Training_the_model_2_evaluations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {
    "id": "vowXRstocd8F"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt # for making figures\n",
    "import re\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Rlc3YeWcm_d",
    "outputId": "bc5730cc-22d4-49e0-b1ce-a0a324914581"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100  240k  100  240k    0     0   828k      0 --:--:-- --:--:-- --:--:--  871k\n"
     ]
    }
   ],
   "source": [
    "# download the male_surnames_rus.txt file from github\n",
    "!curl https://raw.githubusercontent.com/Raven-SL/ru-pnames-list/master/lists/male_surnames_rus.txt -o male_surnames_rus.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uH2MGP4FcidR",
    "outputId": "f9c19210-19fc-4ba3-ce40-e2742986acea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Абабков',\n",
       " 'Абаимов',\n",
       " 'Абакишин',\n",
       " 'Абакулин',\n",
       " 'Абакулов',\n",
       " 'Абакумкин',\n",
       " 'Абакумов',\n",
       " 'Абакушин']"
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "surnames = open('male_surnames_rus.txt', 'r').read().splitlines()\n",
    "surnames[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FbakoEqAcvnF",
    "outputId": "8966dbc3-ec64-43c3-c0e9-1d80070c83e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14651"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(surnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "KEe6MYx4brHo",
    "outputId": "6c1630e7-76d8-4555-fb00-640393a97ff9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: 'А', 2: 'Б', 3: 'В', 4: 'Г', 5: 'Д', 6: 'Е', 7: 'Ж', 8: 'З', 9: 'И', 10: 'К', 11: 'Л', 12: 'М', 13: 'Н', 14: 'О', 15: 'П', 16: 'Р', 17: 'С', 18: 'Т', 19: 'У', 20: 'Ф', 21: 'Х', 22: 'Ц', 23: 'Ч', 24: 'Ш', 25: 'Щ', 26: 'Э', 27: 'Ю', 28: 'Я', 29: 'а', 30: 'б', 31: 'в', 32: 'г', 33: 'д', 34: 'е', 35: 'ж', 36: 'з', 37: 'и', 38: 'й', 39: 'к', 40: 'л', 41: 'м', 42: 'н', 43: 'о', 44: 'п', 45: 'р', 46: 'с', 47: 'т', 48: 'у', 49: 'ф', 50: 'х', 51: 'ц', 52: 'ч', 53: 'ш', 54: 'щ', 55: 'ъ', 56: 'ы', 57: 'ь', 58: 'э', 59: 'ю', 60: 'я', 61: 'ё', 0: '.'}\n"
     ]
    }
   ],
   "source": [
    "# build the vocabulary of characters and mappings to/from integers\n",
    "chars = sorted(list(set(''.join(surnames))))\n",
    "stoi = {s:i+1 for i,s in enumerate(chars)}\n",
    "stoi['.'] = 0\n",
    "itos = {i:s for s,i in stoi.items()}\n",
    "print(itos)\n",
    "N_chars = len(itos) #This line calculates the number of unique characters in the vocabulary by taking the length of the itos dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113
    },
    "id": "MB5XgJxdczFi",
    "outputId": "8bdcaad5-0884-4081-be06-02cb5d84954e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([130518, 3]) torch.Size([130518])\n",
      "torch.Size([130518, 3]) torch.Size([130518])\n",
      "torch.Size([130518, 3]) torch.Size([130518])\n"
     ]
    }
   ],
   "source": [
    "# build the dataset\n",
    "block_size = 3 # context length: how many characters do we take to predict the next one?\n",
    "\n",
    "def build_dataset(cleaned_surnames):\n",
    "  X, Y = [], []\n",
    "  for w in surnames:\n",
    "\n",
    "    #print(w)\n",
    "    context = [0] * block_size\n",
    "    for ch in w + '.':\n",
    "      ix = stoi[ch]\n",
    "      X.append(context)\n",
    "      Y.append(ix)\n",
    "      #print(''.join(itos[i] for i in context), '--->', itos[ix])\n",
    "      context = context[1:] + [ix] # crop and append\n",
    "\n",
    "  X = torch.tensor(X)\n",
    "  Y = torch.tensor(Y)\n",
    "  print(X.shape, Y.shape)\n",
    "  return X, Y\n",
    "\n",
    "import random\n",
    "random.seed(42)\n",
    "random.shuffle(surnames)\n",
    "n1 = int(0.8*len(surnames))\n",
    "n2 = int(0.9*len(surnames))\n",
    "\n",
    "Xtr, Ytr = build_dataset(surnames[:n1])\n",
    "Xdev, Ydev = build_dataset(surnames[n1:n2])\n",
    "Xte, Yte = build_dataset(surnames[n2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OGPnax5rnw8J",
    "outputId": "55b2fab1-e60c-458e-dfa9-de9b1bcb90f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0,  0,  0],\n",
       "        [ 0,  0, 18],\n",
       "        [ 0, 18, 37],\n",
