draft 2 DnD_Dataset_Engineering.ipynb

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      "name": "python3",
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    "language_info": {
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/jbloewencolon/Analyzing-Bergson/blob/main/draft%202%20DnD_Dataset_Engineering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "73bliwpoih3X"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import random"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the file path (it seems you've already specified it)\n",
        "file_path = '/content/drive/MyDrive/Colab Notebooks/DnD/fictional_data.xlsx'\n",
        "\n",
        "# Read the xlsx file\n",
        "df = pd.read_excel(file_path)\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "id": "lG23fVQLs4JY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5680a12e-9bff-43b3-9b11-c53c1225d5e9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 0 entries\n",
            "Data columns (total 39 columns):\n",
            " #   Column                              Non-Null Count  Dtype \n",
            "---  ------                              --------------  ----- \n",
            " 0   City                                0 non-null      object\n",
            " 1   Region                              0 non-null      object\n",
            " 2   Nation                              0 non-null      object\n",
            " 3   Government                          0 non-null      object\n",
            " 4   Percentage of population Humans     0 non-null      object\n",
            " 5   Percentage of population Halflings  0 non-null      object\n",
            " 6   Percentage of population Orcs       0 non-null      object\n",
            " 7   Percentage of population Elves      0 non-null      object\n",
            " 8   Percentage of population Dwarves    0 non-null      object\n",
            " 9   Percentage of population Gnomes     0 non-null      object\n",
            " 10  Average Age of Population           0 non-null      object\n",
            " 11  Typical Cause of Death              0 non-null      object\n",
            " 12  Wizarding School                    0 non-null      object\n",
            " 13  Active Economy                      0 non-null      object\n",
            " 14  Hidden Economy                      0 non-null      object\n",
            " 15  Known Treasure Value                0 non-null      object\n",
            " 16  Rumored Treasure Value              0 non-null      object\n",
            " 17  Major Industries                    0 non-null      object\n",
            " 18  Tax Rate                            0 non-null      object\n",
            " 19  Major Resources                     0 non-null      object\n",
            " 20  # of Landowners                     0 non-null      object\n",
            " 21  # Homeowners                        0 non-null      object\n",
            " 22  Average Rent                        0 non-null      object\n",
            " 23  # of Peasants                       0 non-null      object\n",
            " 24  Average Debt by Household           0 non-null      object\n",
            " 25  # of Nobility                       0 non-null      object\n",
            " 26  # of Active Adventurers             0 non-null      object\n",
            " 27  Number of Active Wizards            0 non-null      object\n",
            " 28  Dragon Sightings                    0 non-null      object\n",
            " 29  Environmental Climate               0 non-null      object\n",
            " 30  Average Temperature                 0 non-null      object\n",
            " 31  Average Rainfall                    0 non-null      object\n",
            " 32  Political Climate                   0 non-null      object\n",
            " 33  Magical Climate                     0 non-null      object\n",
            " 34  Likelihood of Getting Cursed        0 non-null      object\n",
            " 35  Danger Level                        0 non-null      object\n",
            " 36  Openness to Strangers               0 non-null      object\n",
            " 37  Notable Landmarks                   0 non-null      object\n",
            " 38  Fate ranking                        0 non-null      object\n",
            "dtypes: object(39)\n",
            "memory usage: 0.0+ bytes\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Create a list of city names\n",
        "city_names = [\n",
        "    \"Abriymoch\", \"Aelor\", \"Ageadren\", \"Airspur\", \"Airspur (Chessenta)\", \"Akanax\",\n",
        "    \"Akkaido\", \"Al-Anwahr\", \"Alashan\", \"Almraiven\", \"Ammathluir\", \"Ammathtar\",\n",
        "    \"Anaxadren\", \"Araugul\", \"Ariel-than\", \"Asple\", \"Assam\", \"Athkatla\",\n",
        "    \"Aventine\", \"Azumar\", \"Balanzia\", \"Baldur's Gate\", \"Beluir\", \"Beradandar\",\n",
        "    \"Berydren\", \"Bhutan\", \"Bidnop So\", \"Blingdenstone\", \"Bral\", \"Branestria\",\n",
        "    \"Callidae\", \"Calimport\", \"Castle Darkhold\", \"Cathyr\", \"Cedarsproke\", \"Chaoyang\",\n",
        "    \"Chaul'mur'ssin\", \"Chavyondat\", \"Ched Nasad\", \"Cheju\", \"Choshein\", \"Chrysalis\",\n",
        "    \"Chunming\", \"Chunming (T'u Lung)\", \"Cimbar\", \"Citadel of Ice and Steel\",\n",
        "    \"Citadel of Ten Thousand Pearls\", \"City of Brass\", \"City of Caverns\", \"City of Glass\",\n",
        "    \"City of the Werecrocodiles\", \"Clorlyth'na'tsheen\", \"Corrant\", \"Dalekeva\",\n",
        "    \"Dalelost\", \"Darka\", \"Darmshall\", \"Darrawn\", \"Dashadjen\", \"Dauntshield\",\n",
        "    \"Dawnfire\", \"Deepearth\", \"Delia\", \"Delselar\", \"Delzimmer\", \"Derlusk\",\n",
        "    \"Dhinnilith\", \"Dilpur\", \"Djerad Kethendi\", \"Djerad Kusold\", \"Dloemen\", \"Dojyu\",\n",
        "    \"Dolblunde\", \"Dralpur\", \"Drelagara\", \"Duhlnarim\", \"Dunspeirrin\", \"Dusklan\",\n",
        "    \"Dyon G'ennivalz\", \"Elgrin Fau\", \"Ereshkigal\", \"Evereska\", \"Everlund\", \"Evernight\",\n",
        "    \"Eylea\", \"Faenasuor\", \"Fatula Chupa\", \"Fij\", \"Fluvenilstra\", \"Flyndagol\",\n",
        "    \"Fochu\", \"Foo Nakar\", \"Forharn\", \"Fukiow\", \"Gharreil\", \"Gheldaneth\",\n",
        "    \"Gifu\", \"Gizekhtet (city)\", \"Glister\", \"Gloomwrought\", \"Gnarhelm (city)\", \"Goliad\",\n",
        "    \"Gontal City\", \"Guallidurth\", \"Gundbarg\", \"Gurdeth\", \"Halagard\", \"Hammerstaad\",\n",
        "    \"Harglast\", \"Haunvaerune\", \"Hazuth\", \"Helbrester\", \"Heldapan\", \"Helgabal\",\n",
        "    \"Helm's Hold\", \"Herath\", \"Hethar\", \"Hilbrand\", \"Hisari\", \"Hlammach\",\n",
        "    \"Huorm\", \"Iiso\", \"Ilimar\", \"Iliphanar\", \"Illul\", \"Iltkazar\",\n",
        "    \"Imdolphyn\", \"Immurk's Hold\", \"In'ani\", \"Inupras\", \"Iriaebor\", \"Ironfang Deep\",\n",
        "    \"Ironspur\", \"Ithmong (Lapaliiya)\", \"Ixtzul\", \"Jangling Hiter\", \"Jasuga\", \"Jhachalkhyn\",\n",
        "    \"Jhalhoran\", \"Jhothûn (city)\", \"Joya\", \"Jugicha\", \"Kanaglym\", \"Karak\",\n",
        "    \"Karffbadh\", \"Kaspar\", \"Keltar\", \"Khaerbaal\", \"Khampo\", \"Koje\",\n",
        "    \"Konang Pen\", \"Konigheim\", \"Kormul\", \"Kourmira\", \"Kressilacc\", \"Kuda\",\n",
        "    \"Kuo Te' Lung\", \"Kushk\", \"Lasdur\", \"Lheshayl\", \"Lipo\",\n",
        "    \"Lith My'athar\", \"Llorbauth\", \"Llurth Dreier\", \"Lothen of the Silver Spires\", \"Luenath\", \"Lundeth (Thindol)\",\n",
        "    \"Lushpool\", \"Luskan\", \"Lylorn\", \"Maerlar\", \"Malduir\", \"Malsheem\",\n",
        "    \"Malydren\", \"Manass\", \"Marabaya\", \"Marangor\", \"Marrauk\", \"Maxal\",\n",
        "    \"Melvaunt\", \"Messemprar\", \"Mhairhetel\", \"Mictlatepec\", \"Mithral Hall\",\n",
        "    \"Minauros (city)\", \"Mirabar\", \"Mordulkin\", \"Morvar\", \"Motra\", \"Mreyelundar\",\n",
        "    \"Mulmaster\", \"Multum\", \"Mussum\", \"Myrmyr\", \"Myth Adofhaer\", \"Myth Drannor\",\n",
        "    \"Myth Nantar\", \"Nakamaru\", \"Narubel\", \"Nathong\", \"Neldorild\", \"Nesmé\",\n",
        "    \"Nethjet\", \"Nethra\", \"Neverwinter\", \"New Sarshel\", \"Nivian\", \"Nonthal\",\n",
        "    \"Northkeep\", \"Nycopolis\", \"Obbolor\", \"Oberon's Hold\", \"Occidian\", \"Oeble\",\n",
        "    \"Ojy-do\", \"Okahira\", \"Old Stone Keep\", \"Old Vaelen\", \"Omu\", \"Ooltul\",\n",
        "    \"Opawangchicheng\", \"Orlumbor\", \"Ormath\", \"Ormpetarr\", \"Ornthalas\", \"Oryndoll\",\n",
        "    \"Osel\", \"Ossantil\", \"Oth\", \"Outentown\", \"Ozryn\", \"Paolan\",\n",
        "    \"Paraelemental City of Conflagration\", \"Paraelemental City of Deluge\", \"Paraelemental City of Ooze\",\n",
        "    \"Paraelemental City of Sleet\", \"Phandalin\", \"Phannaskul\",\n",
        "    \"Phaervorul\", \"Phrenopolis\", \"Piaoliang\", \"Pinebluff\", \"Pirth City\", \"Piyapon\",\n",
        "    \"Plague-Mort\", \"Plotsklan\", \"Port Castigliar\", \"Port Llast\", \"Port Nyanzaru\", \"Port of Ravens Bluff\",\n",
        "    \"Poytakht\", \"Priapurl\", \"Procampur\", \"Purskul\", \"Pyarados\", \"Q'barra\",\n",
        "    \"Q'Xorlarrin\", \"Qazat\", \"Quaervarr\", \"Quarmall\", \"Qu'ellar d'Uzunt\", \"Qu'ellarz'orl\",\n",
        "    \"Radocan\", \"Raurokh\", \"Raven's Bluff\", \"Razalpartha\", \"Rekkar-Sarrat\", \"Relkath's Foot\",\n",
        "    \"Reth\", \"Revonar\", \"Riatavin\", \"Rilauven\", \"Ruathym (city)\", \"Ruldoer\",\n",
        "    \"Rylatar\", \"Saelmur\", \"Sakkors\", \"Saradush\", \"Sardolphyn\", \"Sarth\",\n",
        "    \"Sattar\", \"Scardale\", \"Schamedar\", \"Schorin\", \"Secomber\", \"Sekras\",\n",
        "    \"Selgaunt\", \"Semkhrun\", \"Sepech\", \"Serôs (city)\", \"Setma'antar\", \"Sethlapurkha\",\n",
        "    \"Shaboath\", \"Shade (city)\", \"Shadusk\", \"Shangban\", \"Shaundalar\", \"Sheirtalar\",\n",
        "    \"Shi-Kuo\", \"Shindikuur\", \"Shoonach\", \"Shu Pao\", \"Shussel\", \"Sildeyuir\",\n",
        "    \"Siluvanede\", \"Simbia\", \"Sintharla\", \"Sjorndorn\", \"Skullport\", \"Skuld\",\n",
        "    \"Soorenar\", \"Sschindylryn\", \"Sshamath\", \"Stornanter\", \"Sudar\", \"Sundabar\",\n",
