {\n",
    "         \"description\": \"Use special characters and mixed case\",\n",
    "         \"type\": \"string\"},\n",
    "     \"lucky_numbers\": {\n",
    "         \"description\": \"Up to 6 favorite numbers 1-100\",\n",
    "         \"type\": \"array\",\n",
    "         \"items\": {\n",
    "           \"type\": \"number\",\n",
    "           \"minimum\": 1,\n",
    "           \"maximum\": 100\n",
    "         },\n",
    "         \"uniqueItems\": true,\n",
    "         \"minItems\": 0,\n",
    "         \"maxItems\": 6\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = json.loads(\"\"\"\n",
    "{\n",
    "  \"name\": \"David Mertz\",\n",
    "  \"password\": \"badpassword\",\n",
    "  \"details\": {\n",
    "    \"profession\": \"Data Scientist\",\n",
    "    \"employer\": \"KDM\"\n",
    "  },\n",
    "  \"lucky_numbers\": [12, 42, 55, 87]\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(david, user_schema):\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99 is not of type 'string'\n",
      "\n",
      "Failed validating 'type' in schema['properties']['name']:\n",
      "    {'type': 'string'}\n",
      "\n",
      "On instance['name']:\n",
      "    99\n"
     ]
    }
   ],
   "source": [
    "barbara_feldon = json.loads(\"\"\"\n",
    "{\n",
    "  \"name\": 99, \n",
    "  \"password\": \"1ibydieZ!S@8\"\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(barbara_feldon, user_schema):\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'name' is a required property\n",
      "\n",
      "Failed validating 'required' in schema:\n",
      "    {'$id': 'http://kdm.training/user-schema.json',\n",
      "     '$schema': 'http://json-schema.org/draft-07/schema#',\n",
      "     'description': 'A User of Our Computer System',\n",
      "     'properties': {'lucky_numbers': {'description': 'Up to 6 favorite '\n",
      "                                                     'numbers 1-100',\n",
      "                                      'items': {'maximum': 100,\n",
      "                                                'minimum': 1,\n",
      "                                                'type': 'number'},\n",
      "                                      'maxItems': 6,\n",
      "                                      'minItems': 0,\n",
      "                                      'type': 'array',\n",
      "                                      'uniqueItems': True},\n",
      "                    'name': {'type': 'string'},\n",
      "                    'password': {'description': 'Use special characters '\n",
      "                                                'and mixed case',\n",
      "                                 'type': 'string'}},\n",
      "     'required': ['name', 'password'],\n",
      "     'title': 'User',\n",
      "     'type': 'object'}\n",
      "\n",
      "On instance:\n",
      "    {'password': 'P4cC!^*8chWz8', 'profession': 'Hacker'}\n"
     ]
    }
   ],
   "source": [
    "intruder = json.loads(\"\"\"\n",
    "{\n",
    "  \"password\": \"P4cC!^*8chWz8\", \n",
    "  \"profession\": \"Hacker\"\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(intruder, user_schema):\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'one' is not of type 'number'\n",
      "\n",
      "Failed validating 'type' in schema['properties']['lucky_numbers']['items']:\n",
      "    {'maximum': 100, 'minimum': 1, 'type': 'number'}\n",
      "\n",
      "On instance['lucky_numbers'][0]:\n",
      "    'one' \n",
      "--------------------\n"
     ]
    }
   ],
   "source": [
    "the_count = json.loads(\"\"\"\n",
    "{\n",
    "  \"name\": \"Count von Count\",\n",
    "  \"password\": \"fourbananas\",\n",
    "  \"lucky_numbers\": [\"one\", \"two\", \"three\"]\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(the_count, user_schema):\n",
    "    print(msg, \"\\n--------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 7, 8] is too long\n",
