05_cloud_pipelines/docs/gans_cities_scraping_and_api.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WSQ5Fev3rZ2M"
   },
   "source": [
    "# Acquiring data on cities\n",
    "\n",
    "This notebook acquires data about cities that GANS operates in from Wikipedia (via web scraping) and OpenWeatherMap (via API access)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ktDmVq208N_-"
   },
   "source": [
    "## Import libraries 💾"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#! pip install lat_lon_parser\n",
    "#! pip install geopy\n",
    "#!pip install timezonefinder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "wL77EPUT7tc6"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from datetime import datetime, timedelta\n",
    "import urllib\n",
    "import json\n",
    "import lat_lon_parser\n",
    "import re\n",
    "import email.utils\n",
    "from geopy import distance\n",
    "from pytz import timezone, utc\n",
    "from timezonefinder import TimezoneFinder\n",
    "import dotenv\n",
    "import os\n",
    "from sqlalchemy import create_engine, text, inspect, engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Credentials\n",
    "For storing your MySQL parameters (including password) and your OpenWeatherMap API key, make a `.env` file in the directory holding this notebook with the following content:\n",
    "```env\n",
    "# parameters for MySQL\n",
    "sql_connection='\n",
    "{                                                                  \n",
    "  \"drivername\": \"mysql+pymysql\",                                   \n",
    "  \"username\": \"username\",\n",
    "  \"password\": \"password\",\n",
    "  \"host\": \"host\",\n",
    "  \"port\": \"3306\",\n",
    "  \"database\": \"wbscs_cities\"\n",
    "}\n",
    "'\n",
    "# API key for OpenWeatherMap\n",
    "openweathermap_key=\"key\"\n",
    "# API key for AeroDataBox\n",
    "aerodatabox_key=\"key\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQL schema"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Record url, dt and ID for any scraped resources\n",
    "scrape_list: list[tuple[str, datetime, int]] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to mysql+pymysql://root:***@35.195.209.223:3306/wbscs_cities\n"
     ]
    }
   ],
   "source": [
    "def connect_sql():\n",
    "  \"\"\"\n",
    "  Open connection to SQL database\n",
    "\n",
    "  This reads connection parameters and credentials from `.env`.\n",
    "\n",
    "  :return: SQLAlchemy connection object\n",
    "  \"\"\"\n",
    "  dotenv.load_dotenv()\n",
    "  \n",
    "  url = engine.url.URL.create(**json.loads(os.getenv('sql_connection')))\n",
    "  print(f'Connecting to {repr(url)}')\n",
    "  return create_engine(url).connect()\n",
    "\n",
    "connection = connect_sql()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def reset_database():\n",
    "    \"\"\"\n",
    "    Drop all SQL tables in the cities DB.\n",
    "    \"\"\"\n",
    "    connection.execute(text('DROP TABLE IF EXISTS flight;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS city_airport;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS airport;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS weather;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS fact;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS city;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS measure;'))\n",
    "    connection.execute(text('DROP TABLE IF EXISTS scrape;'))\n",
    "    # consequently reset scrape database IDs\n",
    "    for scrape in scrape_list:\n",
    "      scrape[2] = None\n",
    "\n",
    "def init_database():\n",
    "    \"\"\"\n",
    "    Initialize SQL table schema if not previously done.\n",
    "\n",
    "    This will not do anything if any tables already exist.\n",
    "    \"\"\"\n",
    "    if len(inspect(connection).get_table_names()):\n",
    "        return\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE city (\n",
    "            id INT AUTO_INCREMENT KEY,\n",
    "            name TEXT NOT NULL,\n",
    "            country TEXT,\n",
    "            latitude DOUBLE,\n",
    "            longitude DOUBLE,\n",
    "            base_elevation TEXT,\n",
    "            peak_elevation TEXT\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE scrape (\n",
    "            id INT AUTO_INCREMENT KEY,\n",
    "            url TEXT NOT NULL,\n",
    "            timestamp TIMESTAMP NOT NULL\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE measure (\n",
    "            id INT AUTO_INCREMENT KEY,\n",
    "            name TEXT NOT NULL,\n",
    "            type TEXT NOT NULL\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE fact (\n",
    "            scrape INT NOT NULL REFERENCES scrape(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            city INT NOT NULL REFERENCES city(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            measure INT NOT NULL REFERENCES measure(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            value TEXT NOT NULL,\n",
    "            meta JSON NULL DEFAULT NULL\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE weather (\n",
    "            scrape INT NOT NULL REFERENCES scrape(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            city INT NOT NULL REFERENCES city(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            dt DATETIME NOT NULL,\n",
    "            T_celsius FLOAT NOT NULL,\n",
    "            T_feelslike_celsius FLOAT NOT NULL,\n",
    "            wind_speed_mps FLOAT NOT NULL,\n",
    "            wind_gust_mps FLOAT NOT NULL,\n",
    "            rain_3h_mm FLOAT NOT NULL,\n",
    "            snow_3h_mm FLOAT NOT NULL\n",
    "        );\n",
    "        '''));\n",
    "    \n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE airport (\n",
    "            icao CHAR(4) KEY,\n",
    "            scrape INT NOT NULL REFERENCES scrape(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            iata TEXT NOT NULL,\n",
    "            name TEXT NOT NULL,\n",
    "            latitude DOUBLE NOT NULL,\n",
    "            longitude DOUBLE NOT NULL\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE city_airport (\n",
    "            scrape INT NOT NULL REFERENCES scrape(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            city INT NOT NULL REFERENCES city(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            icao CHAR(4) NOT NULL REFERENCES airport(icao) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            distance_km DOUBLE NOT NULL\n",
    "        );\n",
    "        '''));\n",
    "\n",
    "    connection.execute(text(\n",
    "        '''\n",
    "        CREATE TABLE flight (\n",
    "            scrape INT NOT NULL REFERENCES scrape(id) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            origin_icao CHAR(4) NOT NULL REFERENCES airport(icao) ON DELETE CASCADE ON UPDATE CASCADE,\n",
    "            scheduled_time TIMESTAMP NOT NULL,\n",
    "            destination_icao CHAR(4) NULL,\n",
    "            destination_name TEXT NOT NULL,\n",
    "            number TEXT NOT NULL,\n",
    "            type ENUM('departure', 'arrival') NOT NULL\n",
    "        );\n",
    "        '''));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_scrape_id(index: int):\n",
    "  \"\"\"\n",
    "  Get or make scrape ID for `scrape` table.\n",
    "  \n",
    "  :param index: index into the `scrape_list`\n",
    "  :return: the new scrape ID\n",
    "  \"\"\"\n",
    "  scrape = scrape_list[index]\n",
    "  if (id := scrape[2]) != None:\n",
    "    return id\n",
    "  redacted_url=re.sub(r'appid=\\w+\\b', 'appid=[redacted]', scrape[0])\n",
    "  # MariaDB: id = connection.execute(text(f'insert into scrape(url, timestamp) values(\"{redacted_url}\", FROM_UNIXTIME({scrape[1].timestamp()})) returning id')).first().id\n",
    "  id = connection.execute(text(f'insert into scrape(url, timestamp) values(\"{redacted_url}\", FROM_UNIXTIME({scrape[1].timestamp()}))')).lastrowid\n",
    "  scrape[2] = id\n",
    "  return id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_scrapes():\n",
    "  \"\"\"\n",
    "  Read list of scrapes from DB\n",
    "  \n",
    "  :return: data-frame with scrape meta-data\n",
    "  \"\"\"\n",
    "  return pd.read_sql('scrape', con=connection).set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_or_create_measure_id(measure: str, type_: str):\n",
    "  \"\"\"\n",
    "  Get or create type of measurement in the `measure` table and return ID.\n",
    "\n",
    "  :param measure: name of the measure\n",
    "  :param type_: type of the measure\n",
    "    The `type` is used as an annotation on how to interpret the `value`\n",
    "    stored in the `fact` table which is physically always stored as a string.\n",
    "  :return: the measure ID\n",
    "  \"\"\"\n",
    "  try:\n",
    "    return connection.execute(text(f'select id from measure where name=\"{measure}\" and type=\"{type_}\"')).first().id\n",
    "  except AttributeError:\n",
    "    # MariaDB: return connection.execute(text(f'insert into measure(name, type) values(\"{measure}\", \"{type_}\") returning id')).first().id\n",
    "    return connection.execute(text(f'insert into measure(name, type) values(\"{measure}\", \"{type_}\")')).lastrowid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_measures():\n",
    "  \"\"\"\n",
    "  Read list of measures from DB\n",
    "  \n",