        "    \"Sunduluth\", \"Sungping\", \"Surkh\", \"Suzail\", \"Svartalfheim\", \"Taejin\",\n",
        "    \"Tagorlad\", \"Takalai\", \"Takar\", \"Talab\", \"Talath Passar\", \"Talath Seren\",\n",
        "    \"Talbot\", \"Talhund\", \"Talinie (city)\", \"Tammar\", \"Tantras\", \"Tasseldale\",\n",
        "    \"Tchazzar\", \"Telantiwar\", \"Telflamm\", \"Telnarquel\", \"Telos City\", \"Telsorn\",\n",
        "    \"Temar\", \"Termalaine\", \"Tethgard\", \"Thallar\", \"Thamor\", \"Thangorodrim\",\n",
        "    \"Tharqualnaar\", \"Thayd\", \"Thelnam\", \"Thentia\", \"Thesk\", \"Thulanth\",\n",
        "    \"Thultanthar\", \"Thundarlun\", \"Thundertree\", \"Thyat\", \"Tibet\", \"Tilverton\",\n",
        "    \"Tith Tilendrothael\", \"Torsil\", \"Triboar\", \"Triel\", \"Tsurlagol\", \"Tu Pe\",\n",
        "    \"Tulmon\", \"Tun Mi Lung\", \"Tuncer\", \"Tunfeng\", \"Tunzel\", \"Turmish (city)\",\n",
        "    \"Twilight Hall\", \"Two Stars\", \"Tyar-Besil\", \"Ulgarth (city)\", \"Ulgarstat\", \"Umbergoth\",\n",
        "    \"Umbraer\", \"Urkhar\", \"Urossos\", \"Uthmere\", \"Uzurr\", \"Vaasa (city)\",\n",
        "    \"Valjevo Castle\", \"Vangal\", \"Velegrar\", \"Vilhon Reach\", \"Volothamp\", \"Voonlar\",\n",
        "    \"Vor Rukoth\", \"V'ral\", \"Waterdeep\", \"Weeping War (city)\", \"Westgate\", \"Wheloon\",\n",
        "    \"Xarcorr\", \"Xinlenal\", \"Xonthal's Tower\", \"Xynnar\", \"Yal Tengri\", \"Yaulazna\",\n",
        "    \"Yhaunn\", \"Yhro 'Lamith\", \"Ylraphon\", \"Yuirwood\", \"Yunn Shicheng\", \"Za Si Tsan\",\n",
        "    \"Zazesspur\", \"Zhentil Keep\", \"Zhenul\", \"Zindalankh\", \"Zokir\"\n",
        "]\n",
        "\n",
        "# Add the new city names to the \"City\" column\n",
        "df['City'] = city_names\n",
        "\n",
        "# Save the updated DataFrame back to the Excel file\n",
        "df.to_excel('/content/drive/MyDrive/Colab Notebooks/DnD/fictional_data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "1enbyOOqo5hF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Generating random data for each column\n",
        "\n",
        "df['Region'] = np.random.choice(['Region1', 'Region2', 'Region3'], size=len(city_names))\n",
        "df['Nation'] = np.random.choice(['Nation1', 'Nation2', 'Nation3'], size=len(city_names))\n",
        "df['Population of City'] = np.random.randint(1000, 100000, size=len(city_names))\n",
        "df['Government'] = np.random.choice(['Democracy', 'Monarchy', 'Oligarchy'], size=len(city_names))\n",
        "\n",
        "# Generating random percentage data (between 0 and 100)\n",
        "percentage_columns = [\n",
        "    'Percentage of population Humans', 'Percentage of population Halflings',\n",
        "    'Percentage of population Orcs', 'Percentage of population Elves',\n",
        "    'Percentage of population Dwarves', 'Percentage of population Gnomes'\n",
        "]\n",
        "for col in percentage_columns:\n",
        "    df[col] = np.random.uniform(0, 100, size=len(city_names))\n",
        "\n",
        "df['Average Age of Population'] = np.random.uniform(20, 80, size=len(city_names))\n",
        "df['Typical Cause of Death'] = np.random.choice(['Old Age', 'Disease', 'Accident'], size=len(city_names))\n",
        "df['Wizarding School'] = np.random.choice(['Yes', 'No'], size=len(city_names))\n",
        "\n",
        "# Generating random financial data (between 1000 and 1 million)\n",
        "financial_columns = [\n",
        "    'Active Economy (Gold)', 'Hidden Economy (Gold)',\n",
        "    'Known Treasure Value', 'Rumored Treasure Value'\n",
        "]\n",
        "for col in financial_columns:\n",
        "    df[col] = np.random.uniform(1000, 1000000, size=len(city_names))\n",
        "\n",
        "df['Tax Rate'] = np.random.uniform(0, 50, size=len(city_names))\n",
        "\n",
        "# Generating random list of strings for industries and resources\n",
        "industries = ['Mining', 'Farming', 'Craftsmanship', 'Trading', 'Magic Goods']\n",
        "resources = ['Gold', 'Silver', 'Wood', 'Stone', 'Magic Herbs']\n",
        "df['Major Industries'] = [np.random.choice(industries, size=np.random.randint(1, len(industries)), replace=False).tolist() for _ in range(len(city_names))]\n",
        "df['Major Resources'] = [np.random.choice(resources, size=np.random.randint(1, len(resources)), replace=False).tolist() for _ in range(len(city_names))]\n",
        "\n",
        "# Generating random integer data for various columns\n",
        "integer_columns = [\n",
        "    '# of Landowners', '# Homeowners', 'Average Rent',\n",
        "    '# of Peasants', 'Average Debt by Household',\n",
        "    '# of Nobility', '# of Active Adventurers',\n",
        "    'Number of Active Wizards', 'Dragon Sightings', 'Fate ranking'\n",
        "]\n",
        "for col in integer_columns:\n",
        "    df[col] = np.random.randint(1, 1000, size=len(city_names))\n",
        "\n",
        "# Generating random float data for climate and other factors\n",
        "float_columns = [\n",
        "    'Environmental Climate', 'Average Temperature',\n",
        "    'Average Rainfall', 'Political Climate',\n",
        "    'Magical Climate', 'Likelihood of Getting Cursed',\n",
        "    'Danger Level', 'Openness to Strangers'\n",
        "]\n",
        "for col in float_columns:\n",
        "    df[col] = np.random.uniform(0, 100, size=len(city_names))\n",
        "\n",
        "# Generating random strings for landmarks\n",
        "df['Notable Landmarks'] = np.random.choice(['Landmark1', 'Landmark2', 'Landmark3'], size=len(city_names))\n",
        "\n",
        "# Save the updated DataFrame back to the Excel file\n",
        "df.to_excel('/content/drive/MyDrive/Colab Notebooks/DnD/fictional_data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "Bmbeksd2yqLn"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "3ho0w3wJy4Wf",
        "outputId": "0e737c26-6817-445a-fda4-cb08327ebd3d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  City   Region   Nation Government  \\\n",
              "0            Abriymoch  Region2  Nation2  Oligarchy   \n",
              "1                Aelor  Region2  Nation3  Oligarchy   \n",
              "2             Ageadren  Region3  Nation2  Oligarchy   \n",
              "3              Airspur  Region2  Nation3  Oligarchy   \n",
              "4  Airspur (Chessenta)  Region1  Nation3  Oligarchy   \n",
              "\n",
              "  Percentage of population Humans   Percentage of population Halflings  \\\n",
              "0                              NaN                            7.904120   \n",
              "1                              NaN                            1.607618   \n",
              "2                              NaN                           54.610246   \n",
              "3                              NaN                           73.721184   \n",
              "4                              NaN                           49.656559   \n",
              "\n",
              "   Percentage of population Orcs  Percentage of population Elves  \\\n",
              "0                      71.520153                       80.264236   \n",
              "1                      11.346570                       68.282074   \n",
              "2                      89.830203                       74.545746   \n",
              "3                      42.047838                       30.094798   \n",
              "4                      84.705312                       85.098654   \n",
              "\n",
              "   Percentage of population Dwarves  Percentage of population Gnomes  ...  \\\n",
              "0                         10.024334                        47.980872  ...   \n",
              "1                         80.864470                        67.145963  ...   \n",
              "2                         30.843258                        46.262019  ...   \n",
              "3                         48.466543                        90.320646  ...   \n",
              "4                         94.914073                        70.351005  ...   \n",
              "\n",
              "   Percentage of population Humans Active Economy (Gold)  \\\n",
              "0                        92.890664         583410.311820   \n",
              "1                        76.311884          36674.057147   \n",
              "2                        12.111212         728946.133156   \n",
              "3                        85.766203         106973.460556   \n",
              "4                         9.291475         516943.002801   \n",
              "\n",
              "  Hidden Economy (Gold) Rumored Treasure Value  \\\n",
              "0         520865.803745          487455.676203   \n",
              "1         882756.376368          708026.851881   \n",
              "2         221949.152649          634693.311657   \n",
              "3         818775.672737          196989.076723   \n",
              "4         119086.542533          150248.340973   \n",
              "\n",
              "                    Major Resources  # of Active Adventurers  \\\n",
              "0       [Gold, Stone, Silver, Wood]                      898   \n",
              "1             [Silver, Magic Herbs]                      821   \n",
              "2  [Wood, Magic Herbs, Stone, Gold]                      957   \n",
              "3                           [Stone]                      458   \n",
              "4      [Silver, Stone, Magic Herbs]                      140   \n",
              "\n",
              "  Environmental Climate Likelihood of Getting Cursed  Danger Level  \\\n",
              "0             21.149629                    36.113485     90.531226   \n",
              "1             85.989144                    33.064317     86.386139   \n",
              "2             41.153760                    94.057346     57.030169   \n",
              "3             47.134302                    46.155325     35.290176   \n",
              "4             65.841312                    12.120318     74.095791   \n",