      "\n",
      "Failed validating 'maxItems' in schema['properties']['lucky_numbers']:\n",
      "    {'description': 'Up to 6 favorite numbers 1-100',\n",
      "     'items': {'maximum': 100, 'minimum': 1, 'type': 'number'},\n",
      "     'maxItems': 6,\n",
      "     'minItems': 0,\n",
      "     'type': 'array',\n",
      "     'uniqueItems': True}\n",
      "\n",
      "On instance['lucky_numbers']:\n",
      "    [1, 2, 3, 4, 5, 6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "george = json.loads(\"\"\"\n",
    "{\n",
    "  \"name\": \"Georg Cantor\",\n",
    "  \"password\": \"omega_aleph\",\n",
    "  \"lucky_numbers\": [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(george, user_schema):\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 9, 9] has non-unique elements\n",
      "\n",
      "Failed validating 'uniqueItems' in schema['properties']['lucky_numbers']:\n",
      "    {'description': 'Up to 6 favorite numbers 1-100',\n",
      "     'items': {'maximum': 100, 'minimum': 1, 'type': 'number'},\n",
      "     'maxItems': 6,\n",
      "     'minItems': 0,\n",
      "     'type': 'array',\n",
      "     'uniqueItems': True}\n",
      "\n",
      "On instance['lucky_numbers']:\n",
      "    [9, 9, 9]\n"
     ]
    }
   ],
   "source": [
    "revolution_9 = json.loads(\"\"\"\n",
    "{\n",
    "  \"name\": \"Yoko Ono\",\n",
    "  \"password\": \"grapefruit\",\n",
    "  \"lucky_numbers\": [9, 9, 9]\n",
    "}\n",
    "\"\"\")\n",
    "\n",
    "if msg := not_valid(revolution_9, user_schema):\n",
    "    print(msg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Body (title): Data Scientist\n",
      "Attributes:   {'employer': 'KDM', 'duration': '26', 'units': 'months'}\n"
     ]
    }
   ],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "tree = ET.parse('data/users.xml')\n",
    "\n",
    "# Let us first find the attributes and text of a profession\n",
    "prof = tree.find('user').find('details').find('profession')\n",
    "print(\"Body (title):\", prof.text.strip())\n",
    "print(\"Attributes:  \", prof.attrib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[12, 42, 55, 87]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = tree.find('user').find('lucky-numbers').findall('item')\n",
    "lucky_numbers = [int(item.text) for item in items]\n",
    "lucky_numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keyhole Markup Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of placemarks: 3221\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Element '{http://www.opengis.net/kml/2.2}Placemark' at 0x7f2fdc184d10>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ns = {'kml': \"http://www.opengis.net/kml/2.2\"}\n",
    "document = ET.parse('data/gz_2010_us_050_00_20m.kml')\n",
    "\n",
    "root = document.getroot()\n",
    "kml_doc = root.find('kml:Document', ns)\n",
    "folder = kml_doc.find('kml:Folder', ns)\n",
    "\n",
    "# Make sure we have the same number of counties as with GeoJSON\n",
    "placemarks = folder.findall('kml:Placemark', ns)\n",
    "print(\"Count of placemarks:\", len(placemarks))\n",
    "# Show one Placemark element object\n",
    "placemarks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "County name: Autauga\n",
      "{'name': 'Name'} Autauga\n",
      "{'name': 'GEO_ID'} 0500000US01001\n",
      "{'name': 'STATE'} 01\n",
      "{'name': 'COUNTY'} 001\n",
      "{'name': 'LSAD'} County\n",
      "{'name': 'CENSUSAREA'} 594.436000000000035\n"
     ]
    }
   ],
   "source": [
    "# The name of the county is comparatively straightforward\n",
    "print(\"County name:\", placemarks[0].find('kml:name', ns).text)\n",
    "\n",
    "# Other county info is only distinguished by attribute\n",
    "sdata = (placemarks[0].find('kml:ExtendedData', ns)\n",
    "                      .find('kml:SchemaData', ns)\n",
    "                      .findall('kml:SimpleData', ns))\n",
    "\n",