    "  :return: data-frame\n",
    "  \"\"\"\n",
    "  return pd.read_sql('measure', con=connection).set_index('id')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Web scraping infrastructure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_number(number: str):\n",
    "  \"\"\"\n",
    "  Remove thousands separator (',') and parse the first number (integer only)\n",
    "\n",
    "  :param number: the string to parse as a number\n",
    "  :return: the parsed number\n",
    "    or None if no number was found\n",
    "  \"\"\"\n",
    "  if match := re.search(r'(\\d+)', number.replace(',', '')):\n",
    "    return int(match.group(1))\n",
    "  return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_date(date: str):\n",
    "  \"\"\"\n",
    "  Search for and parse any of the following date-formats in parentheses:\n",
    "  - (31 December 2018)\n",
    "  - (2018-12-31)\n",
    "  - (2022)\n",
    "  Trailing other text is allowed inside the parentheses if separated by whitespace.\n",
    "\n",
    "  :param date: string containing the date\n",
    "  :return: date as string in format `yyyy-mm-dd` or `yyyy`\n",
    "    or None if not found.\n",
    "  \"\"\"\n",
    "  # 31 December 2018, see Zürich\n",
    "  if match := re.search(r'\\((\\d+ \\w+ \\d{4})(\\s.*)?\\)', date):\n",
    "    return datetime.strptime(match.group(1), '%d %B %Y').strftime('%Y-%m-%d')\n",
    "  # 2018-12-31, see Hamburg\n",
    "  if match := re.search(r'\\((\\d{4}-\\d\\d-\\d\\d)(\\s.*)?\\)', date):\n",
    "    return match.group(1)\n",
    "  # 2022, see most others\n",
    "  if match := re.search(r'\\((20\\d\\d)(\\s.*)?\\)', date):\n",
    "    return match.group(1)\n",
    "  return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_soup(url: str):\n",
    "  \"\"\"\n",
    "  Retrieve HTML soup from URL using a local soup cache.\n",
    "\n",
    "  If the URL was previously retrieved, the cached soup is served instead of making a web request.\n",
    "  If the URL is requested from the web, the soup is stored in the cache.\n",
    "\n",
    "  :param url: the URL to retrieve from cache or web\n",
    "  :return: (soup, scrape_index) where scrape_index refers to the scrape_list\n",
    "  \"\"\"\n",
    "  try:\n",
    "    if cached := get_soup.cache.get(url):\n",
    "      return cached\n",
    "  except:\n",
    "    get_soup.cache = {}\n",
    "    \n",
    "  dt = datetime.now()\n",
    "  response = requests.get(url)\n",
    "  soup = BeautifulSoup(response.content, 'html.parser')\n",
    "  # dt = email.utils.parsedate_to_datetime(response.headers['date'])\n",
    "  scrape_index = len(scrape_list)\n",
    "  scrape_list.append([url, dt, None])\n",
    "  get_soup.cache[url] = (soup, scrape_index)\n",
    "  return (soup, scrape_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U4QQbALMuZ99"
   },
   "source": [
    "## Web scraping Wikipedia\n",
    "\n",
    "Utilise your web scraping skills to gather information about three German cities – Berlin, Hamburg, and Munich – from Wikipedia. You will start by extracting the population of each city and then expand the scope of your data gathering to include latitude and longitude, country, and possibly other relevant details.\n",
    "\n",
    "1. Population Scraping\n",
    "\n",
    "  1.1. Begin by scraping the population of each city from their respective Wikipedia pages:\n",
    "\n",
    " - Berlin: https://en.wikipedia.org/wiki/Berlin\n",
    " - Hamburg: https://en.wikipedia.org/wiki/Hamburg\n",
    " - Munich: https://en.wikipedia.org/wiki/Munich\n",
    "\n",
    "  1.2. Once you have scrapped the population of each city, reflect on the similarities and patterns in accessing the population data across the three pages. Also, analyse the URLs to identify any commonalities. Make a loop that executes once but simultaneously retrieves the population for all three cities.\n",
    "\n",
    "2. Data Organisation\n",
    "\n",
    "  Utilise pandas DataFrame to effectively store the extracted population data. Ensure the data is clean and properly formatted. Remove any unnecessary characters or symbols and ensure the column data types are accurate.\n",
    "\n",
    "3. Further Enhancement\n",
    "\n",
    "  3.1. Expand the scope of your data gathering by extracting other relevant information for each city:\n",
    "\n",
    " - Latitude and longitude\n",
    " - Country of location\n",
    "\n",
    "  3.2. Create a function from the loop and DataFrame to encapsulate the scraping process. This function can be used repeatedly to fetch updated data whenever necessary. It should return a clean, properly formatted DataFrame.\n",
    "\n",
    "4. Global Data Scraping\n",
    "\n",
    "  With your robust scraping skills now honed, venture beyond the confines of Germany and explore other cities around the world. While the extraction methodology for German cities may follow a consistent pattern, this may not be the case for cities from different countries. Can you make a function that returns a clean DataFrame of information for cities worldwide?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def scrape_city(city: str):\n",
    "  \"\"\"\n",
    "  Extract phase: Scrape city data from Wikipedia\n",
    "\n",
    "  Currently scrapes:\n",
    "  - country\n",
    "  - latitude, longitude\n",
    "  - base_elevation, peak_elevation\n",
    "  - population, date (of population count if available)\n",
    "\n",
    "  Uses get_soup() for caching and parse_number(), parse_date().\n",
    "\n",
    "  :param city: the city to retrieve data for,\n",
    "    must be a valid en.wikipedia.org page name\n",
    "  :return: pandas data-frame with the retrieved information\n",
    "  \"\"\"\n",
    "  url_pattern = 'https://en.wikipedia.org/wiki/{city}'\n",
    "  wikidata_pattern = 'https://www.wikidata.org/wiki/Special:EntityData/{entity}.json'\n",
    "\n",
    "  city_soup, scrape_index = get_soup(url_pattern.format(city=urllib.parse.quote(city)))\n",
    "\n",
    "  for ib in city_soup.select('.infobox'): # .ib-settlement would be nicer but Sydney uses infobox only\n",
    "    latitude = ib.select_one('.latitude').text\n",
    "    longitude = ib.select_one('.longitude').text\n",
    "    country = None\n",
    "    date = None\n",
    "    population = None\n",
    "\n",
    "    for th in ib.select('.infobox-label'):\n",
    "      if th.text == 'Country':\n",
    "        country = th.parent.select_one('.infobox-data').text\n",
    "      elif th.text.startswith('Population'):\n",
    "        date = parse_date(th.text)\n",
    "        infobox_data = th.parent.select_one('.infobox-data')\n",
    "        population = parse_number(infobox_data.text)\n",
    "        if not date:\n",
    "          date = parse_date(infobox_data.text)\n",
    "\n",
    "    for th in ib.select('.infobox-header'):\n",
    "      if population == None and th.text.startswith('Population'):\n",
    "        date = parse_date(th.text)\n",
    "\n",
    "        if data := th.parent.select_one('.infobox-data'):\n",
    "          population = parse_number(data.text)\n",
    "          continue\n",
    "\n",
    "        next_tr = th.parent.next_sibling\n",
    "        if not re.match(r'\\s*•\\s+(City|Capital city|Total|Municipality)', next_tr.select_one('.infobox-label').text):\n",
    "          continue\n",
    "\n",
    "        population = parse_number(next_tr.select_one('.infobox-data').text)\n",
    "    break\n",
    "\n",
    "  # WikiData\n",
    "  # wikidata_entity = city_soup.select_one('#t-wikibase a')['href'].rsplit('/', 1)[-1]\n",
    "  # wikidata_url = wikidata_pattern.format(entity=wikidata_entity)\n",
    "  # print(wikidata_url)\n",
    "  # wikidata_json = json.loads(requests.get(wikidata_pattern.format(entity=wikidata_entity)).content)\n",
    "  # print(wikidata_json['entities'][wikidata_entity]['claims']['P2044'][0]['mainsnak']['datavalue']['value']['amount'])\n",
    "\n",
    "  wikidata_soup, wikidata_scrape_index = get_soup(city_soup.select_one('#t-wikibase a')['href'])\n",
    "  try:\n",
    "    base_elevation = wikidata_soup.select_one('#P2044 .wikibase-snakview-value').text\n",
    "  except:\n",
    "    base_elevation = None\n",
    "\n",
    "  try:\n",
    "    peak_elevation = wikidata_soup.select_one('#P610 .wikibase-statementview-qualifiers .wikibase-snakview-value').text\n",
    "  except:\n",
    "    peak_elevation = None\n",
    "\n",
    "  return pd.DataFrame({\n",
    "    'scrape_index': [scrape_index],\n",
    "    'wikidata_scrape_index': [wikidata_scrape_index],\n",
    "    'city': [city],\n",
    "    'country': [country],\n",
    "    'latitude': [latitude],\n",
    "    'longitude': [longitude],\n",
    "    'base_elevation': [base_elevation],\n",
    "    'peak_elevation': [peak_elevation],\n",
    "    'population': [population],\n",
    "    'date': [date]})\n",
    "\n",
    "# scrape_city('Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "jXI3PQxjwNC1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def scrape_cities(cities: list[str] = [ 'Berlin', 'Hamburg', 'Munich' ]):\n",
    "  \"\"\"\n",
    "  Extract phase: Scrape city data from Wikipedia for multiple cities\n",
    "\n",
    "  Uses scape_city() for each city.\n",
    "\n",
    "  :param cities: cities to scrape data for\n",
    "  :return: pandas data-frame with data for all cities\n",
    "  \"\"\"\n",
    "  df = None\n",
    "  for city in cities:\n",
    "    print(city)\n",