       "        ...,\n",
       "        [29, 42, 52],\n",
       "        [42, 52, 37],\n",
       "        [52, 37, 42]])"
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {
    "id": "EAA0_oigc13X"
   },
   "outputs": [],
   "source": [
    "g = torch.Generator().manual_seed(2147483647) # for reproducibility\n",
    "C = torch.randn((N_chars, 10), generator=g) # N_chars is the number of characters\n",
    "W1 = torch.randn((30, 200), generator=g) # Assuming input size 30 + 10 (one-hot vector)\n",
    "b1 = torch.randn(200, generator=g)\n",
    "W2 = torch.randn((200, N_chars), generator=g) # Output size matches the number of characters\n",
    "b2 = torch.randn(N_chars, generator=g) # Bias for the output layer\n",
    "parameters = [C, W1, b1, W2, b2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_HcVef6-c5S4",
    "outputId": "f9ed068e-68f3-4286-bb1b-95799f086f73"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19282"
      ]
     },
     "execution_count": 461,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(p.nelement() for p in parameters) # number of parameters in total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "metadata": {
    "id": "coNBea1sc6tm"
   },
   "outputs": [],
   "source": [
    "for p in parameters:\n",
    "  p.requires_grad = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "metadata": {
    "id": "nFWpAj_9c9Yr"
   },
   "outputs": [],
   "source": [
    "lre = torch.linspace(-3, 0, 1000)\n",
    "lrs = 10**lre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {
    "id": "t2huuyTudBlF"
   },
   "outputs": [],
   "source": [
    "lri = []\n",
    "lossi = []\n",
    "stepi = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f2ZEldLGpH3s",
    "outputId": "79345441-cca4-4cbd-82e8-c512bf962435"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 3])"
      ]
     },
     "execution_count": 465,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ix = torch.randint(0, Xtr.shape[0], (32,))\n",
    "\n",
    "Xtr[ix].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "id": "jiKD-PPOdClh",
    "outputId": "9e5734b7-e201-4f71-bcec-8f237e94a130"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.308521032333374\n"
     ]
    }
   ],
   "source": [
    "for i in range(200000):\n",
    "\n",
    "  # minibatch construct\n",
    "  ix = torch.randint(0, Xtr.shape[0], (32,))\n",
    "\n",
    "  # forward pass\n",
    "  emb = C[Xtr[ix]] # (32, 3, 2)\n",
    "  h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
    "  logits = h @ W2 + b2 # (32, 27)\n",
    "  loss = F.cross_entropy(logits, Ytr[ix])\n",
    "  #print(loss.item())\n",
    "\n",
    "  # backward pass\n",
    "  for p in parameters:\n",
    "    p.grad = None\n",
    "  loss.backward()\n",
    "\n",
    "  # update\n",
    "  #lr = lrs[i]\n",
    "  lr = 0.01 if i < 100000 else 0.001\n",
    "  for p in parameters:\n",
    "    p.data += -lr * p.grad\n",
    "\n",
    "  # track stats\n",
    "  #lri.append(lre[i])\n",
    "  stepi.append(i)\n",
    "  lossi.append(loss.log10().item())\n",
    "\n",
    "print(loss.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 451
    },
    "id": "ZBAYXZ_rdGIG",
    "outputId": "06c3720c-9f23-4042-9fd6-ef0ba18cadc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x120133520>]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stepi, lossi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZlhYinNgdIVF",
    "outputId": "57f7f10d-fd21-41f0-e150-d2c4a2f17dbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.9771, grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training loss\n",
    "emb = C[Xtr] # (32, 3, 2)\n",
    "h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
    "logits = h @ W2 + b2 # (32, 27)\n",
    "loss = F.cross_entropy(logits, Ytr)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cJH3DDu4dLTM",
    "outputId": "2cf536f4-b8c5-4b86-dc75-b7f76bc844f5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.9771, grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 469,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# validation loss\n",
    "emb = C[Xdev] # (32, 3, 2)\n",
    "h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
    "logits = h @ W2 + b2 # (32, 27)\n",