    "# We are going to want GEO_ID, STATE and CENSUSAREA\n",
    "for record in sdata:\n",
    "    print(record.attrib, record.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('-86.497916734108713,32.346347937379285,123.940341341309249 '\n",
      " '-86.719045580223096,32.404719907202413,124.507383406162262 '\n",
      " '-86.816062031841554,32.342711234558017,124.433184524998069 '\n",
      " '-86.891734835750142,32.50487314981855,125.151479452848434 '\n",
      " '-86.918751525796665,32.666059588245083,125.785741473548114 '\n",
      " '-86.714541775531544,32.66362459160964,125.451970156282187 '\n",
      " '-86.715371359148733,32.707584324141543,125.614226697944105 '\n",
      " '-86.414261392701192,32.709278995622782,125.144079957157373 '\n",
      " '-86.41231357529395,32.411845326016262,124.046804890967906 '\n",
      " '-86.497916734108713,32.346347937379285,123.940341341309249')\n"
     ]
    }
   ],
   "source": [
    "coords = (placemarks[0].find('kml:Polygon', ns)\n",
    "                       .find('kml:outerBoundaryIs', ns)\n",
    "                       .find('kml:LinearRing', ns)\n",
    "                       .find('kml:coordinates', ns))\n",
    "pprint(coords.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32.70927899562278, 32.34271123455802, -86.41231357529395, -86.91875152579667)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def kml_extremes(coordinates):\n",
    "    \"Pass in a KML coordinates ElementTree object\"\n",
    "    text_points = coordinates.text.split()\n",
    "    points = [p.split(',') for p in text_points]\n",
    "    points = [[float(p[0]), float(p[1])] for p in points]\n",
    "    # We pass a list-of-list-of-lists here\n",
    "    return extremes([points])\n",
    "\n",
    "kml_extremes(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def kml_county_summary(placemarks, ns=ns):\n",
    "    geo_id = []\n",
    "    state, county_name, area = [], [], []\n",
    "    north, south, east, west = [], [], [], []\n",
    "\n",
    "    for placemark in placemarks:\n",
    "        # Get county name here and below to assure consistency\n",
    "        name = placemark.find('kml:name', ns).text\n",
    "        \n",
    "        # Other county info is distinguished by XML attribute\n",
    "        sdata = (placemark.find('kml:ExtendedData', ns)\n",
    "                          .find('kml:SchemaData', ns)\n",
    "                          .findall('kml:SimpleData', ns))\n",
    "        # We want Name, GEO_ID, STATE and CENSUSAREA\n",
    "        for record in sdata:\n",
    "            rectype = record.attrib['name']  # XML attrib\n",
    "            if rectype == 'Name':  # String 'Name' (county)\n",
    "                # If name is recorded differently, problem!\n",
    "                assert record.text == name\n",
    "                county_name.append(name)\n",
    "            elif rectype == 'GEO_ID':\n",
    "                geo_id.append(record.text)\n",
    "            elif rectype == 'CENSUSAREA':\n",
    "                # Convert to km^2 from mi^2\n",
    "                area.append(float(record.text) * 2.59)\n",
    "            elif rectype == 'STATE':\n",
    "                # District of Columbia is not a US state\n",
    "                state_name = fips_map.get(int(record.text), None)\n",
    "                state.append(state_name)\n",
    "\n",
    "        # We are going to \"cheat\" here a little bit.  \n",
    "        # Sometimes a placemark has a top level <MultiGeometry>\n",
    "        # with several Polygons; we will skip that calculation \n",
    "        try:\n",
    "            coordinates = (placemark\n",
    "                    .find('kml:Polygon', ns)\n",
    "                    .find('kml:outerBoundaryIs', ns)\n",
    "                    .find('kml:LinearRing', ns)\n",
    "                    .find('kml:coordinates', ns))                \n",
    "            n, s, e, w = kml_extremes(coordinates)\n",