    "    df = pd.concat([df, scrape_city(city)], ignore_index=True)\n",
    "  return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 936
    },
    "id": "wIJM4ATUtOzz",
    "outputId": "9159a853-02c2-41d6-df1a-10e3f8a843c9"
   },
   "outputs": [],
   "source": [
    "if False:\n",
    "  df = scrape_cities([\n",
    "      'Berlin',\n",
    "      'Hamburg',\n",
    "      'Munich',\n",
    "      'Stuttgart',\n",
    "      'Tübingen',\n",
    "      'Potsdam',\n",
    "      'Werder (Havel)',\n",
    "      'Paris',\n",
    "      'London',\n",
    "      'Vienna',\n",
    "      'Warsaw',\n",
    "      'Prague',\n",
    "      'Zürich',\n",
    "      'New York City',\n",
    "      'Tokyo',\n",
    "      'Beijing',\n",
    "      'Moscow',\n",
    "      'Sydney'\n",
    "      ])\n",
    "  display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def transform_lat_lon(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Transform phase: Transform `deg°min'sec''[NSEW]` to decimal degrees\n",
    "\n",
    "  :param df: data-frame to transform\n",
    "  :return: transformed data-frame\n",
    "  \"\"\"\n",
    "  return df.assign(\n",
    "    latitude=lambda x: x.latitude.apply(lat_lon_parser.parse),\n",
    "    longitude=lambda x: x.longitude.apply(lat_lon_parser.parse)\n",
    "  )\n",
    "# transform_lat_lon(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "id": "TY6EFQhBuM_S",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_cities():\n",
    "  \"\"\"\n",
    "  Retrieve city table from SQL\n",
    "  \n",
    "  :return: data-frame with city properties indexed by ID\n",
    "  \"\"\"\n",
    "  return pd.read_sql('city', con=connection).set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "id": "TY6EFQhBuM_S",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_cities(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Load phase: Store extracted and transformed static city data\n",
    "\n",
    "  Uses global SQL `connection`.\n",
    "  \n",
    "  Skips cities that are already in the DB (by `name` column).\n",
    "\n",
    "  :param df: data-frame containing the city data\n",
    "  \"\"\"\n",
    "  existing_cities = read_cities().reset_index().set_index('name')\n",
    "  sql_df = (\n",
    "    df[['city','country','latitude','longitude','base_elevation','peak_elevation']]\n",
    "    .rename(columns={'city': 'name'})\n",
    "    .join(existing_cities.id, on='name') # assign existing IDs\n",
    "    [lambda x: pd.isna(x.id)] # skip existing IDs\n",
    "    .drop(columns=['id'])\n",
    "  )\n",
    "  sql_df.to_sql('city',\n",
    "                  if_exists='append',\n",
    "                  con=connection,\n",
    "                  index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def to_meta(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Formats each row of a data-frame as a JSON string for storage in the `fact.meta` column.\n",
    "\n",
    "  :param df: data-frame to format row-wise as JSON\n",
    "  :return: series of JSON strings\n",
    "  \"\"\"\n",
    "  return df.apply(axis=1, func=lambda x: x.to_json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_facts():\n",
    "  \"\"\"\n",
    "  Read `fact` table\n",
    "  \n",
    "  :return: data-frame\n",
    "  \"\"\"\n",
    "  return pd.read_sql('fact', con=connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_facts(df: pd.DataFrame, fact_column: str, meta_columns: list[str]):\n",
    "  \"\"\"\n",
    "  Store columns as city-facts with meta-data\n",
    "\n",
    "  Uses global SQL `connection`.\n",
    "\n",
    "  :param df: data-frame to store facts from, \n",
    "    must have `city` column with city names\n",
    "    and a `scrape_index` column to track the scrape\n",
    "  :param fact_column: name of the column to store as fact\n",
    "  :param meta_columns: list of columns to store as meta-data\n",
    "  \"\"\"\n",
    "  measure_id = get_or_create_measure_id(fact_column, df.dtypes[fact_column])\n",
    "  cities_df = read_cities()\n",
    "  (\n",
    "      df[['scrape_index', 'city', fact_column, *meta_columns]]\n",
    "      .join(\n",
    "          cities_df\n",
    "          [['name']]\n",
    "          .reset_index()\n",
    "          .set_index('name'),\n",
    "          on='city')\n",
    "      .drop(columns=['city'])\n",
    "      .rename(columns={'id': 'city'})\n",
    "      .assign(\n",
    "          scrape=lambda x: x.scrape_index.apply(get_scrape_id),\n",
    "          measure=measure_id,\n",
    "          value=lambda x: x[fact_column].astype(str),\n",
    "          meta=lambda x: to_meta(x[meta_columns])\n",
    "      )\n",
    "      .drop(columns=['scrape_index', fact_column, *meta_columns])\n",
    "  ).to_sql('fact', con=connection, if_exists='append', index=False)\n",
    "\n",
    "# store_facts(df, 'population', ['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd.read_sql('fact', con=connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd.read_sql('measure', con=connection).set_index('id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def etl_cities():\n",
    "  \"\"\"\n",
    "  Extract, transform and load city data into SQL database\n",
    "  \"\"\"\n",
    "  \n",
    "  print('Scraping wikipedia for city data...')\n",
    "  \n",
    "  cities = [\n",
    "    \"Berlin\",\n",
    "    \"Hamburg\",\n",
    "    \"Munich\",\n",
    "    \"Stuttgart\",\n",
    "    \"Tübingen\",\n",
    "    \"Potsdam\",\n",
    "    \"Werder (Havel)\",\n",
    "    \"Paris\",\n",
    "    \"London\",\n",
    "    \"Vienna\",\n",
    "    \"Warsaw\",\n",
    "    \"Prague\",\n",
    "    \"Zürich\",\n",
    "    \"New York City\",\n",
    "    \"Tokyo\",\n",
    "    \"Beijing\",\n",
    "    \"Moscow\",\n",
    "    \"Sydney\",\n",
    "  ]\n",
    "\n",
    "  # extract\n",
    "  df = scrape_cities(cities)\n",
    "  # transform\n",
    "  df = transform_lat_lon(df)\n",
    "  # load\n",
    "  store_cities(df)\n",
    "  store_facts(df, 'population', ['date'])\n",
    "  print('.\\n')\n",
    "  \n",
    "# etl_cities()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API infrastructure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_json(url, headers = None):\n",
    "  \"\"\"\n",
    "  Retrieve JSON from URL using a local cache.\n",
    "\n",
    "  If the URL was previously retrieved, the cached JSON is served instead of making a web request.\n",
    "  If the URL is requested from the web, the JSON is stored in the cache.\n",
    "\n",
    "  :param url: the URL to retrieve from cache or web\n",
    "  :param headers: the HTTP request headers to pass (for authorization)\n",
    "  :return: (json, scrape_index) where scrape_index refers to the scrape_list\n",
    "  \"\"\"\n",
    "  try:\n",
    "    if cached := get_json.cache.get(url):\n",
    "      return cached\n",
    "  except:\n",
    "    get_json.cache = {}\n",
    "    \n",
    "  dt = datetime.now()\n",
    "  \n",
    "  response = requests.get(url, headers=headers)\n",
    "  response.raise_for_status()\n",
    "  if response.status_code == 204: # No Content\n",
    "    json = None\n",
    "  else:\n",
    "    json = response.json()\n",
    "    \n",
    "  # dt = email.utils.parsedate_to_datetime(response.headers['date'])\n",
    "  scrape_index = len(scrape_list)\n",
    "  scrape_list.append([url, dt, None])\n",
    "  get_json.cache[url] = (json, scrape_index)\n",
    "  return (json, scrape_index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OpenWeatherMap data via API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_weather(city=None, lat=None, lon=None):\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve weather forecast for location from OpenWeatherMap\n",
    "  \n",
    "  The location can either be given by a city name (known in the `city` table)\n",
    "  or by giving latitude/longitude explicitly.\n",
    "  \n",
    "  :param city: city to retrieve weather data for\n",
    "  :param lat: latitude to retrieve weather data for\n",
    "  :param lon: longitude to retrieve weather data for\n",
    "  :return: data-frame containing a time-series of forecast data\n",
    "  \"\"\"\n",
    "  if city != None:\n",
    "    cities_df = read_cities()\n",
    "    latlon = cities_df.set_index('name').loc[city, ['latitude', 'longitude']]\n",
    "    return get_weather(lat=latlon['latitude'], lon=latlon['longitude'])\n",
    "\n",
    "  openweathermap_pattern = 'https://api.openweathermap.org/data/2.5/forecast?lat={lat}&lon={lon}&appid={openweathermap_key}&units=metric'\n",
    "  openweathermap_key = os.getenv('openweathermap_key')\n",
    "\n",
    "  url = openweathermap_pattern.format(lat=lat, lon=lon, openweathermap_key=openweathermap_key)\n",
    "  json, scrape_index = get_json(url)\n",
    "  df = pd.json_normalize(json['list'])\n",
    "  df['scrape_index'] = scrape_index\n",
    "  for optional in ['rain.3h', 'snow.3h']:\n",
    "    if not optional in df:\n",
    "      df[optional] = np.nan\n",
    "  df.dt = pd.to_datetime(df.dt, unit='s')\n",
    "  return df[['scrape_index', 'dt', 'main.temp', 'main.feels_like', 'wind.speed', 'wind.gust', 'rain.3h', 'snow.3h']].fillna(0)\n",
    "\n",
    "# get_weather(city='Berlin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_all_weather():\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve weather data for all cities in the DB\n",
    "  \n",
    "  :return: collected data-frame\n",