              "\n",
              "  Notable Landmarks  \n",
              "0         Landmark1  \n",
              "1         Landmark3  \n",
              "2         Landmark3  \n",
              "3         Landmark2  \n",
              "4         Landmark1  \n",
              "\n",
              "[5 rows x 50 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Region</th>\n",
              "      <th>Nation</th>\n",
              "      <th>Government</th>\n",
              "      <th>Percentage of population Humans</th>\n",
              "      <th>Percentage of population Halflings</th>\n",
              "      <th>Percentage of population Orcs</th>\n",
              "      <th>Percentage of population Elves</th>\n",
              "      <th>Percentage of population Dwarves</th>\n",
              "      <th>Percentage of population Gnomes</th>\n",
              "      <th>...</th>\n",
              "      <th>Percentage of population Humans</th>\n",
              "      <th>Active Economy (Gold)</th>\n",
              "      <th>Hidden Economy (Gold)</th>\n",
              "      <th>Rumored Treasure Value</th>\n",
              "      <th>Major Resources</th>\n",
              "      <th># of Active Adventurers</th>\n",
              "      <th>Environmental Climate</th>\n",
              "      <th>Likelihood of Getting Cursed</th>\n",
              "      <th>Danger Level</th>\n",
              "      <th>Notable Landmarks</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abriymoch</td>\n",
              "      <td>Region2</td>\n",
              "      <td>Nation2</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.904120</td>\n",
              "      <td>71.520153</td>\n",
              "      <td>80.264236</td>\n",
              "      <td>10.024334</td>\n",
              "      <td>47.980872</td>\n",
              "      <td>...</td>\n",
              "      <td>92.890664</td>\n",
              "      <td>583410.311820</td>\n",
              "      <td>520865.803745</td>\n",
              "      <td>487455.676203</td>\n",
              "      <td>[Gold, Stone, Silver, Wood]</td>\n",
              "      <td>898</td>\n",
              "      <td>21.149629</td>\n",
              "      <td>36.113485</td>\n",
              "      <td>90.531226</td>\n",
              "      <td>Landmark1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aelor</td>\n",
              "      <td>Region2</td>\n",
              "      <td>Nation3</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.607618</td>\n",
              "      <td>11.346570</td>\n",
              "      <td>68.282074</td>\n",
              "      <td>80.864470</td>\n",
              "      <td>67.145963</td>\n",
              "      <td>...</td>\n",
              "      <td>76.311884</td>\n",
              "      <td>36674.057147</td>\n",
              "      <td>882756.376368</td>\n",
              "      <td>708026.851881</td>\n",
              "      <td>[Silver, Magic Herbs]</td>\n",
              "      <td>821</td>\n",
              "      <td>85.989144</td>\n",
              "      <td>33.064317</td>\n",
              "      <td>86.386139</td>\n",
              "      <td>Landmark3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ageadren</td>\n",
              "      <td>Region3</td>\n",
              "      <td>Nation2</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>54.610246</td>\n",
              "      <td>89.830203</td>\n",
              "      <td>74.545746</td>\n",
              "      <td>30.843258</td>\n",
              "      <td>46.262019</td>\n",
              "      <td>...</td>\n",
              "      <td>12.111212</td>\n",
              "      <td>728946.133156</td>\n",
              "      <td>221949.152649</td>\n",
              "      <td>634693.311657</td>\n",
              "      <td>[Wood, Magic Herbs, Stone, Gold]</td>\n",
              "      <td>957</td>\n",
              "      <td>41.153760</td>\n",
              "      <td>94.057346</td>\n",
              "      <td>57.030169</td>\n",
              "      <td>Landmark3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Airspur</td>\n",
              "      <td>Region2</td>\n",
              "      <td>Nation3</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>73.721184</td>\n",
              "      <td>42.047838</td>\n",
              "      <td>30.094798</td>\n",
              "      <td>48.466543</td>\n",
              "      <td>90.320646</td>\n",
              "      <td>...</td>\n",
              "      <td>85.766203</td>\n",
              "      <td>106973.460556</td>\n",
              "      <td>818775.672737</td>\n",
              "      <td>196989.076723</td>\n",
              "      <td>[Stone]</td>\n",
              "      <td>458</td>\n",
              "      <td>47.134302</td>\n",
              "      <td>46.155325</td>\n",
              "      <td>35.290176</td>\n",
              "      <td>Landmark2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Airspur (Chessenta)</td>\n",
              "      <td>Region1</td>\n",
              "      <td>Nation3</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>NaN</td>\n",
              "      <td>49.656559</td>\n",
              "      <td>84.705312</td>\n",
              "      <td>85.098654</td>\n",
              "      <td>94.914073</td>\n",
              "      <td>70.351005</td>\n",
              "      <td>...</td>\n",
              "      <td>9.291475</td>\n",
              "      <td>516943.002801</td>\n",
              "      <td>119086.542533</td>\n",
              "      <td>150248.340973</td>\n",
              "      <td>[Silver, Stone, Magic Herbs]</td>\n",
              "      <td>140</td>\n",
              "      <td>65.841312</td>\n",
              "      <td>12.120318</td>\n",
              "      <td>74.095791</td>\n",
              "      <td>Landmark1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 50 columns</p>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TIosHo18q_cn",
        "outputId": "67e2b724-2935-48ed-cab1-cf96e1798135"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 411 entries, 0 to 410\n",
            "Data columns (total 40 columns):\n",
            " #   Column                              Non-Null Count  Dtype  \n",
            "---  ------                              --------------  -----  \n",
            " 0   City                                411 non-null    object \n",
            " 1   Region                              411 non-null    object \n",
            " 2   Nation                              411 non-null    object \n",
            " 3   Population of City                  411 non-null    int64  \n",
            " 4   Government                          411 non-null    object \n",
            " 5   Percentage of population Humans     411 non-null    float64\n",
            " 6   Percentage of population Halflings  411 non-null    float64\n",
            " 7   Percentage of population Orcs       411 non-null    float64\n",
            " 8   Percentage of population Elves      411 non-null    float64\n",
            " 9   Percentage of population Dwarves    411 non-null    float64\n",
            " 10  Percentage of population Gnomes     411 non-null    float64\n",
            " 11  Average Age of Population           411 non-null    float64\n",
            " 12  Typical Cause of Death              411 non-null    object \n",
            " 13  Wizarding School                    411 non-null    object \n",
            " 14  Active Economy (Gold)               411 non-null    float64\n",
            " 15  Hidden Economy (Gold)               411 non-null    float64\n",
            " 16  Known Treasure Value                411 non-null    float64\n",
            " 17  Rumored Treasure Value              411 non-null    float64\n",
            " 18  Tax Rate                            411 non-null    float64\n",
            " 19  Major Industries                    411 non-null    object \n",
            " 20  Major Resources                     411 non-null    object \n",
            " 21  # of Landowners                     411 non-null    int64  \n",
            " 22  # Homeowners                        411 non-null    int64  \n",
            " 23  Average Rent                        411 non-null    int64  \n",
            " 24  # of Peasants                       411 non-null    int64  \n",
            " 25  Average Debt by Household           411 non-null    int64  \n",
            " 26  # of Nobility                       411 non-null    int64  \n",
            " 27  # of Active Adventurers             411 non-null    int64  \n",
            " 28  Number of Active Wizards            411 non-null    int64  \n",
            " 29  Dragon Sightings                    411 non-null    int64  \n",
            " 30  Environmental Climate               411 non-null    float64\n",
            " 31  Average Temperature                 411 non-null    float64\n",
            " 32  Average Rainfall                    411 non-null    float64\n",
            " 33  Political Climate                   411 non-null    float64\n",
            " 34  Magical Climate                     411 non-null    float64\n",
            " 35  Likelihood of Getting Cursed        411 non-null    float64\n",
            " 36  Danger Level                        411 non-null    float64\n",
            " 37  Openness to Strangers               411 non-null    float64\n",
            " 38  Notable Landmarks                   411 non-null    object \n",
            " 39  Fate ranking                        411 non-null    int64  \n",
            "dtypes: float64(20), int64(11), object(9)\n",
            "memory usage: 128.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame back to the Excel file\n",
        "df.to_excel('/content/drive/MyDrive/Colab Notebooks/DnD/fictional_data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "VqQn9sNbsxu2"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Setting Total Population of Faerun\n",
        "total_population = np.random.randint(66e6, 70e6)\n",