    "        except AttributeError:\n",
    "            n, s, e, w = None, None, None, None\n",
    "            \n",
    "        north.append(n); south.append(s); \n",
    "        east.append(e); west.append(w)\n",
    "\n",
    "    df = pd.DataFrame({\n",
    "            'geo_id': geo_id, 'state': state, \n",
    "            'county': county_name, 'area': area,\n",
    "            'northmost': north, 'southmost': south,\n",
    "            'eastmost': east, 'westmost': west\n",
    "        })\n",
    "    return df.set_index('geo_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>area</th>\n",
       "      <th>northmost</th>\n",
       "      <th>southmost</th>\n",
       "      <th>eastmost</th>\n",
       "      <th>westmost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>geo_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0500000US01001</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>1539.58924</td>\n",
       "      <td>32.709279</td>\n",
       "      <td>32.342711</td>\n",
       "      <td>-86.412314</td>\n",
       "      <td>-86.918752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01009</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Blount</td>\n",
       "      <td>1669.96984</td>\n",
       "      <td>34.261131</td>\n",
       "      <td>33.767154</td>\n",
       "      <td>-86.304677</td>\n",
       "      <td>-86.964531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01017</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chambers</td>\n",
       "      <td>1545.01529</td>\n",
       "      <td>33.109960</td>\n",
       "      <td>32.730429</td>\n",
       "      <td>-85.124537</td>\n",
       "      <td>-85.594308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US01021</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Chilton</td>\n",
       "      <td>1794.49186</td>\n",
       "      <td>33.073731</td>\n",
       "      <td>32.663625</td>\n",
       "      <td>-86.376119</td>\n",
       "      <td>-87.020318</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51021</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Bland</td>\n",
       "      <td>926.50775</td>\n",
       "      <td>37.295189</td>\n",
       "      <td>36.954152</td>\n",
       "      <td>-80.855694</td>\n",
       "      <td>-81.463294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51027</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>1302.15617</td>\n",
       "      <td>37.539502</td>\n",
       "      <td>37.043415</td>\n",
       "      <td>-81.739470</td>\n",
       "      <td>-82.306981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51037</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Charlotte</td>\n",
       "      <td>1230.95189</td>\n",
       "      <td>37.250505</td>\n",
       "      <td>36.699679</td>\n",
       "      <td>-78.444320</td>\n",
       "      <td>-78.905600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0500000US51041</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Chesterfield</td>\n",
       "      <td>1096.33923</td>\n",
       "      <td>37.564372</td>\n",
       "      <td>37.224467</td>\n",
       "      <td>-77.245139</td>\n",
       "      <td>-77.856138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3221 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   state        county        area  northmost  southmost  \\\n",
       "geo_id                                                                     \n",
       "0500000US01001   Alabama       Autauga  1539.58924  32.709279  32.342711   \n",
       "0500000US01009   Alabama        Blount  1669.96984  34.261131  33.767154   \n",
       "0500000US01017   Alabama      Chambers  1545.01529  33.109960  32.730429   \n",
       "0500000US01021   Alabama       Chilton  1794.49186  33.073731  32.663625   \n",
       "...                  ...           ...         ...        ...        ...   \n",