    "  \"\"\"\n",
    "  cities_df = read_cities()\n",
    "  df = None\n",
    "  for city_id, (city, lat, lon) in cities_df[[\"name\", \"latitude\", \"longitude\"]].iterrows():\n",
    "    rows = get_weather(lat=lat, lon=lon).assign(city=city_id)\n",
    "    print(city, len(rows))\n",
    "    df = pd.concat([df, rows])\n",
    "  return df\n",
    "#get_all_weather()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def rename_weather_columns(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Transform phase: Rename columns from OpenWeathMap names to DB names\n",
    "  \n",
    "  :param df: data-frame to process\n",
    "  :return: resulting data-frame\n",
    "  \"\"\"\n",
    "  return df.rename(\n",
    "    columns={\n",
    "      \"main.temp\": \"T_celsius\",\n",
    "      \"main.feels_like\": \"T_feelslike_celsius\",\n",
    "      \"wind.speed\": \"wind_speed_mps\",\n",
    "      \"wind.gust\": \"wind_gust_mps\",\n",
    "      \"rain.3h\": \"rain_3h_mm\",\n",
    "      \"snow.3h\": \"snow_3h_mm\",\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_weather(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Load phase: Store extracted and transformed data in DB\n",
    "  \n",
    "  :param df: data-frame to store\n",
    "  \"\"\"\n",
    "  (\n",
    "    df\n",
    "    .assign(scrape=lambda x: x.scrape_index.apply(get_scrape_id))\n",
    "    .drop(columns=['scrape_index'])\n",
    "  ).to_sql(\"weather\", con=connection, index=False, if_exists=\"append\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_weather():\n",
    "  \"\"\"\n",
    "  Read `weather` table from DB.\n",
    "  \n",
    "  :return: data-frame\n",
    "  \"\"\"\n",
    "  return pd.read_sql('weather', con=connection).set_index('city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def etl_weather():\n",
    "  \"\"\"\n",
    "  Extract, transform and load weather data into DB\n",
    "  \"\"\"\n",
    "  print('Retrieving weather data from OpenWeatherMap...')\n",
    "  \n",
    "  df = get_all_weather()\n",
    "  df = rename_weather_columns(df)\n",
    "  store_weather(df)\n",
    "  print('.\\n')\n",
    "\n",
    "# etl_weather()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flight data from AeroDataBox\n",
    "### Airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_airports(lat: float, lon: float, radius_km: float = 25, limit: int = 10):\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve nearby airports for location from AeroDataBox\n",
    "  \n",
    "  :param lat: latitude to retrieve weather data for\n",
    "  :param lon: longitude to retrieve weather data for\n",
    "  :param radius_km: search radius,\n",
    "    default 50 km\n",
    "  :param limit: limit of number of results\n",
    "  \n",
    "  :return: data-frame with list of airports\n",
    "  \"\"\"\n",
    "  dotenv.load_dotenv()\n",
    "  url = \"https://aerodatabox.p.rapidapi.com/airports/search/location?{params}\"\n",
    "  querystring = {\"lat\":lat, \"lon\":lon, \"radiusKm\":radius_km, \"limit\":limit, \"withFlightInfoOnly\":\"true\"}\n",
    "\n",
    "  headers = {\n",
    "      \"X-RapidAPI-Key\": os.getenv('aerodatabox_key'),\n",
    "      \"X-RapidAPI-Host\": \"aerodatabox.p.rapidapi.com\"\n",
    "  }\n",
    "  \n",
    "  json, scrape_index = get_json(url.format(params=urllib.parse.urlencode(querystring)), headers)\n",
    "\n",
    "  df = pd.json_normalize(json['items'])\n",
    "  df['scrape_index'] = scrape_index\n",
    "  return df\n",
    "# get_airports(*cities_df.set_index('name').loc['Berlin',['latitude', 'longitude']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_all_airports():\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve airport data for all cities in the DB\n",
    "  \n",
    "  :return: tuple (data-frame of collected airports, data-frame of airport-city associations)\n",
    "  \"\"\"\n",
    "  cities_df = read_cities()\n",
    "  airports_df = None\n",
    "  city_airports_df = None\n",
    "  for city_id, (city, lat, lon) in cities_df[[\"name\", \"latitude\", \"longitude\"]].iterrows():\n",
    "    rows = get_airports(lat=lat, lon=lon)\n",
    "    print(city, len(rows))\n",
    "    airports_df = pd.concat([airports_df, rows], ignore_index=True)\n",
    "    if len(rows):\n",
    "      city_airports_df = pd.concat([city_airports_df, rows[['icao','scrape_index']].assign(city=city_id)], ignore_index=True)\n",
    "  airports_df = airports_df.drop_duplicates(subset=['icao'])\n",
    "  return airports_df, city_airports_df\n",
    "\n",
    "if False:\n",
    "  for df in get_all_airports():\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def rename_airport_columns(airports_df: pd.DataFrame, city_airports_df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Transform phase: Rename columns from AeroDataBox airport names to DB names\n",
    "  \n",
    "  :param airports_df: airport data-frame to process\n",
    "  :param city_airports_df: city-airport association data-frame to process\n",
    "  :return: tuple (data-frame of collected airports, data-frame of airport-city associations)\n",
    "  \"\"\"\n",
    "  return (\n",
    "    (\n",
    "      airports_df\n",
    "      .rename(columns={\n",
    "        \"location.lat\": \"latitude\",\n",
    "        \"location.lon\": \"longitude\",\n",
    "      })\n",
    "      [['scrape_index', 'icao', 'iata', 'name', 'latitude', 'longitude']]\n",
    "    ),\n",
    "    city_airports_df\n",
    "  )\n",
    "#rename_airport_columns(*get_all_airports())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def calculate_airport_distance(airports_df: pd.DataFrame, city_airports_df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Transform phase: Calculate AeroDataBox airport distance to respective city\n",
    "  \n",
    "  :param airports_df: airport data-frame to process\n",
    "  :param city_airports_df: city-airport association data-frame to process\n",
    "  :return: tuple (data-frame of collected airports, data-frame of airport-city associations)\n",
    "  \"\"\"\n",
    "  cities_df = read_cities()\n",
    "  return (\n",
    "    airports_df,\n",
    "    (\n",
    "      city_airports_df\n",
    "      .join(\n",
    "        cities_df[['latitude', 'longitude']]\n",
    "        .rename(columns={'latitude': 'city_lat', 'longitude': 'city_lon'}),\n",
    "        on='city'\n",
    "      )\n",
    "      .join(\n",
    "        airports_df[['icao', 'latitude', 'longitude']]\n",
    "        .set_index('icao'),\n",
    "        on='icao'\n",
    "      )\n",
    "      .assign(\n",
    "        distance_km=lambda x: x.apply(\n",
    "          lambda r: distance.distance((r.latitude, r.longitude), (r.city_lat, r.city_lon)).km, \n",
    "          axis=1)\n",
    "      )\n",
    "      .drop(columns=['latitude', 'longitude', 'city_lat', 'city_lon'])\n",
    "    )\n",
    "  )\n",
    "if False:\n",
    "  for df in calculate_airport_distance(*rename_airport_columns(*get_all_airports())):\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def read_airports():\n",
    "  \"\"\"\n",
    "  Load airports from DB.\n",
    "  \n",
    "  :return: tuple (data-frame of collected airports, data-frame of airport-city associations)\n",
    "  \"\"\"\n",
    "  return (\n",
    "    pd.read_sql('airport', con=connection).set_index('icao'),\n",
    "    pd.read_sql('city_airport', con=connection).set_index('city')\n",
    "  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_airports(airports_df: pd.DataFrame, city_airports_df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Load phase: Store extracted and transformed data in DB\n",
    "  \n",
    "  This skips (city, airport) pairs that are already present.\n",
    "  \n",
    "  :param airports_df: airport data-frame to process\n",
    "  :param city_airports_df: city-airport association data-frame to process\n",
    "  \"\"\"\n",
    "  existing_airports, existing_city_airports = read_airports()\n",
    "  existing_city_airports = existing_city_airports.reset_index().set_index(['city', 'icao'])\n",
    "  (\n",
    "    airports_df\n",
    "    .join(existing_airports.assign(exists=True).exists, on='icao')\n",
    "    [lambda x: x.exists.isna()]\n",
    "    .drop(columns=['exists'])\n",
    "    .assign(scrape=lambda x: x.scrape_index.apply(get_scrape_id))\n",
    "    .drop(columns=['scrape_index'])\n",
    "  ).to_sql(\"airport\", con=connection, index=False, if_exists=\"append\")\n",
    "  (\n",
    "    city_airports_df\n",
    "    .join(existing_city_airports.assign(exists=True).exists, on=['city', 'icao'])\n",
    "    [lambda x: x.exists.isna()]\n",
    "    .drop(columns=['exists'])\n",
    "    .assign(scrape=lambda x: x.scrape_index.apply(get_scrape_id))\n",
    "    .drop(columns=['scrape_index'])\n",
    "  ).to_sql(\"city_airport\", con=connection, index=False, if_exists=\"append\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def etl_airports():\n",
    "  \"\"\"\n",
    "  Extract, transform and load airport data into DB\n",
    "  \"\"\"\n",
    "  print('Locating airports with AeroDataBox API...')\n",
    "  df = get_all_airports()\n",
    "  df = rename_airport_columns(*df)\n",
    "  df = calculate_airport_distance(*df)\n",
    "  store_airports(*df)\n",
    "  print('.\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if False: # dev code\n",
    "  reset_database()\n",
    "  init_database()\n",
    "  etl_cities()\n",
