{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SW05 - Flipped Classroom  \n",
    "\n",
    "*This JupyterNotebook is intended to practice the theory alongside the slides and is used as a \"practical check of the theory input\". There are no sample solutions and the file will not be corrected and does not have to be submitted.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***   \n",
    "# Tuples  \n",
    "\n",
    "A tuple is an ordered sequence of elements that is immutable. - **non-mutable**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "2\n",
      "4\n",
      "word\n",
      "20\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "x = 20\n",
    "my_tuple = (1, 2, 4, \"word\", x)\n",
    "\n",
    "print(my_tuple[0])\n",
    "\n",
    "for telement in my_tuple:\n",
    "    print(telement)\n",
    "\n",
    "print(1 in my_tuple)\n",
    "print((1,2) in my_tuple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "my_tuple = (1, 2)\n",
    "x, y = my_tuple\n",
    "\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# round brackets could be omitted but is not recommeded\n",
    "my_tuple = 1, 2, 3\n",
    "x, y, z = my_tuple\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***  \n",
    "# Sets  \n",
    "\n",
    "Sets are unordered lists that contain each element **only once**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'pear', 'apple', 'banana', 'pineapple'}\n",
      "{'pear', 'apple', 'banana', 'pineapple'}\n",
      "{'pear', 1, 'banana', 'pineapple', 'apple'}\n"
     ]
    }
   ],
   "source": [
    "my_set = {\"apple\",\"banana\", \"pear\"}\n",
    "\n",
    "# add an item\n",
    "my_set.add(\"pineapple\")\n",
    "print(my_set)\n",
    "\n",
    "# Add same item again\n",
    "my_set.add(\"pineapple\")\n",
    "print(my_set)\n",
    "\n",
    "my_set.add(1)\n",
    "print(my_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apple', 'banana', 'pear', 'pineapple'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_set.remove(1)\n",
    "my_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"pear\" in my_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Superset und Subset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_set is superset of my_set2: True\n",
      "my_set_2 is superset of my_set: True\n"
     ]
    }
   ],
   "source": [
    "my_set = {'banana', 'pineapple', 'pear', 'apple'}\n",
    "my_set_2 = {'banana', 'pear', 'pineapple'}\n",
    "\n",
    "\n",
    "print(f\"my_set is superset of my_set2: {my_set.issuperset(my_set_2)}\")\n",
    "print(f\"my_set_2 is superset of my_set: {my_set_2.issubset(my_set)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***   \n",
    "# Dictionaries  \n",
    "\n",
    "A dictionary is a \"list\" of key-value-pairs. Like in a Set each key can only be in there once.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "kv_dict = {\"key\":\"value\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'value'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kv_dict[\"key\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'key': 'value'}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kv_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'joy'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dict with several key-value pairs\n",
    "\n",
    "my_dict = {\"b\": \"beauty\", \"j\": \"joy\"}\n",
    "my_dict\n",
    "\n",
    "my_dict[\"b\"]\n",
    "my_dict[\"j\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b': 'boy', 'j': 'joy', 'c': 'computing', 'd': 'dream'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# adding another key-value pair to my_dict\n",
    "my_dict[\"c\"] = \"computing\"\n",
    "\n",
    "# and another one\n",
    "my_dict[\"d\"] = \"dream\"\n",
    "\n",
    "# overwriting the old key \"b\"\n",
    "my_dict[\"b\"] = \"boy\"\n",
    "\n",
    "my_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dict in Dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'John': {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'}, 'James': {'Age': 25, 'Gender': 'm', 'Profession': 'Student'}, 'Peter': {'Age': 35, 'Gender': 'm', 'Profession': 'Electrician', 'Family': {}}}\n"
     ]
    }
   ],
   "source": [
    "# two elements in the dict. each one consisting of a dictionary for the key name\n",
    "my_family = {\"John\":{\n",
    "    \"Age\": 48,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Carpenter\"\n",
    "    },\n",
    "    \"James\":{\n",
    "    \"Age\": 25,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Student\",\n",
    "\n",