       "0500000US51021  Virginia         Bland   926.50775  37.295189  36.954152   \n",
       "0500000US51027  Virginia      Buchanan  1302.15617  37.539502  37.043415   \n",
       "0500000US51037  Virginia     Charlotte  1230.95189  37.250505  36.699679   \n",
       "0500000US51041  Virginia  Chesterfield  1096.33923  37.564372  37.224467   \n",
       "\n",
       "                 eastmost   westmost  \n",
       "geo_id                                \n",
       "0500000US01001 -86.412314 -86.918752  \n",
       "0500000US01009 -86.304677 -86.964531  \n",
       "0500000US01017 -85.124537 -85.594308  \n",
       "0500000US01021 -86.376119 -87.020318  \n",
       "...                   ...        ...  \n",
       "0500000US51021 -80.855694 -81.463294  \n",
       "0500000US51027 -81.739470 -82.306981  \n",
       "0500000US51037 -78.444320 -78.905600  \n",
       "0500000US51041 -77.245139 -77.856138  \n",
       "\n",
       "[3221 rows x 7 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kml_counties = kml_county_summary(placemarks)\n",
    "kml_counties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### INI and Flat Custom Formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sections:    ['owner', 'database']\n",
      "Owner keys:  ['name', 'organization']\n",
      "Owner/name:  John Doe\n",
      "Port #:      143\n"
     ]
    }
   ],
   "source": [
    "import configparser\n",
    "cfg = configparser.ConfigParser()\n",
    "cfg.read('data/example.ini')\n",
    "\n",
    "print(\"Sections:   \", cfg.sections())\n",
    "print(\"Owner keys: \", [k for k in cfg['owner']])\n",
    "print(\"Owner/name: \", cfg['owner']['name'])\n",
    "print(\"Port #:     \", cfg['database'].getint('port'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TOML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'title': 'TOML Example',\n",
       " 'owner': {'name': 'Tom Preston-Werner',\n",
       "  'dob': datetime.datetime(1979, 5, 27, 7, 32, tzinfo=<toml.tz.TomlTz object at 0x7f2fdc171a30>)},\n",
       " 'database': {'server': '192.168.1.1',\n",
       "  'ports': [8001, 8001, 8002],\n",
       "  'connection_max': 5000,\n",
       "  'enabled': True},\n",
       " 'servers': {'alpha': {'ip': '10.0.0.1', 'dc': 'eqdc10'},\n",
       "  'beta': {'ip': '10.0.0.2', 'dc': 'eqdc10'}},\n",
       " 'clients': {'data': [['gamma', 'delta'], [1, 2]],\n",
       "  'hosts': ['alpha', 'omega']}}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import toml\n",
    "toml.load(open('data/example.toml'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TomlDecodeError\n",
      "invalid literal for int() with base 0: '2] []\n",
      "hosts = [   \"alpha\"' (line 27 column 1 char 433)\n"
     ]
    }
   ],
   "source": [
    "with open('data/example-bad.toml') as fh:\n",
    "    try:\n",
    "        cfg = toml.load(fh)\n",
    "    except Exception as err:\n",
    "        print_err(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    26\t[clients]\n",
      "    27\tdata = [ [\"gamma\", \"delta\"], [1, 2] []\n",
      "    28\t\n",
      "    29\t# Line breaks are OK when inside arrays\n",
      "    30\thosts = [\n",
      "    31\t  \"alpha\",\n",
      "    32\t  \"omega\"\n",
      "    33\t]"
     ]
    }
   ],
   "source": [
    "!cat -n data/example-bad.toml | tail -8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of 5\n",
      " $ clients :List of 2\n",
      "  ..$ data :List of 2\n",
      "  .. ..$ : chr [1:2] \"gamma\" \"delta\"\n",
      "  .. ..$ : int [1:2] 1 2\n",
      "  ..$ hosts: chr [1:2] \"alpha\" \"omega\"\n",
      " $ database:List of 4\n",
      "  ..$ connection_max: int 5000\n",
      "  ..$ enabled       : logi TRUE\n",
      "  ..$ ports         : int [1:3] 8001 8001 8002\n",
      "  ..$ server        : chr \"192.168.1.1\"\n",
      " $ owner   :List of 2\n",
      "  ..$ dob : POSIXct[1:1], format: \"1979-05-27 15:32:00\"\n",
      "  ..$ name: chr \"Tom Preston-Werner\"\n",