        "cities = 411  # Number of cities\n",
        "city_population = np.random.randint(100000, 300000, size=cities)\n",
        "city_population = (city_population / city_population.sum()) * total_population\n",
        "\n",
        "# Step 2: Setting Species Population Percentage for each city\n",
        "city_data = pd.DataFrame()\n",
        "city_data['City Population'] = city_population.astype(int)\n",
        "city_data['Humans'] = np.random.uniform(0.78, 0.92, cities) * city_data['City Population']\n",
        "city_data['Halflings'] = np.random.uniform(0.05, 0.06, cities) * city_data['City Population']\n",
        "city_data['Dwarves'] = np.random.uniform(0.03, 0.04, cities) * city_data['City Population']\n",
        "city_data['Elves'] = np.random.uniform(0.02, 0.03, cities) * city_data['City Population']\n",
        "city_data['Orcs'] = np.random.uniform(0.02, 0.03, cities) * city_data['City Population']\n",
        "city_data['Gnomes'] = np.random.uniform(0.01, 0.02, cities) * city_data['City Population']\n",
        "\n",
        "# Convert them to integers\n",
        "species_columns = ['Humans', 'Halflings', 'Dwarves', 'Elves', 'Orcs', 'Gnomes']\n",
        "city_data[species_columns] = city_data[species_columns].astype(int)\n",
        "\n",
        "# Print first few rows to check\n",
        "print(city_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QHI01FJys4je",
        "outputId": "57d955c4-4a97-4ffb-bf8f-61eb18ce7b95"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   City Population  Humans  Halflings  Dwarves  Elves  Orcs  Gnomes\n",
            "0           211446  189768      11453     7622   5469  4244    3223\n",
            "1           132049  117203       7846     4970   3219  3942    2361\n",
            "2           102787   89566       5454     4031   2171  2598    1938\n",
            "3           156526  126051       8041     5519   4484  3406    2897\n",
            "4           207238  184331      12275     7835   5071  6053    3857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Setting Tax Rate and Number of Nobility\n",
        "city_data['# of Nobility'] = (city_data['City Population'] * np.random.uniform(0.01, 0.03)).astype(int)\n",
        "city_data['Tax Rate'] = np.random.uniform(0.1, 0.3, cities) + (city_data['# of Nobility'] / city_data['City Population'])\n",
        "\n",
        "# Step 4: Calculating Danger Level\n",
        "city_data['Dragon Sightings'] = np.random.randint(0, 100, cities)\n",
        "city_data['Political Climate'] = np.random.uniform(0, 1, cities)\n",
        "city_data['Magical Climate'] = np.random.uniform(0, 1, cities)\n",
        "city_data['Openness to Strangers'] = np.random.uniform(0, 1, cities)\n",
        "city_data['Average Debt by Household'] = np.random.uniform(0, 1e4, cities)\n",
        "city_data['Rumored Treasure Value'] = np.random.uniform(0, 1e6, cities)\n",
        "\n",
        "bottom_quartile = city_data['Openness to Strangers'].quantile(0.25)\n",
        "top_quartile = city_data['Openness to Strangers'].quantile(0.75)\n",
        "openness_score = np.where(city_data['Openness to Strangers'] < bottom_quartile, 1,\n",
        "                np.where(city_data['Openness to Strangers'] > top_quartile, 1, 0))\n",
        "\n",
        "city_data['Likelihood of Getting Cursed'] = city_data['Magical Climate'] + openness_score\n",
        "city_data['Danger Level'] = (city_data['Dragon Sightings'] + city_data['Political Climate'] +\n",
        "                             city_data['Magical Climate'] + city_data['Likelihood of Getting Cursed'] +\n",
        "                             city_data['Tax Rate'] + city_data['Average Debt by Household'] +\n",
        "                             city_data['Rumored Treasure Value'] + openness_score) / 8\n",
        "\n",
        "# Step 5: Setting Economic Factors\n",
        "total_economy = np.random.uniform(80e6, 100e6)\n",
        "city_data['Active Economy (Gold)'] = (np.random.uniform(0.4, 0.6, cities) * (city_data['City Population'] / total_population) * total_economy).astype(int)\n",
        "city_data['Hidden Economy (Gold)'] = ((city_data['City Population'] / total_population) * total_economy - city_data['Active Economy (Gold)']).astype(int)\n",
        "\n",
        "# Step 6: Setting Population Count for Nobility and Peasants\n",
        "city_data['# of Peasants'] = (city_data['City Population'] * np.random.uniform(0.8, 0.9)).astype(int)\n",
        "\n",
        "# Step 7: Configuring Fate Ranking\n",
        "city_data['Wizarding School'] = np.random.choice(['yes', 'no'], cities)\n",
        "city_data['Fate ranking'] = (city_data['Rumored Treasure Value'] / 1e6) + np.where(city_data['Wizarding School'] == 'yes', 1, 0)\n",
        "\n",
        "# Print first few rows to check\n",
        "print(city_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "851P2aiCHeky",
        "outputId": "2bd37c60-70f1-46fe-b3e7-7c49b3f628dc"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   City Population  Humans  Halflings  Dwarves  Elves  Orcs  Gnomes  \\\n",
            "0           211446  189768      11453     7622   5469  4244    3223   \n",
            "1           132049  117203       7846     4970   3219  3942    2361   \n",
            "2           102787   89566       5454     4031   2171  2598    1938   \n",
            "3           156526  126051       8041     5519   4484  3406    2897   \n",
            "4           207238  184331      12275     7835   5071  6053    3857   \n",
            "\n",
            "   # of Nobility  Tax Rate  Dragon Sightings  ...  Openness to Strangers  \\\n",
            "0           2633  0.292279                77  ...               0.453717   \n",
            "1           1644  0.155011                19  ...               0.918264   \n",
            "2           1280  0.157687                25  ...               0.514864   \n",
            "3           1949  0.291992                38  ...               0.921383   \n",
            "4           2581  0.121567                97  ...               0.653692   \n",
            "\n",
            "   Average Debt by Household  Rumored Treasure Value  \\\n",
            "0                4605.786974           135209.679497   \n",
            "1                1626.688669           673780.463366   \n",
            "2                  37.057913           207759.785657   \n",
            "3                9728.051513           589240.450988   \n",
            "4                3154.891658           158665.818147   \n",
            "\n",
            "   Likelihood of Getting Cursed  Danger Level  Active Economy (Gold)  \\\n",
            "0                      0.030353  17486.726748                 129080   \n",
            "1                      1.808182  84428.834098                  86123   \n",
            "2                      0.962269  25978.069289                  66803   \n",
            "3                      1.556146  74876.358921                 101350   \n",
            "4                      0.581675  20239.987652                 118944   \n",
            "\n",
            "   Hidden Economy (Gold)  # of Peasants  Wizarding School  Fate ranking  \n",
            "0                 160374         180474               yes      1.135210  \n",
            "1                  94642         112707               yes      1.673780  \n",
            "2                  73905          87731               yes      1.207760  \n",
            "3                 112923         133598                no      0.589240  \n",
            "4                 164750         176882                no      0.158666  \n",
            "\n",
            "[5 rows x 22 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: Setting Political Climate\n",
        "city_data['# Homeowners'] = (city_data['City Population'] * np.random.uniform(0.5, 0.7)).astype(int)\n",
        "city_data['Average Rent'] = np.random.uniform(500, 2000, cities)\n",
        "city_data['Average Age of Population'] = np.random.uniform(25, 70, cities)\n",
        "city_data['Known Treasure Value'] = np.random.uniform(0, 1e6, cities)\n",
        "\n",
        "city_data['Political Climate'] = (city_data['Tax Rate'] + city_data['# Homeowners']/city_data['City Population'] +\n",
        "                                  city_data['Average Rent']/2000 + city_data['Average Debt by Household']/1e4 +\n",
        "                                  city_data['Average Age of Population']/70 + city_data['Hidden Economy (Gold)']/1e6 +\n",
        "                                  city_data['Known Treasure Value']/1e6 + city_data['Rumored Treasure Value']/1e6) / 8\n",
        "\n",
        "# Step 10: Adjusting Fate Ranking\n",
        "city_data['Fate ranking'] = (city_data['Rumored Treasure Value'] / 1e6) + np.where(city_data['Wizarding School'] == 'yes', 1, 0) + city_data['Political Climate']\n",
        "\n",
        "# Step 11: Making Dataset Generation Replicable\n",
        "def generate_dataset(seed=42):\n",
        "    np.random.seed(seed)\n",
        "    # ... (insert all the previous code that generates the data here)\n",
        "    return city_data\n",
        "\n",
        "# Generate a new dataset with a different seed each time to get a slightly different dataset\n",
        "new_dataset = generate_dataset(seed=45)\n",
        "print(new_dataset.head())\n",
        "\n",
        "# You can use the generate_dataset function to create new datasets with minor variations each time\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0mMwVEjFH2zN",
        "outputId": "b5948047-ef5c-41e2-9345-673f3f421f7c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   City Population  Humans  Halflings  Dwarves  Elves  Orcs  Gnomes  \\\n",
            "0           211446  189768      11453     7622   5469  4244    3223   \n",
            "1           132049  117203       7846     4970   3219  3942    2361   \n",
            "2           102787   89566       5454     4031   2171  2598    1938   \n",
            "3           156526  126051       8041     5519   4484  3406    2897   \n",
            "4           207238  184331      12275     7835   5071  6053    3857   \n",