    "    }\n",
    "}\n",
    "\n",
    "# adding another member\n",
    "my_family[\"Peter\"] = {\n",
    "    \"Age\": 35,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Electrician\",\n",
    "    \"Family\": {},\n",
    "    }\n",
    "\n",
    "print(my_family)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accessing the value age\n",
    "my_family[\"Peter\"][\"Age\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***  \n",
    "### Exercise with my_friends dictionary  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Julia': {'age': 23,\n",
       "  'height': 1.68,\n",
       "  'gender': 'f',\n",
       "  'profession': 'nurse',\n",
       "  'relationship': 'girlfriend'},\n",
       " 'Maya': {'age': 27,\n",
       "  'height': 1.6,\n",
       "  'gender': 'd',\n",
       "  'profession': 'policewoman',\n",
       "  'relationship': 'bff'},\n",
       " 'Alex': {'age': 22,\n",
       "  'height': 1.79,\n",
       "  'gender': 'm',\n",
       "  'profession': 'priest',\n",
       "  'relationship': 'friend'}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single entry in the dict\n",
    "my_friends = {\"Julia\": {\"age\": 23, \"height\": 1.68, \"gender\": \"f\", \"profession\": \"nurse\", \"relationship\": \"girlfriend\"}, \n",
    "\"Maya\": {\"age\": 27, \"height\": 1.60, \"gender\": \"d\", \"profession\": \"policewoman\", \"relationship\": \"bff\"}}\n",
    "\n",
    "# add another friend\n",
    "my_friends[\"Alex\"] = {\"age\": 22, \"height\": 1.79, \"gender\": \"m\", \"profession\": \"priest\", \"relationship\": \"friend\"}\n",
    "my_friends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "27\n",
      "22\n"
     ]
    }
   ],
   "source": [
    "for friends in my_friends:\n",
    "    print(my_friends[friends][\"age\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***  \n",
    "### Dict Funktionen: len() and .items()  \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# the \"length\" of a dictionary\n",
    "print(len(my_family))\n",
    "print(len(my_family[\"John\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'John': {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'}, 'James': {'Age': 25, 'Gender': 'm', 'Profession': 'Student'}, 'Peter': {'Age': 35, 'Gender': 'm', 'Profession': 'Electrician', 'Family': {}}}\n"
     ]
    }
   ],
   "source": [
    "# two elements in the dict. each one consisting of a dictionary for the key name\n",
    "my_family = {\"John\":{\n",
    "    \"Age\": 48,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Carpenter\"\n",
    "    },\n",
    "    \"James\":{\n",
    "    \"Age\": 25,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Student\",\n",
    "\n",
    "    }\n",
    "}\n",
    "\n",
    "# adding another member\n",
    "my_family[\"Peter\"] = {\n",
    "    \"Age\": 35,\n",
    "    \"Gender\": \"m\",\n",
    "    \"Profession\":\"Electrician\",\n",
    "    \"Family\": {},\n",
    "    }\n",
    "\n",
    "print(my_family)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "John\n",
      "James\n",
      "Peter\n"
     ]
    }
   ],
   "source": [
    "for key in my_family:\n",
    "    print(key)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "John: {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'}\n",
      "James: {'Age': 25, 'Gender': 'm', 'Profession': 'Student'}\n",
      "Peter: {'Age': 35, 'Gender': 'm', 'Profession': 'Electrician', 'Family': {}}\n"
     ]
    }
   ],
   "source": [
    "# item() funktion to access key and value\n",
    "for key, value in my_family.items():\n",
    "    print(f\"{key}: {value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "John: Age: 48\n",
      "John: Gender: m\n",
      "John: Profession: Carpenter\n",
      "James: Age: 25\n",
      "James: Gender: m\n",
      "James: Profession: Student\n",
      "Peter: Age: 35\n",
      "Peter: Gender: m\n",
      "Peter: Profession: Electrician\n",
      "Peter: Family: {}\n"
     ]
    }
   ],
   "source": [
    "# loop in each dict\n",
    "for key, value in my_family.items():\n",
    "    for k, v in value.items():\n",
    "        print(f\"{key}: {k}: {v}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{  'James': {'Age': 25, 'Gender': 'm', 'Profession': 'Student'},\n",
      "   'John': {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'},\n",
      "   'Peter': {  'Age': 35,\n",
      "               'Family': {},\n",
      "               'Gender': 'm',\n",
      "               'Profession': 'Electrician'}}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "pp = pprint.PrettyPrinter(indent=3, depth=3)\n",