      " $ servers :List of 2\n",
      "  ..$ alpha:List of 2\n",
      "  .. ..$ dc: chr \"eqdc10\"\n",
      "  .. ..$ ip: chr \"10.0.0.1\"\n",
      "  ..$ beta :List of 2\n",
      "  .. ..$ dc: chr \"eqdc10\"\n",
      "  .. ..$ ip: chr \"10.0.0.2\"\n",
      " $ title   : chr \"TOML Example\"\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(RcppTOML)\n",
    "parseTOML(\"data/example.toml\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Yet Another Markup Language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'invoice': 34843,\n",
       " 'date': datetime.date(2001, 1, 23),\n",
       " 'bill-to': {'given': 'Chris',\n",
       "  'family': 'Dumars',\n",
       "  'address': {'lines': '458 Walkman Dr.\\nSuite #292\\n',\n",
       "   'city': 'Royal Oak',\n",
       "   'state': 'MI',\n",
       "   'postal': 48046}},\n",
       " 'ship-to': {'given': 'Chris',\n",
       "  'family': 'Dumars',\n",
       "  'address': {'lines': '458 Walkman Dr.\\nSuite #292\\n',\n",
       "   'city': 'Royal Oak',\n",
       "   'state': 'MI',\n",
       "   'postal': 48046}},\n",
       " 'product': [{'sku': 'BL394D',\n",
       "   'quantity': 4,\n",
       "   'description': 'Basketball',\n",
       "   'price': 450.0},\n",
       "  {'sku': 'BL4438H',\n",
       "   'quantity': 1,\n",
       "   'description': 'Super Hoop',\n",
       "   'price': 2392.0}],\n",
       " 'tax': 251.42,\n",
       " 'total': 4443.52,\n",
       " 'comments': 'Late afternoon is best. Backup contact is Nancy Billsmer @ 338-4338.'}"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import yaml\n",
    "order = yaml.load(open('data/example.yaml'))\n",
    "order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Is nested dict same object under different keys?\n",
    "order['ship-to'] is order['bill-to']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object load_all at 0x7f2fcf760820> \n",
      "\n",
      "{'name': 'David', 'age': 55}\n",
      "{'name': 'Mei', 'age': 50}\n",
      "{'name': 'Juana', 'age': 47}\n",
      "{'name': 'Adebayo', 'age': 58}\n"
     ]
    }
   ],
   "source": [
    "with open('data/multidoc.yaml') as stream:\n",
    "    docs = yaml.load_all(stream)\n",
    "    print(docs, '\\n')\n",
    "    for doc in docs:\n",
    "        print(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScannerError\n",
      "mapping values are not allowed here   in \"data/example-bad.yaml\", line\n",
      "17, column 31\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    yaml.load(open('data/example-bad.yaml'))\n",
    "except Exception as err:\n",
    "    print_err(err)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    15\t    - sku         : BL394D\n",
      "    16\t      quantity    : 4\n",
      "    17\t      description : Basketball: ERROR\n",
      "    18\t      price       : 450.00\n",
      "    19\t    - sku         : BL4438H\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "cat -n data/example-bad.yaml | sed '15,19p;d' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'David', 'age': 55}\n",
      "{'name': 'Mei', 'age': 50}\n",
      "ScannerError\n",
      "mapping values are not allowed here   in \"data/multidoc-bad.yaml\",\n",
      "line 10, column 12\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    for doc in yaml.load_all(open('data/multidoc-bad.yaml')):\n",
    "        print(doc)\n",
    "except Exception as err:\n",
    "    print_err(err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NoSQL Databases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assume that MongoDB is running on local system\n",
    "from pymongo import MongoClient\n",
    "client = MongoClient('mongodb://localhost:27017')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['admin', 'business', 'config', 'local']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What databases exist on the local server?\n",
    "client.database_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Restaurants: 50\n",