            "\n",
            "   # of Nobility  Tax Rate  Dragon Sightings  ...  Danger Level  \\\n",
            "0           2633  0.292279                77  ...  17486.726748   \n",
            "1           1644  0.155011                19  ...  84428.834098   \n",
            "2           1280  0.157687                25  ...  25978.069289   \n",
            "3           1949  0.291992                38  ...  74876.358921   \n",
            "4           2581  0.121567                97  ...  20239.987652   \n",
            "\n",
            "   Active Economy (Gold)  Hidden Economy (Gold)  # of Peasants  \\\n",
            "0                 129080                 160374         180474   \n",
            "1                  86123                  94642         112707   \n",
            "2                  66803                  73905          87731   \n",
            "3                 101350                 112923         133598   \n",
            "4                 118944                 164750         176882   \n",
            "\n",
            "   Wizarding School  Fate ranking  # Homeowners  Average Rent  \\\n",
            "0               yes      1.505973        134624    871.655550   \n",
            "1               yes      2.127391         84073    814.843049   \n",
            "2               yes      1.531353         65443    933.542229   \n",
            "3                no      1.074583         99657   1570.491751   \n",
            "4                no      0.637683        131945   1926.639039   \n",
            "\n",
            "   Average Age of Population  Known Treasure Value  \n",
            "0                  56.310888          40716.846454  \n",
            "1                  66.985666         541742.858443  \n",
            "2                  29.599798         619373.981276  \n",
            "3                  34.270718           4271.132595  \n",
            "4                  42.400489         865941.584472  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 325
        },
        "id": "qlPdpOf4IMFV",
        "outputId": "64f3f461-6096-457a-839d-c17c79903b24"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  City   Region   Nation  Population of City Government  \\\n",
              "0            Abriymoch  Region2  Nation2               15922  Oligarchy   \n",
              "1                Aelor  Region2  Nation3               12583  Oligarchy   \n",
              "2             Ageadren  Region3  Nation2               84272  Oligarchy   \n",
              "3              Airspur  Region2  Nation3               50224  Oligarchy   \n",
              "4  Airspur (Chessenta)  Region1  Nation3               17081  Oligarchy   \n",
              "\n",
              "   Percentage of population Humans  Percentage of population Halflings  \\\n",
              "0                        92.890664                            7.904120   \n",
              "1                        76.311884                            1.607618   \n",
              "2                        12.111212                           54.610246   \n",
              "3                        85.766203                           73.721184   \n",
              "4                         9.291475                           49.656559   \n",
              "\n",
              "   Percentage of population Orcs  Percentage of population Elves  \\\n",
              "0                      71.520153                       80.264236   \n",
              "1                      11.346570                       68.282074   \n",
              "2                      89.830203                       74.545746   \n",
              "3                      42.047838                       30.094798   \n",
              "4                      84.705312                       85.098654   \n",
              "\n",
              "   Percentage of population Dwarves  ...  Environmental Climate  \\\n",
              "0                         10.024334  ...              21.149629   \n",
              "1                         80.864470  ...              85.989144   \n",
              "2                         30.843258  ...              41.153760   \n",
              "3                         48.466543  ...              47.134302   \n",
              "4                         94.914073  ...              65.841312   \n",
              "\n",
              "   Average Temperature Average Rainfall Political Climate  Magical Climate  \\\n",
              "0            28.056975        51.715684         37.255145        16.583509   \n",
              "1            42.689376        84.906626         15.165676        92.435707   \n",
              "2            21.158781         9.216342         65.065520        44.178035   \n",
              "3            16.988302        23.385766         41.125886        54.351449   \n",
              "4            78.579678        66.109504         50.585987        21.530267   \n",
              "\n",
              "   Likelihood of Getting Cursed  Danger Level  Openness to Strangers  \\\n",
              "0                     36.113485     90.531226              60.926050   \n",
              "1                     33.064317     86.386139               5.308632   \n",
              "2                     94.057346     57.030169              46.249247   \n",
              "3                     46.155325     35.290176              39.565244   \n",
              "4                     12.120318     74.095791              33.795816   \n",
              "\n",
              "   Notable Landmarks Fate ranking  \n",
              "0          Landmark1           45  \n",
              "1          Landmark3          416  \n",
              "2          Landmark3          227  \n",
              "3          Landmark2           86  \n",
              "4          Landmark1          519  \n",
              "\n",
              "[5 rows x 40 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Region</th>\n",
              "      <th>Nation</th>\n",
              "      <th>Population of City</th>\n",
              "      <th>Government</th>\n",
              "      <th>Percentage of population Humans</th>\n",
              "      <th>Percentage of population Halflings</th>\n",
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              "      <th>Political Climate</th>\n",
              "      <th>Magical Climate</th>\n",
              "      <th>Likelihood of Getting Cursed</th>\n",
              "      <th>Danger Level</th>\n",
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              "      <th>Notable Landmarks</th>\n",
              "      <th>Fate ranking</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Abriymoch</td>\n",
              "      <td>Region2</td>\n",
              "      <td>Nation2</td>\n",
              "      <td>15922</td>\n",
              "      <td>Oligarchy</td>\n",
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              "      <td>36.113485</td>\n",
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              "      <td>60.926050</td>\n",
              "      <td>Landmark1</td>\n",
              "      <td>45</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aelor</td>\n",
              "      <td>Region2</td>\n",
              "      <td>Nation3</td>\n",
              "      <td>12583</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>76.311884</td>\n",
              "      <td>1.607618</td>\n",
              "      <td>11.346570</td>\n",
              "      <td>68.282074</td>\n",
              "      <td>80.864470</td>\n",
              "      <td>...</td>\n",
              "      <td>85.989144</td>\n",
              "      <td>42.689376</td>\n",
              "      <td>84.906626</td>\n",
              "      <td>15.165676</td>\n",
              "      <td>92.435707</td>\n",
              "      <td>33.064317</td>\n",
              "      <td>86.386139</td>\n",
              "      <td>5.308632</td>\n",
              "      <td>Landmark3</td>\n",
              "      <td>416</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ageadren</td>\n",
              "      <td>Region3</td>\n",
              "      <td>Nation2</td>\n",
              "      <td>84272</td>\n",
              "      <td>Oligarchy</td>\n",
              "      <td>12.111212</td>\n",
              "      <td>54.610246</td>\n",
              "      <td>89.830203</td>\n",
              "      <td>74.545746</td>\n",
              "      <td>30.843258</td>\n",
              "      <td>...</td>\n",
              "      <td>41.153760</td>\n",
              "      <td>21.158781</td>\n",
              "      <td>9.216342</td>\n",
              "      <td>65.065520</td>\n",
              "      <td>44.178035</td>\n",
              "      <td>94.057346</td>\n",
              "      <td>57.030169</td>\n",
              "      <td>46.249247</td>\n",
              "      <td>Landmark3</td>\n",
              "      <td>227</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>Nation3</td>\n",
              "      <td>50224</td>\n",
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              "      <td>42.047838</td>\n",
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              "      <td>35.290176</td>\n",
              "      <td>39.565244</td>\n",
              "      <td>Landmark2</td>\n",
              "      <td>86</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Airspur (Chessenta)</td>\n",
              "      <td>Region1</td>\n",
              "      <td>Nation3</td>\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e8442b36-cec7-428a-8456-700e9a1653e9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e8442b36-cec7-428a-8456-700e9a1653e9')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8442b36-cec7-428a-8456-700e9a1653e9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the updated DataFrame back to the Excel file\n",
        "df.to_excel('/content/drive/MyDrive/Colab Notebooks/DnD/fictional_data.xlsx', index=False)"
      ],
      "metadata": {