    "  etl_airports()\n",
    "  for df in read_airports():\n",
    "    display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_flights(airport: pd.Series):\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve flights for airport from AeroDataBox\n",
    "  \n",
    "  This retrieves 24h worth of flights starting 0h tomorrow in UTC.\n",
    "  \n",
    "  :param airport: row from the `airport` table\n",
    "  \n",
    "  :return: data-frame with list of flights\n",
    "  \"\"\"\n",
    "  def parse_adb_datetime(dt: str):\n",
    "    return datetime.fromisoformat(dt.replace(' ', 'T'))\n",
    "\n",
    "  def flights_12h_from(from_datetime: datetime):\n",
    "    to_datetime = from_datetime + timedelta(hours=12, microseconds=-1)\n",
    "\n",
    "    # make local \"naive\" (without tzinfo) datetimes in ISO format\n",
    "    from_datetime = from_datetime.astimezone(tz).replace(tzinfo=None).isoformat()\n",
    "    to_datetime = to_datetime.astimezone(tz).replace(tzinfo=None).isoformat()\n",
    "\n",
    "    url=url_pattern.format(\n",
    "      icao=airport.icao, \n",
    "      from_datetime=from_datetime, \n",
    "      to_datetime=to_datetime, \n",
    "      query=urllib.parse.urlencode(params))\n",
    "\n",
    "    headers = {\n",
    "        \"X-RapidAPI-Key\": os.getenv('aerodatabox_key'),\n",
    "        \"X-RapidAPI-Host\": \"aerodatabox.p.rapidapi.com\"\n",
    "    }\n",
    "\n",
    "    try:\n",
    "      json, scrape_index = get_json(url, headers)\n",
    "    except HTTPError as e:\n",
    "      print(f'Error: {e}')\n",
    "      return None\n",
    "\n",
    "    extract = []\n",
    "    if json != None:\n",
    "      for direction in ['departures', 'arrivals']:\n",
    "        this_direction = 'departure' if direction == 'departures' else 'arrival'\n",
    "        other_direction = 'arrival' if direction == 'departures' else 'departure'\n",
    "        extract += [ \n",
    "          { \n",
    "            'scheduled_time': parse_adb_datetime(flight[this_direction]['scheduledTime']['utc']),\n",
    "            'destination_icao': flight[other_direction]['airport'].get('icao', None),\n",
    "            'destination_name': flight[other_direction]['airport']['name'],\n",
    "            'number': flight['number'],\n",
    "            'type': this_direction\n",
    "          } \n",
    "          for flight in json[direction]\n",
    "        ]\n",
    "    return pd.json_normalize(extract).assign(scrape_index=scrape_index, origin_icao=airport.icao)\n",
    "\n",
    "  dotenv.load_dotenv()\n",
    "\n",
    "  url_pattern = \"https://aerodatabox.p.rapidapi.com/flights/airports/icao/{icao}/{from_datetime}/{to_datetime}?{query}\"\n",
    "  params = {\"withLeg\":\"true\",\"withCancelled\":\"false\",\"withCodeshared\":\"true\",\"withCargo\":\"false\",\"withPrivate\":\"false\",\"withLocation\":\"false\"}\n",
    "\n",
    "  # get timezone for airport as time must be local\n",
    "  # static variable:\n",
    "  try:\n",
    "    tf = get_flights.tf\n",
    "  except:\n",
    "    tf = TimezoneFinder()\n",
    "    get_flights.tf = tf\n",
    "\n",
    "  tz_name = tf.timezone_at(lat=airport.latitude, lng=airport.longitude)\n",
    "  tz = timezone(tz_name)\n",
    "\n",
    "  \n",
    "  return pd.concat([\n",
    "    flights_12h_from((datetime.now(tz=utc) + timedelta(days=1)).replace(hour=hour, minute=0, second=0, microsecond=0)) \n",
    "    for hour in [0,12]])\n",
    "  \n",
    "# get_flights(read_airports()[0].reset_index().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_all_flights():\n",
    "  \"\"\"\n",
    "  Extract phase: Retrieve flights for all airports in the DB\n",
    "  \n",
    "  :return: collected data-frame\n",
    "  \"\"\"\n",
    "  airports_df, city_airports_df = read_airports()\n",
    "  \n",
    "  df = None\n",
    "  for _, airport in airports_df.reset_index().iterrows():\n",
    "    rows = get_flights(airport)\n",
    "    print(airport.icao, len(rows))\n",
    "    df = pd.concat([df, rows], ignore_index=True)\n",
    "  return df\n",
    "# get_all_flights()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def store_flights(df: pd.DataFrame):\n",
    "  \"\"\"\n",
    "  Load phase: Store extracted and transformed data in DB\n",
    "  \n",
    "  :param df: data-frame to process\n",
    "  \"\"\"\n",
    "  (\n",
    "    df\n",
    "    .assign(scrape=lambda x: x.scrape_index.apply(get_scrape_id))\n",
    "    .drop(columns=['scrape_index'])\n",
    "  ).to_sql(\"flight\", con=connection, index=False, if_exists=\"append\")\n",
    "\n",
    "if False:\n",
    "  reset_database()\n",
    "  etl_cities()\n",
    "  etl_airports()\n",
    "  store_flights(get_all_flights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def etl_flights():\n",
    "  \"\"\"\n",
    "  Extract, transform and load flight data into DB\n",
    "  \"\"\"\n",
    "  print('Retrieving flights from AeroDataBox API...')\n",
    "  df = get_all_flights()\n",
    "  # no transformations\n",
    "  store_flights(df)\n",
    "  print('.\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Orchestration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def etl_all():\n",
    "  etl_cities()\n",
    "  etl_weather()\n",
    "  etl_airports()\n",
    "  etl_flights()\n",
    "  print('Done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute orchestration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping wikipedia for city data...\n",
      "Berlin\n",
      "Hamburg\n",
      "Munich\n",
      "Stuttgart\n",
      "Tübingen\n",
      "Potsdam\n",
      "Werder (Havel)\n",
      "Paris\n",
      "London\n",
      "Vienna\n",
      "Warsaw\n",
      "Prague\n",
      "Zürich\n",
      "New York City\n",
      "Tokyo\n",
      "Beijing\n",
      "Moscow\n",
      "Sydney\n",
      ".\n",
      "\n",
      "Retrieving weather data from OpenWeatherMap...\n",
      "Berlin 40\n",
      "Hamburg 40\n",
      "Munich 40\n",
      "Stuttgart 40\n",
      "Tübingen 40\n",
      "Potsdam 40\n",
      "Werder (Havel) 40\n",
      "Paris 40\n",
      "London 40\n",
      "Vienna 40\n",
      "Warsaw 40\n",
      "Prague 40\n",
      "Zürich 40\n",
      "New York City 40\n",
      "Tokyo 40\n",
      "Beijing 40\n",
      "Moscow 40\n",
      "Sydney 40\n",
      ".\n",
      "\n",
      "Locating airports with AeroDataBox API...\n",
      "Berlin 1\n",
      "Hamburg 1\n",
      "Munich 0\n",
      "Stuttgart 1\n",
      "Tübingen 1\n",
      "Potsdam 0\n",
      "Werder (Havel) 0\n",
      "Paris 3\n",
      "London 2\n",
      "Vienna 1\n",
      "Warsaw 1\n",
      "Prague 1\n",
      "Zürich 1\n",
      "New York City 4\n",
      "Tokyo 1\n",
      "Beijing 0\n",
      "Moscow 0\n",
      "Sydney 1\n",
      ".\n",
      "\n",
      "Retrieving flights from AeroDataBox API...\n",
      "EDDB 319\n",
      "EDDH 186\n",
      "EDDS 114\n",
      "EGLC 141\n",
      "EGLL 4215\n",
      "EPWA 355\n",
      "KEWR 1011\n",
      "KJFK 1067\n",
      "KLGA 1031\n",
      "KTEB 1\n",
      "LFPB 0\n",
      "LFPG 814\n",
      "LFPO 334\n",
      "LKPR 305\n",
      "LOWW 771\n",
      "LSZH 1000\n",
      "RJTT 1577\n",
      "YSSY 1090\n",
      ".\n",
      "\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "reset_database()\n",
    "init_database()\n",
    "etl_all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary of acquired data\n",
    "## Static data\n",
    "### Cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "tags": []
   },
   "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>country</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>base_elevation</th>\n",
       "      <th>peak_elevation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</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>1</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.520000</td>\n",
       "      <td>13.405000</td>\n",
       "      <td>34±1 metre</td>\n",
       "      <td>121.9 metre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hamburg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>53.550000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6±1 metre</td>\n",
       "      <td>116.2 metre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.137500</td>\n",
       "      <td>11.575000</td>\n",
       "      <td>519±1 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.777500</td>\n",
       "      <td>9.180000</td>\n",
       "      <td>245±1 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tübingen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.520000</td>\n",
       "      <td>9.055556</td>\n",
       "      <td>338 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Potsdam</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.400000</td>\n",
       "      <td>13.066667</td>\n",
       "      <td>35 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Werder (Havel)</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.383333</td>\n",
       "      <td>12.933333</td>\n",
       "      <td>31±1 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Paris</td>\n",
       "      <td>France</td>\n",
       "      <td>48.856667</td>\n",
       "      <td>2.352222</td>\n",
       "      <td>28±1 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>51.507222</td>\n",
       "      <td>-0.127500</td>\n",
       "      <td>15 metre</td>\n",
       "      <td>245 metre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Vienna</td>\n",