      "{'_id': ObjectId('5f30928db504836031a2c2a1'),\n",
      " 'cuisine': 'Mexican',\n",
      " 'name': 'Kitchen Tasty Inc.',\n",
      " 'phone': '+1 524 555 9265'}\n",
      "{'_id': ObjectId('5f30928db504836031a2c2a2'),\n",
      " 'cuisine': 'Sandwich',\n",
      " 'name': 'Sweet Salty Take-Out',\n",
      " 'phone': '+1 408 555 6924'}\n",
      "{'_id': ObjectId('5f30928db504836031a2c2a3'),\n",
      " 'cuisine': 'Vegetarian',\n",
      " 'name': 'City Kitchen Inc.',\n",
      " 'phone': '+1 528 555 8923'}\n"
     ]
    }
   ],
   "source": [
    "db_biz = client.business\n",
    "print(\"Restaurants:\", db_biz.info.count())\n",
    "for biz in db_biz.info.find(limit=3):\n",
    "    pprint(biz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reviews: 5000\n",
      "{'_id': ObjectId('5f30928db504836031a2c2d3'),\n",
      " 'name': 'Tasty Sweet Inc.',\n",
      " 'price': 'cheap',\n",
      " 'rating': 1}\n",
      "{'_id': ObjectId('5f30928db504836031a2c2d4'),\n",
      " 'name': 'Big Big Restaurant',\n",
      " 'price': 'cheap',\n",
      " 'rating': 6}\n",
      "{'_id': ObjectId('5f30928db504836031a2c2d5'),\n",
      " 'name': 'Goat Big Take-Out',\n",
      " 'price': 'reasonable',\n",
      " 'rating': 8}\n"
     ]
    }
   ],
   "source": [
    "print(\"Reviews:\", db_biz.reviews.count())\n",
    "for review in db_biz.reviews.find(limit=3):\n",
    "    pprint(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'_id': ObjectId('5f30928db504836031a2c2ea'),\n",
      " 'name': 'City Kitchen Inc.',\n",
      " 'price': 'cheap',\n",
      " 'rating': 3}\n",
      "{'_id': ObjectId('5f30928db504836031a2c435'),\n",
      " 'name': 'City Kitchen Inc.',\n",
      " 'price': 'cheap',\n",
      " 'rating': 7}\n",
      "{'_id': ObjectId('5f30928db504836031a2c553'),\n",
      " 'name': 'City Kitchen Inc.',\n",
      " 'price': 'cheap',\n",
      " 'rating': 3}\n",
      "{'_id': ObjectId('5f30928db504836031a2c5d6'),\n",
      " 'name': 'City Kitchen Inc.',\n",
      " 'price': 'cheap',\n",
      " 'rating': 1}\n"
     ]
    }
   ],
   "source": [
    "query = {'price': 'cheap', 'name': 'City Kitchen Inc.'}\n",
    "for review in db_biz.reviews.find(query, limit=4):\n",
    "    pprint(review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Missing Fields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratings = []\n",
    "query = {'name': 'City Kitchen Inc.'}\n",
    "for review in db_biz.reviews.find(query):\n",
    "    try:\n",
    "        ratings.append(review['rating'])\n",
    "    except KeyError:\n",
    "        pass\n",
    "\n",
    "n = len(ratings)\n",
    "pd.Series(ratings).plot(kind=\"hist\", title=f\"{n} ratings\")\n",
    "plt.savefig(f\"img/(Ch02){n} ratings.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "110"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_biz.reviews.find({'name': 'City Kitchen Inc.'}).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'_id': ObjectId('5f30928db504836031a2c3fa'),\n",
       "  'name': 'City Kitchen Inc.',\n",
       "  'price': 'expensive'},\n",
       " {'_id': ObjectId('5f30928db504836031a2c6b6'),\n",
       "  'name': 'City Kitchen Inc.',\n",
       "  'price': 'reasonable'}]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(db_biz.reviews.find({'name': 'City Kitchen Inc.', 'rating': None}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Denormalization and Its Discontents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def has_best_review(name, db=db_biz):\n",
    "    \"Return phone if restaurant has at least one 10 rating\"\n",
    "    query = {'name': name, 'rating': 10}\n",
    "    review = None\n",
    "    \n",
    "    # Fast path has phone in local results\n",
    "    for review in db.reviews.find(query):\n",
    "        phone = review.get('phone')\n",
    "        if phone:\n",
    "            return f\"Call {name} at {phone}! (FAST query)\"\n",
    "\n",