    "\n",
    "pp.pprint(my_family)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***  \n",
    "# JSON  \n",
    "\n",
    "to save dictionaries, standard format for data exchange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'John': {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'}, 'James': {'Age': 25, 'Gender': 'm', 'Profession': 'Student'}, 'Peter': {'Age': 35, 'Gender': 'm', 'Profession': 'Electrician', 'Family': {}}}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "print(my_family)\n",
    "# öffnen eines Files\n",
    "with open(file=\"my_family.json\",mode = \"w+\") as fp:\n",
    "\n",
    "    # speichen (schreiben) des Dicts in das File\n",
    "    json.dump(obj = my_family,fp = fp, indent= 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "reading a json file back into a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{  'James': {'Age': 25, 'Gender': 'm', 'Profession': 'Student'},\n",
      "   'John': {'Age': 48, 'Gender': 'm', 'Profession': 'Carpenter'},\n",
      "   'Peter': {  'Age': 35,\n",
      "               'Family': {},\n",
      "               'Gender': 'm',\n",
      "               'Profession': 'Electrician'}}\n"
     ]
    }
   ],
   "source": [
    "# öffnen des JSON files\n",
    "import pprint\n",
    "pp = pprint.PrettyPrinter(depth=3, indent = 3)\n",
    "\n",
    "with open(file=\"my_family.json\", mode = \"r+\") as fp:\n",
    "    # auslesen des Dicts\n",
    "    my_read_dict = json.load(fp=fp)\n",
    "\n",
    "pp.pprint(my_read_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***  \n",
    "### JSON and API  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Response [200]>\n",
      "{  'date': '2024-10-17',\n",
      "   'lat': 8.30635,\n",
      "   'lon': 47.05048,\n",
      "   'tz': '+03:00',\n",
      "   'units': 'metric',\n",
      "   'weather_overview': 'Currently, the weather is partly cloudy with a '\n",
      "                       'temperature of 30°C and a slight breeze with a wind '\n",
      "                       'speed of 4 meters per second. The humidity is at 40%, '\n",
      "                       'and the visibility is at 10,000 meters. The '\n",
      "                       'atmospheric pressure is at 1013 hPa with a dew point '\n",
      "                       'of 15°C. The UV index is at a high level of 8, so it '\n",
      "                       'is important to take precautions when spending time '\n",
      "                       'outdoors. Overall, it is a pleasant day with '\n",
      "                       'comfortable temperatures and some scattered clouds in '\n",
      "                       'the sky. If you plan to be outside, be mindful of the '\n",
      "                       'moderate wind speed and high UV index.'}\n",
      "\n",
      "Weather in Luzern:  Currently, the weather is partly cloudy with a temperature of 30°C and a slight breeze with a wind speed of 4 meters per second. The humidity is at 40%, and the visibility is at 10,000 meters. The atmospheric pressure is at 1013 hPa with a dew point of 15°C. The UV index is at a high level of 8, so it is important to take precautions when spending time outdoors. Overall, it is a pleasant day with comfortable temperatures and some scattered clouds in the sky. If you plan to be outside, be mindful of the moderate wind speed and high UV index.\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import pprint\n",
    "pp = pprint.PrettyPrinter(depth=3, indent = 3)\n",
    "\n",
    "# city Luzern\n",
    "city = \"Luzern\"\n",
    "lon = 47.05048 \n",
    "lat = 8.30635\n",
    "\n",
    "# more info under: www.openweathermap.org, the api_key is retrieved from the website after signing up\n",
    "api_key = \"f19b10b50f6eeed1504adad1b2a8982c\"\n",
    "url = f\"https://api.openweathermap.org/data/3.0/onecall/overview?lat={lat}&lon={lon}&appid={api_key}&units=metric\"\n",
    "\n",
    "response = requests.get(url)\n",
    "\n",
    "print(response)\n",
    "\n",
    "# Checking if the request was successful\n",
    "if response.status_code == 200:\n",
    "    # Parsing the response as JSON\n",
    "    data = response.json()\n",
    "    pp.pprint(data)  # Printing the response data\n",
    "else:\n",
    "    print(f\"Error: {response.status_code}\")\n",
    "\n",
    "# printing the extracted text from the dictionary\n",
    "print(f\"\\nWeather in {city}: \", data[\"weather_overview\"])"
   ]
  }
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