       "      <td>Austria</td>\n",
       "      <td>48.208333</td>\n",
       "      <td>16.372500</td>\n",
       "      <td>151 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Poland</td>\n",
       "      <td>52.230000</td>\n",
       "      <td>21.011111</td>\n",
       "      <td>103 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Prague</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>50.087500</td>\n",
       "      <td>14.421389</td>\n",
       "      <td>235 metre</td>\n",
       "      <td>399 metre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Zürich</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>47.374444</td>\n",
       "      <td>8.541111</td>\n",
       "      <td>408 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>New York City</td>\n",
       "      <td>United States</td>\n",
       "      <td>40.712778</td>\n",
       "      <td>-74.006111</td>\n",
       "      <td>11 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Japan</td>\n",
       "      <td>35.689722</td>\n",
       "      <td>139.692222</td>\n",
       "      <td>6 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>39.906667</td>\n",
       "      <td>116.397500</td>\n",
       "      <td>43 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>Russia</td>\n",
       "      <td>55.755833</td>\n",
       "      <td>37.617222</td>\n",
       "      <td>156 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>None</td>\n",
       "      <td>-33.867778</td>\n",
       "      <td>151.210000</td>\n",
       "      <td>6 metre</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name          country   latitude   longitude base_elevation  \\\n",
       "id                                                                          \n",
       "1           Berlin          Germany  52.520000   13.405000     34±1 metre   \n",
       "2          Hamburg          Germany  53.550000   10.000000      6±1 metre   \n",
       "3           Munich          Germany  48.137500   11.575000    519±1 metre   \n",
       "4        Stuttgart          Germany  48.777500    9.180000    245±1 metre   \n",
       "5         Tübingen          Germany  48.520000    9.055556      338 metre   \n",
       "6          Potsdam          Germany  52.400000   13.066667       35 metre   \n",
       "7   Werder (Havel)          Germany  52.383333   12.933333     31±1 metre   \n",
       "8            Paris           France  48.856667    2.352222     28±1 metre   \n",
       "9           London          England  51.507222   -0.127500       15 metre   \n",
       "10          Vienna          Austria  48.208333   16.372500      151 metre   \n",
       "11          Warsaw           Poland  52.230000   21.011111      103 metre   \n",
       "12          Prague   Czech Republic  50.087500   14.421389      235 metre   \n",
       "13          Zürich      Switzerland  47.374444    8.541111      408 metre   \n",
       "14   New York City    United States  40.712778  -74.006111       11 metre   \n",
       "15           Tokyo            Japan  35.689722  139.692222        6 metre   \n",
       "16         Beijing            China  39.906667  116.397500       43 metre   \n",
       "17          Moscow           Russia  55.755833   37.617222      156 metre   \n",
       "18          Sydney             None -33.867778  151.210000        6 metre   \n",
       "\n",
       "   peak_elevation  \n",
       "id                 \n",
       "1     121.9 metre  \n",
       "2     116.2 metre  \n",
       "3            None  \n",
       "4            None  \n",
       "5            None  \n",
       "6            None  \n",
       "7            None  \n",
       "8            None  \n",
       "9       245 metre  \n",
       "10           None  \n",
       "11           None  \n",
       "12      399 metre  \n",
       "13           None  \n",
       "14           None  \n",
       "15           None  \n",
       "16           None  \n",
       "17           None  \n",
       "18           None  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_cities()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Airports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>city</th>\n",
       "      <th>airport_name</th>\n",
       "      <th>icao</th>\n",
       "      <th>iata</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>distance_km</th>\n",
       "      <th>scrape_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>Berlin Brandenburg</td>\n",
       "      <td>EDDB</td>\n",
       "      <td>BER</td>\n",
       "      <td>52.35139</td>\n",
       "      <td>13.493889</td>\n",
       "      <td>19.712043</td>\n",
       "      <td>2024-01-15 15:06:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hamburg</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>EDDH</td>\n",
       "      <td>HAM</td>\n",
       "      <td>53.63040</td>\n",
       "      <td>9.988229</td>\n",
       "      <td>8.982190</td>\n",
       "      <td>2024-01-15 15:06:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>EDDS</td>\n",
       "      <td>STR</td>\n",
       "      <td>48.68990</td>\n",
       "      <td>9.221960</td>\n",
       "      <td>10.218829</td>\n",
       "      <td>2024-01-15 15:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tübingen</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>EDDS</td>\n",
       "      <td>STR</td>\n",
       "      <td>48.68990</td>\n",
       "      <td>9.221960</td>\n",
       "      <td>22.529059</td>\n",
       "      <td>2024-01-15 15:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Paris</td>\n",
       "      <td>Paris -Le Bourget</td>\n",
       "      <td>LFPB</td>\n",
       "      <td>LBG</td>\n",
       "      <td>48.96940</td>\n",
       "      <td>2.441390</td>\n",
       "      <td>14.138277</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paris</td>\n",
       "      <td>Paris -Orly</td>\n",
       "      <td>LFPO</td>\n",
       "      <td>ORY</td>\n",
       "      <td>48.72530</td>\n",
       "      <td>2.359440</td>\n",
       "      <td>14.618344</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Paris</td>\n",
       "      <td>Paris Charles de Gaulle</td>\n",
       "      <td>LFPG</td>\n",
       "      <td>CDG</td>\n",
       "      <td>49.01280</td>\n",
       "      <td>2.549999</td>\n",
       "      <td>22.615527</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>London</td>\n",
       "      <td>London City</td>\n",
       "      <td>EGLC</td>\n",
       "      <td>LCY</td>\n",
       "      <td>51.50530</td>\n",
       "      <td>0.055277</td>\n",
       "      <td>12.692189</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>London</td>\n",
       "      <td>London Heathrow</td>\n",
       "      <td>EGLL</td>\n",
       "      <td>LHR</td>\n",
       "      <td>51.47060</td>\n",
       "      <td>-0.461941</td>\n",
       "      <td>23.584002</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vienna</td>\n",
       "      <td>Vienna Schwechat</td>\n",
       "      <td>LOWW</td>\n",
       "      <td>VIE</td>\n",
       "      <td>48.11030</td>\n",
       "      <td>16.569700</td>\n",
       "      <td>18.277173</td>\n",
       "      <td>2024-01-15 15:07:02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Warsaw</td>\n",
       "      <td>Warsaw Chopin</td>\n",
       "      <td>EPWA</td>\n",
       "      <td>WAW</td>\n",
       "      <td>52.16570</td>\n",
       "      <td>20.967100</td>\n",
       "      <td>7.761816</td>\n",
       "      <td>2024-01-15 15:07:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prague</td>\n",
       "      <td>Prague Ruzyně</td>\n",
       "      <td>LKPR</td>\n",
       "      <td>PRG</td>\n",
       "      <td>50.10080</td>\n",
       "      <td>14.260000</td>\n",
       "      <td>11.642654</td>\n",
       "      <td>2024-01-15 15:07:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Zürich</td>\n",
       "      <td>Zurich Kloten</td>\n",
       "      <td>LSZH</td>\n",
       "      <td>ZRH</td>\n",
       "      <td>47.46470</td>\n",
       "      <td>8.549169</td>\n",
       "      <td>10.052929</td>\n",
       "      <td>2024-01-15 15:07:03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New York City</td>\n",
       "      <td>New York La Guardia</td>\n",
       "      <td>KLGA</td>\n",
       "      <td>LGA</td>\n",
       "      <td>40.77720</td>\n",
       "      <td>-73.872600</td>\n",
       "      <td>13.354095</td>\n",
       "      <td>2024-01-15 15:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Newark Liberty</td>\n",
       "      <td>KEWR</td>\n",
       "      <td>EWR</td>\n",
       "      <td>40.69250</td>\n",
       "      <td>-74.168700</td>\n",
       "      <td>13.924027</td>\n",
       "      <td>2024-01-15 15:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>New York City</td>\n",
       "      <td>Teterboro</td>\n",
       "      <td>KTEB</td>\n",
       "      <td>TEB</td>\n",
       "      <td>40.85010</td>\n",
       "      <td>-74.060800</td>\n",
       "      <td>15.933027</td>\n",
       "      <td>2024-01-15 15:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>New York City</td>\n",
       "      <td>New York John F Kennedy</td>\n",
       "      <td>KJFK</td>\n",
       "      <td>JFK</td>\n",
       "      <td>40.63980</td>\n",
       "      <td>-73.778900</td>\n",
       "      <td>20.849126</td>\n",
       "      <td>2024-01-15 15:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>RJTT</td>\n",