        "id": "XDmTHXTYIceT"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Update Percentage Columns\n",
        "percentage_columns = [\n",
        "    'Percentage of population Humans',\n",
        "    'Percentage of population Halflings',\n",
        "    'Percentage of population Dwarves',\n",
        "    'Percentage of population Elves',\n",
        "    'Percentage of population Orcs',\n",
        "    'Percentage of population Gnomes'\n",
        "]\n",
        "\n",
        "for col in percentage_columns:\n",
        "    city_data[col] = city_data[col].round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "bY17zmmyIvtA",
        "outputId": "88253e4c-2a7c-4676-bbf6-deacda0103a6"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Humans'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-21-c0ad7c70f302>\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpercentage_columns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Humans'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Adjust Danger Level\n",
        "\n",
        "# Convert \"Danger Level\" to an integer between 1 and 10\n",
        "city_data['Danger Level'] = (city_data['Dragon Sightings'] + city_data['Political Climate'] + city_data['Magical Climate'] + city_data['Likelihood of Getting Cursed'] + city_data['Tax Rate'] + city_data['Average Debt by Household'] + city_data['Rumored Treasure Value'] + city_data['Openness to Strangers'].apply(lambda x: 10 if x <= 2.5 or x >= 7.5 else 0)).astype(int)\n",
        "\n",
        "# We normalize the \"Danger Level\" to be between 1 and 10\n",
        "city_data['Danger Level'] = ((city_data['Danger Level'] - city_data['Danger Level'].min()) / (city_data['Danger Level'].max() - city_data['Danger Level'].min()) * 9 + 1).astype(int)\n",
        "\n",
        "# Increase \"Danger Level\" if Orcs are present\n",
        "city_data.loc[city_data['Percentage of population Orcs'] > 0, 'Danger Level'] += 1\n",
        "\n",
        "# Decrease \"Danger Level\" if Elves are present\n",
        "city_data.loc[city_data['Percentage of population Elves'] > 0, 'Danger Level'] -= 1\n",
        "\n",
        "# Ensure \"Danger Level\" remains within the range 1-10\n",
        "city_data['Danger Level'] = city_data['Danger Level'].clip(1, 10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "CoQtETz8MSIH",
        "outputId": "3e107b86-2eb4-4b79-8c0b-d23c1b5aa16c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Orcs'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-ba372499be91>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Increase \"Danger Level\" if Orcs are present\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Percentage of population Orcs'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Danger Level'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Decrease \"Danger Level\" if Elves are present\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Orcs'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Modify Dragon Sightings\n",
        "\n",
        "# First, we'll create a mapping of the total gold per region\n",
        "region_gold_mapping = city_data.groupby('Region')['Active Economy (Gold)', 'Hidden Economy (Gold)'].sum().sum(axis=1)\n",
        "\n",
        "# Next, we'll adjust the Dragon Sightings based on the total gold in the region and the Fate ranking of the city\n",
        "city_data['Dragon Sightings'] = city_data.apply(lambda row: (row['Fate ranking'] * (region_gold_mapping[row['Region']] / 1e6)), axis=1)\n",
        "\n",
        "# We'll then normalize this to be a number between 0 and 50\n",
        "city_data['Dragon Sightings'] = ((city_data['Dragon Sightings'] - city_data['Dragon Sightings'].min()) / (city_data['Dragon Sightings'].max() - city_data['Dragon Sightings'].min()) * 50).astype(int)\n",
        "\n",
        "# Making sure \"Dragon Sightings\" does not exceed 50\n",
        "city_data['Dragon Sightings'] = city_data['Dragon Sightings'].clip(0, 50)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "OoOS0IJRMiDj",
        "outputId": "e729a023-6008-4fc1-e891-5bcca1878124"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-23-955dbff3a42c>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# First, we'll create a mapping of the total gold per region\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mregion_gold_mapping\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Active Economy (Gold)'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Hidden Economy (Gold)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m# Next, we'll adjust the Dragon Sightings based on the total gold in the region and the Fate ranking of the city\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   8400\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8401\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8402\u001b[0;31m         return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m   8403\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8404\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 965\u001b[0;31m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m    966\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    886\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 888\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    889\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Region'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Determine Average Age of Population\n",
        "\n",
        "# First, we'll define a function to calculate the average age based on the population percentages and the species' age ranges\n",
        "def calculate_average_age(row):\n",
        "    species_age_ranges = {\n",
        "        \"Humans\": [1, 100],\n",
        "        \"Dwarves\": [1, 350],\n",
        "        \"Elves\": [1, 750],\n",
        "        \"Gnomes\": [300, 500],\n",
        "        \"Halflings\": [1, 150],\n",
        "        \"Orcs\": [1, 50]\n",
        "    }\n",
        "\n",
        "    total_population = row['Population of City']\n",
        "    weighted_ages = sum([(row[f'Percentage of population {species}'] / 100) * (sum(species_age_ranges[species]) / 2) * total_population for species in species_age_ranges])\n",
        "\n",
        "    return weighted_ages / total_population\n",
        "\n",
        "# Now, we'll apply this function to determine the average age of population\n",
        "city_data['Average Age of Population'] = city_data.apply(calculate_average_age, axis=1)\n",
        "\n",
        "# Considering the effect of \"Danger Level\" on the average age\n",
        "city_data['Average Age of Population'] = city_data['Average Age of Population'] - (city_data['Danger Level'] * 0.5)\n",
        "\n",
        "# Ensuring the average age doesn't fall below the minimum possible age\n",
        "city_data['Average Age of Population'] = city_data['Average Age of Population'].clip(1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "SxMLvFf4Msvo",
        "outputId": "adf51f76-56b1-4631-9257-8f4145ed06b0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Population of City'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-24-e96522dea5bf>\u001b[0m in \u001b[0;36m<cell line: 20>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# Now, we'll apply this function to determine the average age of population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Average Age of Population'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_average_age\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# Considering the effect of \"Danger Level\" on the average age\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[1;32m   9566\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9567\u001b[0m         )\n\u001b[0;32m-> 9568\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9570\u001b[0m     def applymap(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-24-e96522dea5bf>\u001b[0m in \u001b[0;36mcalculate_average_age\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m     12\u001b[0m     }\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mtotal_population\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Population of City'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     \u001b[0mweighted_ages\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34mf'Percentage of population {species}'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspecies_age_ranges\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mspecies\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtotal_population\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mspecies\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mspecies_age_ranges\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1090\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Population of City'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Adjusting population distribution for specific species across cities\n",
        "def adjust_population_distribution(city_data, species, percentage, city_count):\n",
        "    total_population = city_data['Population of City'].sum()\n",
        "    target_population = int(total_population * (percentage / 100))\n",
        "    selected_cities = city_data.sample(city_count)\n",
        "    each_city_population = target_population // city_count\n",
        "    for i, city in selected_cities.iterrows():\n",
        "        city_data.at[i, f'Percentage of population {species}'] = (each_city_population / city['Population of City']) * 100\n",
        "\n",
        "# Adjusting the population distribution\n",
        "adjust_population_distribution(city_data, \"Gnomes\", 99, 2)\n",
        "adjust_population_distribution(city_data, \"Dwarves\", 80, 4)\n",
        "adjust_population_distribution(city_data, \"Elves\", 95, 5)\n",
        "adjust_population_distribution(city_data, \"Orcs\", 99, 1)\n",
        "\n",
        "# Adjusting Dragon Sightings\n",
        "city_data['Dragon Sightings'] = (city_data['Region'].map(city_data.groupby('Region')['Fate ranking'].sum() + city_data.groupby('Region')['Active Economy (Gold)'].sum()) / 1e6).clip(0, 50)\n",
        "\n",
        "# Adjusting Danger Level\n",