    "    # If there were no ratings of 10, we don't like it!\n",
    "    if not review:\n",
    "        return f\"Do not bother with {name}!\"\n",
    "\n",
    "    # MUCH SLOWER path is second query\n",
    "    info = db.info.find_one({'name': name})\n",
    "    return f\"Call {name} at {info['phone']}! (SLOW query)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Call Salty Big Take-Out at +1 354 555 8317! (FAST query)'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_best_review('Salty Big Take-Out')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Call City Kitchen Inc. at +1 528 555 8923! (SLOW query)'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_best_review('City Kitchen Inc.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Do not bother with Out of Business!'"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "has_best_review('Out of Business')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': ObjectId('5f30928db504836031a2c7c9'),\n",
       " 'name': 'Salty Big Take-Out',\n",
       " 'price': 'reasonable',\n",
       " 'rating': 10,\n",
       " 'phone': '+1 354 555 8317'}"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = {'name': 'Salty Big Take-Out', \n",
    "         'rating': 10, 'phone':{\"$ne\":None}}\n",
    "\n",
    "db_biz.reviews.find_one(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': ObjectId('5f30928db504836031a2c2aa'),\n",
       " 'name': 'Salty Big Take-Out',\n",
       " 'cuisine': 'Mexican',\n",
       " 'phone': '+1 967 555 5487'}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db_biz.info.find_one({'name': 'Salty Big Take-Out'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Key/Value Stores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b'Big Sweet Take-Out::info::phone',\n",
       " b'Big Sweet Inc.::ratings',\n",
       " b'Goat Sweet Inc.::info::phone',\n",
       " b'Fish City Restaurant//ratings',\n",
       " b'Delight Goat Inc.::ratings',\n",
       " b'DESCRIPTION',\n",
       " b'Salty Delight Take-Out::ratings',\n",
       " b'Sweet Tasty Restaurant::info::phone',\n",
       " b'Delight Salty Restaurant::info::phone',\n",
       " b'Tasty Fish Inc.::info::cuisine']"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "biz = dbm.open('data/keyval.db')\n",
    "seed(6)\n",
    "# Keys are bytes; could convert to strings if desired\n",
    "sample(list(biz.keys()), 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overview: b'Restaurant information'\n",
      "Cuisine:  b'Mexican'\n",
      "Ratings:  b'2;1;1;10;5;7;1;4;8;10;7;7;6;8;' ...\n"
     ]
    }
   ],
   "source": [
    "name = b\"Tasty Fish Inc.\"\n",
    "print(\"Overview:\", biz[b\"DESCRIPTION\"])\n",
    "print(\"Cuisine: \", biz[name + b\"::info::cuisine\"] )\n",
    "print(\"Ratings: \", biz[name + b\"::ratings\"][:30], \"...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tasty Fish Inc.::ratings \t 2;1;1;10;5;7;1;4;8;10;7;7;6;8;\n",
      "Tasty Fish Inc.::info::phone \t +1 935 555 8029\n",
      "Tasty Fish Inc.::info::cuisine \t Mexican\n"
     ]
    }
   ],
   "source": [
    "for key, val in biz.items():\n",
    "    if key.startswith(b'Tasty Fish Inc.::'):\n",
    "        print(key.decode(), '\\t', val[:30].decode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Fish City Restaurant::ratings' b'6;10;4;3;10;5;1;4;7;8;5;2;1;5;'\n",
      "b'Fish City Restaurant//ratings' b'9'\n",
      "b'Fish City Restaurant::info::phone' b'+1 851 555 1082'\n",
      "b'Fish City Restaurant::info::cuisine' b'American'\n"
     ]
    }
   ],
   "source": [
    "for key, val in biz.items():\n",
    "    if key.startswith(b'Fish City Restaurant'):\n",
    "        print(key, val[:30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a Relational Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dbm.dumb'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbm.whichdb('data/keyval.db')"
   ]
  }
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