       "      <td>HND</td>\n",
       "      <td>35.55230</td>\n",
       "      <td>139.780000</td>\n",
       "      <td>17.196349</td>\n",
       "      <td>2024-01-15 15:07:04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>Sydney City Sydney Kingsford Smith</td>\n",
       "      <td>YSSY</td>\n",
       "      <td>SYD</td>\n",
       "      <td>-33.94610</td>\n",
       "      <td>151.177000</td>\n",
       "      <td>9.208064</td>\n",
       "      <td>2024-01-15 15:07:05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city                        airport_name  icao iata  latitude  \\\n",
       "0          Berlin                  Berlin Brandenburg  EDDB  BER  52.35139   \n",
       "1         Hamburg                            Hamburg   EDDH  HAM  53.63040   \n",
       "2       Stuttgart                          Stuttgart   EDDS  STR  48.68990   \n",
       "3        Tübingen                          Stuttgart   EDDS  STR  48.68990   \n",
       "4           Paris                   Paris -Le Bourget  LFPB  LBG  48.96940   \n",
       "5           Paris                         Paris -Orly  LFPO  ORY  48.72530   \n",
       "6           Paris             Paris Charles de Gaulle  LFPG  CDG  49.01280   \n",
       "7          London                         London City  EGLC  LCY  51.50530   \n",
       "8          London                     London Heathrow  EGLL  LHR  51.47060   \n",
       "9          Vienna                    Vienna Schwechat  LOWW  VIE  48.11030   \n",
       "10         Warsaw                       Warsaw Chopin  EPWA  WAW  52.16570   \n",
       "11         Prague                       Prague Ruzyně  LKPR  PRG  50.10080   \n",
       "12         Zürich                       Zurich Kloten  LSZH  ZRH  47.46470   \n",
       "13  New York City                 New York La Guardia  KLGA  LGA  40.77720   \n",
       "14  New York City                      Newark Liberty  KEWR  EWR  40.69250   \n",
       "15  New York City                          Teterboro   KTEB  TEB  40.85010   \n",
       "16  New York City             New York John F Kennedy  KJFK  JFK  40.63980   \n",
       "17          Tokyo                              Tokyo   RJTT  HND  35.55230   \n",
       "18         Sydney  Sydney City Sydney Kingsford Smith  YSSY  SYD -33.94610   \n",
       "\n",
       "     longitude  distance_km    scrape_timestamp  \n",
       "0    13.493889    19.712043 2024-01-15 15:06:59  \n",
       "1     9.988229     8.982190 2024-01-15 15:06:59  \n",
       "2     9.221960    10.218829 2024-01-15 15:07:00  \n",
       "3     9.221960    22.529059 2024-01-15 15:07:00  \n",
       "4     2.441390    14.138277 2024-01-15 15:07:02  \n",
       "5     2.359440    14.618344 2024-01-15 15:07:02  \n",
       "6     2.549999    22.615527 2024-01-15 15:07:02  \n",
       "7     0.055277    12.692189 2024-01-15 15:07:02  \n",
       "8    -0.461941    23.584002 2024-01-15 15:07:02  \n",
       "9    16.569700    18.277173 2024-01-15 15:07:02  \n",
       "10   20.967100     7.761816 2024-01-15 15:07:03  \n",
       "11   14.260000    11.642654 2024-01-15 15:07:03  \n",
       "12    8.549169    10.052929 2024-01-15 15:07:03  \n",
       "13  -73.872600    13.354095 2024-01-15 15:07:04  \n",
       "14  -74.168700    13.924027 2024-01-15 15:07:04  \n",
       "15  -74.060800    15.933027 2024-01-15 15:07:04  \n",
       "16  -73.778900    20.849126 2024-01-15 15:07:04  \n",
       "17  139.780000    17.196349 2024-01-15 15:07:04  \n",
       "18  151.177000     9.208064 2024-01-15 15:07:05  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "  read_airports()[1]\n",
    "  .join(read_cities().name.rename('city_name'), on='city')\n",
    "  .join(read_scrapes().timestamp.rename('scrape_timestamp'), on='scrape')\n",
    "  .join(read_airports()[0].drop(columns='scrape'), on='icao')\n",
    "  .reset_index()\n",
    "  [['city_name', 'name', 'icao', 'iata', 'latitude', 'longitude', 'distance_km', 'scrape_timestamp']]\n",
    "  .rename(columns={'city_name': 'city', 'name': 'airport_name'})\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic data\n",
    "### Scrapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>url</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Berlin</td>\n",
       "      <td>2024-01-15 14:55:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Hamburg</td>\n",
       "      <td>2024-01-15 14:55:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Munich</td>\n",
       "      <td>2024-01-15 14:55:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://en.wikipedia.org/wiki/Stuttgart</td>\n",
       "      <td>2024-01-15 14:55:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>https://en.wikipedia.org/wiki/T%C3%BCbingen</td>\n",
       "      <td>2024-01-15 14:55:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>https://aerodatabox.p.rapidapi.com/flights/air...</td>\n",
       "      <td>2024-01-15 15:07:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>https://aerodatabox.p.rapidapi.com/flights/air...</td>\n",
       "      <td>2024-01-15 15:07:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>https://aerodatabox.p.rapidapi.com/flights/air...</td>\n",
       "      <td>2024-01-15 15:07:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>https://aerodatabox.p.rapidapi.com/flights/air...</td>\n",
       "      <td>2024-01-15 15:07:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>https://aerodatabox.p.rapidapi.com/flights/air...</td>\n",
       "      <td>2024-01-15 15:14:39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  url           timestamp\n",
       "id                                                                       \n",
       "1                https://en.wikipedia.org/wiki/Berlin 2024-01-15 14:55:20\n",
       "2               https://en.wikipedia.org/wiki/Hamburg 2024-01-15 14:55:22\n",
       "3                https://en.wikipedia.org/wiki/Munich 2024-01-15 14:55:28\n",
       "4             https://en.wikipedia.org/wiki/Stuttgart 2024-01-15 14:55:30\n",
       "5         https://en.wikipedia.org/wiki/T%C3%BCbingen 2024-01-15 14:55:31\n",
       "..                                                ...                 ...\n",
       "78  https://aerodatabox.p.rapidapi.com/flights/air... 2024-01-15 15:07:22\n",
       "79  https://aerodatabox.p.rapidapi.com/flights/air... 2024-01-15 15:07:22\n",
       "80  https://aerodatabox.p.rapidapi.com/flights/air... 2024-01-15 15:07:23\n",
       "81  https://aerodatabox.p.rapidapi.com/flights/air... 2024-01-15 15:07:24\n",
       "82  https://aerodatabox.p.rapidapi.com/flights/air... 2024-01-15 15:14:39\n",
       "\n",
       "[82 rows x 2 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "read_scrapes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### City measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_name</th>\n",
       "      <th>measure_name</th>\n",
       "      <th>value</th>\n",
       "      <th>meta</th>\n",
       "      <th>scrape_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>population</td>\n",
       "      <td>3850809</td>\n",
       "      <td>{'date': '2021'}</td>\n",
       "      <td>2024-01-15 14:55:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hamburg</td>\n",
       "      <td>population</td>\n",
       "      <td>1945532</td>\n",
       "      <td>{'date': '2022-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Munich</td>\n",
       "      <td>population</td>\n",
       "      <td>1512491</td>\n",
       "      <td>{'date': '2022-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>population</td>\n",
       "      <td>626275</td>\n",
       "      <td>{'date': '2021-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Tübingen</td>\n",
       "      <td>population</td>\n",
       "      <td>91877</td>\n",
       "      <td>{'date': '2021-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Potsdam</td>\n",
       "      <td>population</td>\n",
       "      <td>183154</td>\n",
       "      <td>{'date': '2021-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Werder (Havel)</td>\n",
       "      <td>population</td>\n",
       "      <td>26767</td>\n",
       "      <td>{'date': '2021-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paris</td>\n",
       "      <td>population</td>\n",
       "      <td>2102650</td>\n",
       "      <td>{'date': '2023'}</td>\n",
       "      <td>2024-01-15 14:55:35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>London</td>\n",
       "      <td>population</td>\n",
       "      <td>8799800</td>\n",
       "      <td>{'date': '2021'}</td>\n",
       "      <td>2024-01-15 14:55:37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vienna</td>\n",
       "      <td>population</td>\n",
       "      <td>2002821</td>\n",
       "      <td>{'date': None}</td>\n",
       "      <td>2024-01-15 14:55:39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Warsaw</td>\n",
       "      <td>population</td>\n",
       "      <td>1863056</td>\n",
       "      <td>{'date': '2022'}</td>\n",
       "      <td>2024-01-15 14:55:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Prague</td>\n",
       "      <td>population</td>\n",
       "      <td>1357326</td>\n",
       "      <td>{'date': '2023-01-01'}</td>\n",
       "      <td>2024-01-15 14:55:43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Zürich</td>\n",
       "      <td>population</td>\n",
       "      <td>415367</td>\n",