        "city_data['Danger Level'] = city_data.apply(lambda row: np.random.randint(1, 11) + (3 if row['Percentage of population Orcs'] > 0 else 0) - (2 if row['Percentage of population Elves'] > 0 else 0), axis=1)\n",
        "\n",
        "# Adjusting Openness to Strangers\n",
        "city_data['Openness to Strangers'] = city_data.apply(lambda row: np.random.randint(0, 11) - (3 if row['Percentage of population Elves'] > 0 or row['Percentage of population Orcs'] > 0 else 0), axis=1)\n",
        "\n",
        "# Adjusting Magical Climate\n",
        "city_data['Magical Climate'] = city_data.apply(lambda row: row['Magical Climate'] + (row['Percentage of population Elves'] / 100 * 2) + (row['Dragon Sightings'] / 10) + row['Number of Active Wizards'], axis=1)\n",
        "\n",
        "# Adjusting Hidden Economy based on the number of nobility\n",
        "city_data['Hidden Economy (Gold)'] = city_data.apply(lambda row: row['Hidden Economy (Gold)'] * (1 + (row['# of Nobility'] / row['Population of City']) * 100), axis=1)\n",
        "\n",
        "# Ensuring Percentage values are rounded to 2 decimal places\n",
        "percentage_columns = [col for col in city_data.columns if 'Percentage' in col]\n",
        "city_data[percentage_columns] = city_data[percentage_columns].round(2)\n",
        "\n",
        "# Making sure Openness to Strangers is between 0 and 10\n",
        "city_data['Openness to Strangers'] = city_data['Openness to Strangers'].clip(0, 10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "H4-MZWPMM5GN",
        "outputId": "bf8c9320-8d04-4c22-e68a-37f1ea6fa5de"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Population of City'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-4a56fc7c3082>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# Adjusting the population distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0madjust_population_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Gnomes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0madjust_population_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Dwarves\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0madjust_population_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Elves\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m95\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-25-4a56fc7c3082>\u001b[0m in \u001b[0;36madjust_population_distribution\u001b[0;34m(city_data, species, percentage, city_count)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Adjusting population distribution for specific species across cities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0madjust_population_distribution\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspecies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpercentage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcity_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mtotal_population\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Population of City'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mtarget_population\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_population\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpercentage\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mselected_cities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Population of City'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_average_age(row):\n",
        "    human_percentage = row['Percentage of population Humans']\n",
        "    dwarf_percentage = row['Percentage of population Dwarves']\n",
        "    elf_percentage = row['Percentage of population Elves']\n",
        "    gnome_percentage = row['Percentage of population Gnomes']\n",
        "    halfling_percentage = row['Percentage of population Halflings']\n",
        "    orc_percentage = row['Percentage of population Orcs']\n",
        "\n",
        "    # Getting the random average age for each species\n",
        "    human_avg_age = np.random.uniform(1, 100, 1)[0]\n",
        "    dwarf_avg_age = np.random.uniform(1, 350, 1)[0]\n",
        "    elf_avg_age = np.random.uniform(1, 750, 1)[0]\n",
        "    gnome_avg_age = np.random.uniform(300, 500, 1)[0]\n",
        "    halfling_avg_age = np.random.uniform(1, 150, 1)[0]\n",
        "    orc_avg_age = np.random.uniform(1, 50, 1)[0]\n",
        "\n",
        "    # Calculating the average age of the population\n",
        "    average_age = (\n",
        "        (human_avg_age * human_percentage) +\n",
        "        (dwarf_avg_age * dwarf_percentage) +\n",
        "        (elf_avg_age * elf_percentage) +\n",
        "        (gnome_avg_age * gnome_percentage) +\n",
        "        (halfling_avg_age * halfling_percentage) +\n",
        "        (orc_avg_age * orc_percentage)\n",
        "    ) / 100\n",
        "\n",
        "    return average_age\n",
        "\n",
        "# Setting the average age of population\n",
        "city_data['Average Age of Population'] = city_data.apply(calculate_average_age, axis=1)\n",
        "\n",
        "# Setting the number of active wizards randomly between 0 and 20\n",
        "city_data['Number of Active Wizards'] = np.random.randint(0, 21, city_data.shape[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "dmhNHEhzNHm2",
        "outputId": "a6af6d71-5162-4022-c178-f8db76cb602f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Humans'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-dfb2cfe0b6b8>\u001b[0m in \u001b[0;36m<cell line: 30>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;31m# Setting the average age of population\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Average Age of Population'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_average_age\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# Setting the number of active wizards randomly between 0 and 20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[1;32m   9566\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9567\u001b[0m         )\n\u001b[0;32m-> 9568\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9570\u001b[0m     def applymap(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-26-dfb2cfe0b6b8>\u001b[0m in \u001b[0;36mcalculate_average_age\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalculate_average_age\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mhuman_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Percentage of population Humans'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0mdwarf_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Percentage of population Dwarves'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0melf_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Percentage of population Elves'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mgnome_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Percentage of population Gnomes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    980\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 981\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    982\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    983\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1088\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1089\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1090\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Percentage of population Humans'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_dragon_sightings(row):\n",
        "    # The dragon sightings are influenced by the total gold in a region and the fate ranking of the city\n",
        "    region_gold = city_data[city_data['Region'] == row['Region']]['Active Economy (Gold)'].sum() + city_data[city_data['Region'] == row['Region']]['Hidden Economy (Gold)'].sum()\n",
        "    max_sightings = min(50, row['Fate ranking'] + region_gold // 1e6)\n",
        "    return np.random.randint(0, max_sightings+1)\n",
        "\n",
        "def adjust_danger_level(row):\n",
        "    # Adjusting danger level based on the presence of Orcs and Elves\n",
        "    danger_level = row['Danger Level']\n",
        "    if row['Percentage of population Orcs'] > 0:\n",
        "        danger_level += 1\n",
        "    if row['Percentage of population Elves'] > 0:\n",
        "        danger_level -= 1\n",
        "    return min(max(1, danger_level), 10)\n",
        "\n",
        "def calculate_magical_climate(row):\n",
        "    # Adjusting magical climate based on dragon sightings and the number of active wizards\n",
        "    return row['Dragon Sightings'] * 0.1 + row['Number of Active Wizards'] * 0.5\n",
        "\n",
        "def calculate_openness_to_strangers(row):\n",
        "    # Adjusting openness to strangers based on the presence of Elves or Orcs\n",
        "    openness = row['Openness to Strangers']\n",
        "    if row['Percentage of population Elves'] > 0 or row['Percentage of population Orcs'] > 0:\n",
        "        openness -= 2\n",
        "    return min(max(0, openness), 10)\n",
        "\n",
        "# Applying the functions\n",
        "city_data['Dragon Sightings'] = city_data.apply(calculate_dragon_sightings, axis=1)\n",
        "city_data['Danger Level'] = city_data.apply(adjust_danger_level, axis=1)\n",
        "city_data['Magical Climate'] = city_data.apply(calculate_magical_climate, axis=1)\n",
        "city_data['Openness to Strangers'] = city_data.apply(calculate_openness_to_strangers, axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "da02TtucNTPE",
        "outputId": "3cd5d7c0-67a3-40b0-d5c6-0236c6f741a9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Region'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-27-f48812b368a3>\u001b[0m in \u001b[0;36m<cell line: 28>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0;31m# Applying the functions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Dragon Sightings'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_dragon_sightings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Danger Level'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madjust_danger_level\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Magical Climate'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalculate_magical_climate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, axis, raw, result_type, args, **kwargs)\u001b[0m\n\u001b[1;32m   9566\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9567\u001b[0m         )\n\u001b[0;32m-> 9568\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"apply\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   9569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9570\u001b[0m     def applymap(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_raw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    890\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 891\u001b[0;31m         \u001b[0mresults\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mres_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_series_generator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    892\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[0;31m# wrap results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_series_generator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    905\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseries_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    906\u001b[0m                 \u001b[0;31m# ignore SettingWithCopy here in case the user mutates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 907\u001b[0;31m                 \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    908\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m                     \u001b[0;31m# If we have a view on v, we need to make a copy because\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-27-f48812b368a3>\u001b[0m in \u001b[0;36mcalculate_dragon_sightings\u001b[0;34m(row)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcalculate_dragon_sightings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# The dragon sightings are influenced by the total gold in a region and the fate ranking of the city\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mregion_gold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Region'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Region'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Active Economy (Gold)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Region'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Region'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Hidden Economy (Gold)'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mmax_sightings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fate ranking'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mregion_gold\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_sightings\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Region'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8.1: Adjusting Population Distribution\n",
        "# Define the cities where majority of the specific species will reside\n",
        "gnome_cities = np.random.choice(city_data['City'], size=2, replace=False)\n",
        "dwarf_cities = np.random.choice(city_data['City'], size=4, replace=False)\n",
        "elf_cities = np.random.choice(city_data['City'], size=5, replace=False)\n",
        "orc_city = np.random.choice(city_data['City'], size=1, replace=False)\n",
        "\n",
        "# Define functions to adjust the population percentages for Gnomes, Dwarves, Elves, and Orcs\n",
        "def adjust_gnome_population(row):\n",
        "    if row['City'] in gnome_cities:\n",
        "        return np.random.uniform(40, 50)\n",
        "    return np.random.uniform(0, 0.1)\n",
        "\n",
        "def adjust_dwarf_population(row):\n",
        "    if row['City'] in dwarf_cities:\n",
        "        return np.random.uniform(15, 25)\n",
        "    return np.random.uniform(0, 1)\n",
        "\n",
        "def adjust_elf_population(row):\n",
        "    if row['City'] in elf_cities:\n",
        "        return np.random.uniform(15, 20)\n",
        "    return np.random.uniform(0, 1)\n",
        "\n",
        "def adjust_orc_population(row):\n",
        "    if row['City'] == orc_city:\n",
        "        return np.random.uniform(90, 99)\n",
        "    return np.random.uniform(0, 1)\n",
        "\n",
        "# Apply the functions to adjust the population distribution\n",
        "city_data['Percentage of population Gnomes'] = city_data.apply(adjust_gnome_population, axis=1)\n",
        "city_data['Percentage of population Dwarves'] = city_data.apply(adjust_dwarf_population, axis=1)\n",
        "city_data['Percentage of population Elves'] = city_data.apply(adjust_elf_population, axis=1)\n",
        "city_data['Percentage of population Orcs'] = city_data.apply(adjust_orc_population, axis=1)\n",
        "\n",
        "# Step 8.2: Calculating Average Age of Population\n",
        "# Define a function to calculate the average age based on the species population percentages and danger level\n",
        "def calculate_average_age(row):\n",
        "    human_age = np.random.uniform(1, 100)\n",
        "    dwarf_age = np.random.uniform(1, 350)\n",
        "    elf_age = np.random.uniform(1, 750)\n",
        "    gnome_age = np.random.uniform(300, 500)\n",
        "    halfling_age = np.random.uniform(1, 150)\n",
        "    orc_age = np.random.uniform(1, 50)\n",
        "    total_population = row['Total Population']\n",
        "\n",
        "    avg_age = ((human_age * row['Percentage of population Humans'] +\n",
        "                dwarf_age * row['Percentage of population Dwarves'] +\n",
        "                elf_age * row['Percentage of population Elves'] +\n",
        "                gnome_age * row['Percentage of population Gnomes'] +\n",
        "                halfling_age * row['Percentage of population Halflings'] +\n",
        "                orc_age * row['Percentage of population Orcs']) / 100)\n",
        "\n",
        "    return avg_age - (row['Danger Level'] / 10)\n",
        "\n",
        "# Apply the function to calculate the average age\n",
        "city_data['Average Age of Population'] = city_data.apply(calculate_average_age, axis=1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "-a9f9DFiNhJD",
        "outputId": "bbc1f530-6924-40fd-e4dc-dcea849e2d51"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'City'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-09a54ee85d37>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Step 8.1: Adjusting Population Distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Define the cities where majority of the specific species will reside\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgnome_cities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'City'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdwarf_cities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'City'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0melf_cities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'City'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'City'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9.1: Calculate Dragon Sightings based on total gold in a region and the Fate ranking of the city\n",
        "city_data['Dragon Sightings'] = (city_data['Total Gold in Region'] / 1e6) + city_data['Fate ranking']\n",
        "city_data['Dragon Sightings'] = city_data['Dragon Sightings'].apply(lambda x: min(x, 50))\n",
        "\n",
        "# Step 9.2: Modify Danger Level\n",
        "def calculate_danger_level(row):\n",
        "    danger_level = row['Danger Level']\n",
        "    if row['Percentage of population Orcs'] > 0:\n",
        "        danger_level += 1\n",
        "    if row['Percentage of population Elves'] > 0:\n",
        "        danger_level -= 1\n",
        "    return min(max(int(danger_level), 1), 10)\n",
        "city_data['Danger Level'] = city_data.apply(calculate_danger_level, axis=1)\n",
        "\n",
        "# Step 9.3: Modify Magical Climate\n",
        "def calculate_magical_climate(row):\n",
        "    magical_climate = row['Magical Climate']\n",
        "    if row['Percentage of population Elves'] > 0:\n",
        "        magical_climate += 1\n",
        "    magical_climate += row['Dragon Sightings'] / 10\n",
        "    magical_climate += row['Number of Active Wizards'] / 2\n",
        "    return min(max(int(magical_climate), 1), 10)\n",
        "city_data['Magical Climate'] = city_data.apply(calculate_magical_climate, axis=1)\n",
        "\n",
        "# Step 9.4: Modify Openness to Strangers\n",
        "def calculate_openness_to_strangers(row):\n",
        "    openness = row['Openness to Strangers']\n",
        "    if row['Percentage of population Elves'] > 0 or row['Percentage of population Orcs'] > 0:\n",
        "        openness -= 2\n",
        "    return min(max(int(openness), 0), 10)\n",
        "city_data['Openness to Strangers'] = city_data.apply(calculate_openness_to_strangers, axis=1)\n",
        "\n",
        "# Step 9.5: Modify Hidden Economy\n",
        "city_data['Hidden Economy'] = city_data['# of Nobility'] * 1e4\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "id": "YrhivYJNNvxv",
        "outputId": "dd253dcd-c4a5-4989-d9a9-2b58e499eace"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Total Gold in Region'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-29-65b86c148b4a>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Step 9.1: Calculate Dragon Sightings based on total gold in a region and the Fate ranking of the city\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Dragon Sightings'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Total Gold in Region'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Fate ranking'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Dragon Sightings'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcity_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Dragon Sightings'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# Step 9.2: Modify Danger Level\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Total Gold in Region'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GXbGHZnLN615"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}