       "      <td>{'date': '2018-12-31'}</td>\n",
       "      <td>2024-01-15 14:55:46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>New York City</td>\n",
       "      <td>population</td>\n",
       "      <td>8804190</td>\n",
       "      <td>{'date': '2020'}</td>\n",
       "      <td>2024-01-15 14:55:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>population</td>\n",
       "      <td>14094034</td>\n",
       "      <td>{'date': '2023'}</td>\n",
       "      <td>2024-01-15 14:55:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>population</td>\n",
       "      <td>21893095</td>\n",
       "      <td>{'date': '2020'}</td>\n",
       "      <td>2024-01-15 14:55:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Moscow</td>\n",
       "      <td>population</td>\n",
       "      <td>13010112</td>\n",
       "      <td>{'date': '2021'}</td>\n",
       "      <td>2024-01-15 14:55:55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>population</td>\n",
       "      <td>5297089</td>\n",
       "      <td>{'date': '2022'}</td>\n",
       "      <td>2024-01-15 14:55:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         city_name measure_name     value                    meta  \\\n",
       "0           Berlin   population   3850809        {'date': '2021'}   \n",
       "1          Hamburg   population   1945532  {'date': '2022-12-31'}   \n",
       "2           Munich   population   1512491  {'date': '2022-12-31'}   \n",
       "3        Stuttgart   population    626275  {'date': '2021-12-31'}   \n",
       "4         Tübingen   population     91877  {'date': '2021-12-31'}   \n",
       "5          Potsdam   population    183154  {'date': '2021-12-31'}   \n",
       "6   Werder (Havel)   population     26767  {'date': '2021-12-31'}   \n",
       "7            Paris   population   2102650        {'date': '2023'}   \n",
       "8           London   population   8799800        {'date': '2021'}   \n",
       "9           Vienna   population   2002821          {'date': None}   \n",
       "10          Warsaw   population   1863056        {'date': '2022'}   \n",
       "11          Prague   population   1357326  {'date': '2023-01-01'}   \n",
       "12          Zürich   population    415367  {'date': '2018-12-31'}   \n",
       "13   New York City   population   8804190        {'date': '2020'}   \n",
       "14           Tokyo   population  14094034        {'date': '2023'}   \n",
       "15         Beijing   population  21893095        {'date': '2020'}   \n",
       "16          Moscow   population  13010112        {'date': '2021'}   \n",
       "17          Sydney   population   5297089        {'date': '2022'}   \n",
       "\n",
       "      scrape_timestamp  \n",
       "0  2024-01-15 14:55:20  \n",
       "1  2024-01-15 14:55:22  \n",
       "2  2024-01-15 14:55:28  \n",
       "3  2024-01-15 14:55:30  \n",
       "4  2024-01-15 14:55:31  \n",
       "5  2024-01-15 14:55:33  \n",
       "6  2024-01-15 14:55:34  \n",
       "7  2024-01-15 14:55:35  \n",
       "8  2024-01-15 14:55:37  \n",
       "9  2024-01-15 14:55:39  \n",
       "10 2024-01-15 14:55:41  \n",
       "11 2024-01-15 14:55:43  \n",
       "12 2024-01-15 14:55:46  \n",
       "13 2024-01-15 14:55:48  \n",
       "14 2024-01-15 14:55:52  \n",
       "15 2024-01-15 14:55:53  \n",
       "16 2024-01-15 14:55:55  \n",
       "17 2024-01-15 14:55:58  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "  read_facts()\n",
    "  .join(read_measures().rename(columns={'name': 'measure_name', 'type': 'measure_type'}), on='measure')\n",
    "  .join(read_cities().name.rename('city_name'), on='city')\n",
    "  .join(read_scrapes().timestamp.rename('scrape_timestamp'), on='scrape')\n",
    "  [['city_name','measure_name','value','meta','scrape_timestamp']]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prospective data\n",
    "### Weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "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>city</th>\n",
       "      <th>dt</th>\n",
       "      <th>T_feelslike_celsius</th>\n",
       "      <th>rain_3h_mm</th>\n",
       "      <th>snow_3h_mm</th>\n",
       "      <th>scrape_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2024-01-15 18:00:00</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2024-01-15 15:06:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2024-01-15 21:00:00</td>\n",
       "      <td>-4.42</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2024-01-16 00:00:00</td>\n",
       "      <td>-3.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>2024-01-15 15:06:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2024-01-16 03:00:00</td>\n",
       "      <td>-5.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.69</td>\n",
       "      <td>2024-01-15 15:06:53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Berlin</td>\n",
       "      <td>2024-01-16 06:00:00</td>\n",
       "      <td>-5.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2024-01-15 15:06:53</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>715</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2024-01-20 03:00:00</td>\n",
       "      <td>23.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2024-01-20 06:00:00</td>\n",
       "      <td>24.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2024-01-20 09:00:00</td>\n",
       "      <td>22.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>718</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2024-01-20 12:00:00</td>\n",
       "      <td>22.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>Sydney</td>\n",
       "      <td>2024-01-20 15:00:00</td>\n",
       "      <td>21.33</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2024-01-15 15:06:58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>720 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       city                  dt  T_feelslike_celsius  rain_3h_mm  snow_3h_mm  \\\n",
       "0    Berlin 2024-01-15 18:00:00                -3.70        0.00        0.22   \n",
       "1    Berlin 2024-01-15 21:00:00                -4.42        0.00        0.00   \n",
       "2    Berlin 2024-01-16 00:00:00                -3.90        0.00        0.71   \n",
       "3    Berlin 2024-01-16 03:00:00                -5.11        0.00        1.69   \n",
       "4    Berlin 2024-01-16 06:00:00                -5.10        0.00        0.74   \n",
       "..      ...                 ...                  ...         ...         ...   \n",
       "715  Sydney 2024-01-20 03:00:00                23.91        0.00        0.00   \n",
       "716  Sydney 2024-01-20 06:00:00                24.32        0.00        0.00   \n",
       "717  Sydney 2024-01-20 09:00:00                22.67        0.00        0.00   \n",
       "718  Sydney 2024-01-20 12:00:00                22.41        0.00        0.00   \n",
       "719  Sydney 2024-01-20 15:00:00                21.33        1.02        0.00   \n",
       "\n",
       "       scrape_timestamp  \n",
       "0   2024-01-15 15:06:53  \n",
       "1   2024-01-15 15:06:53  \n",
       "2   2024-01-15 15:06:53  \n",
       "3   2024-01-15 15:06:53  \n",
       "4   2024-01-15 15:06:53  \n",
       "..                  ...  \n",
       "715 2024-01-15 15:06:58  \n",
       "716 2024-01-15 15:06:58  \n",
       "717 2024-01-15 15:06:58  \n",
       "718 2024-01-15 15:06:58  \n",
       "719 2024-01-15 15:06:58  \n",
       "\n",
       "[720 rows x 6 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joined_weather_df = (\n",
    "  read_weather()\n",
    "  .join(read_cities().name.rename('city_name'), on='city')\n",
    "  .reset_index()\n",
    "  .drop(columns='city')\n",
    "  .rename(columns={'city_name':'city'})\n",
    "  .join(read_scrapes().timestamp.rename('scrape_timestamp'), on='scrape')\n",
    "  [['city', 'dt', 'T_feelslike_celsius', 'rain_3h_mm', 'snow_3h_mm', 'scrape_timestamp']]\n",
    ")\n",
    "joined_weather_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/til/local/opt/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1069.62x1800 with 36 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import seaborn as sns\n",
    "\n",
    "col_wrap = 3\n",
    "\n",
    "g = sns.FacetGrid(\n",
    "    data=joined_weather_df.drop(columns=[\"scrape_timestamp\"]),\n",
    "    col=\"city\",\n",
    "    col_wrap=col_wrap,\n",
    "    sharex=True,\n",
    ")\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter(\"%d-%m\"))\n",
    "name_to_color = {\n",
    "    \"temperature [°C]\": \"orange\",\n",
    "    \"rain [mm/3h]\": \"blue\",\n",
    "    \"snow [mm/3h]\": \"lightblue\",\n",
    "}\n",
    "\n",
    "patches = {k: mpatches.Patch(color=v) for k, v in name_to_color.items()}\n",
    "g = g.map(plt.plot, \"dt\", \"T_feelslike_celsius\", color=\"orange\").add_legend(patches)\n",
    "g.set(ylabel=\"°C\")\n",
    "plt.gcf().autofmt_xdate()\n",
    "\n",
    "for ax, ((i, j, k), subdata) in zip(g.axes, g.facet_data()):\n",
    "    ax2 = ax.twinx()\n",
    "    if j % col_wrap == col_wrap - 1:\n",
    "        ax2.set(ylabel=\"mm\")\n",
    "    ax2.plot(subdata[\"dt\"], subdata[\"rain_3h_mm\"], color=\"blue\")\n",
    "    ax2.plot(subdata[\"dt\"], subdata[\"snow_3h_mm\"], color=\"lightblue\")\n",
    "\n",
    "g.tight_layout()\n",
    "sns.move_legend(g, \"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc-showcode": false,
  "toc-showtags": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}