    "loss = F.cross_entropy(logits, Ydev)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DwOgCg1vdNhe",
    "outputId": "4f62146f-c4e9-4ad0-c116-b0646d99a45c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(1.9771, grad_fn=<NllLossBackward0>)"
      ]
     },
     "execution_count": 470,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# test loss\n",
    "emb = C[Xte] # (32, 3, 2)\n",
    "h = torch.tanh(emb.view(-1, 30) @ W1 + b1) # (32, 100)\n",
    "logits = h @ W2 + b2 # (32, 27)\n",
    "loss = F.cross_entropy(logits, Yte)\n",
    "loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "id": "CCWLYP9GdPP4",
    "outputId": "7cc7d085-674d-4687-8068-e8bf9728d76d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize dimensions 0 and 1 of the embedding matrix C for all characters\n",
    "plt.figure(figsize=(8,8))\n",
    "plt.scatter(C[:,0].data, C[:,1].data, s=200)\n",
    "for i in range(C.shape[0]):\n",
    "    plt.text(C[i,0].item(), C[i,1].item(), itos[i], ha=\"center\", va=\"center\", color='white')\n",
    "plt.grid('minor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "TQUMmgRrdRIA",
    "outputId": "950198a1-acb1-4d71-9bf5-2a08074d6391"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Борашуев\n",
      "Кашапейко\n",
      "Евортин\n",
      "Насолин\n",
      "Содоговшулов\n",
      "Ивастуиник\n",
      "Дериков\n",
      "Сутов\n",
      "Родойногулин\n",
      "Улагов\n",
      "Шанов\n",
      "Крамов\n",
      "Чернышсов\n",
      "Хомнов\n",
      "Безоретоментороробих\n",
      "Солговарьнин\n",
      "Волеткин\n",
      "Кнувеев\n",
      "Харшин\n",
      "Бойко\n",
      "Адалов\n",
      "Солуженко\n",
      "Папинов\n",
      "Брроздендоблунов\n",
      "Клянов\n",
      "Яебюрдериков\n",
      "Порбушкин\n",
      "Конлицкостухов\n",
      "Шапин\n",
      "Циц\n",
      "Ялезозчоков\n",
      "Галастюнинаводиев\n",
      "Высчеркин\n",
      "Авлев\n",
      "Курпиев\n",
      "Шегтубьянкин\n",
      "Жиркин\n",
      "Нугданов\n",
      "Оснроваледенев\n",
      "Радев\n",
      "Парушкин\n",
      "Стешнявов\n",
      "Варбин\n",
      "Бахругых\n",
      "Сербатонникрреницкий\n",
      "Вицкий\n",
      "Олеев\n",
      "Ярологин\n",
      "Халазок\n",
      "Вонякин\n",
      "Волов\n",
      "Орогкин\n",
      "Анеш\n",
      "Васюшев\n",
      "Востов\n",
      "Копалов\n",
      "Рябаров\n",
      "Азжанов\n",
      "Сурак\n",
      "Носдошкин\n",
      "Зелозников\n",
      "Лехибов\n",
      "Сознипов\n",
      "Аментин\n",
      "Солкин\n",
      "Иров\n",
      "Шаринцев\n",
      "Стеянин\n",
      "Анпрошкин\n",
      "Максинкин\n",
      "Жашев\n",
      "Заклитонов\n",
      "Голадаренов\n",
      "Костов\n",
      "Абошняк\n",
      "Соваройчук\n",
      "Смып\n",
      "Кульмуньченко\n",
      "Поратеваленов\n",
      "Алишгов\n",
      "Рышин\n",
      "Васев\n",
      "Копетков\n",
      "Канов\n",
      "Васюлининородкин\n",
      "Воволов\n",
      "Сойпон\n",
      "Атдейчичов\n",
      "Осопьчков\n",
      "Иньевйчков\n",
      "Хохин\n",
      "Агранельпов\n",
      "Антель\n",
      "Тиманыев\n",
      "Саков\n",
      "Филчи\n",
      "Диунов\n",
      "Анасевыхин\n",
      "Бафуловский\n",
      "Роменкуриковсков\n"
     ]
    }
   ],
   "source": [
    "# sample from the model\n",
    "g = torch.Generator().manual_seed(2147483647 + 10)\n",
    "\n",
    "predicted_last_names = []\n",
    "for _ in range(100):\n",
    "\n",
    "    out = []\n",
    "    context = [0] * block_size # initialize with all ...\n",
    "    while True:\n",
    "      emb = C[torch.tensor([context])] # (1,block_size,d)\n",
    "      h = torch.tanh(emb.view(1, -1) @ W1 + b1)\n",
    "      logits = h @ W2 + b2\n",
    "      probs = F.softmax(logits, dim=1)\n",
    "      ix = torch.multinomial(probs, num_samples=1, generator=g).item()\n",
    "      context = context[1:] + [ix]\n",
    "      out.append(ix)\n",
    "      if ix == 0:\n",
    "        break\n",
    "# We get rid of the final dot - nessasary step for further evaluation.\n",
    "    last_name = ''.join(itos[i] for i in out)\n",
    "    last_name = re.sub(r'\\.', '', last_name)\n",
    "    predicted_last_names.append(last_name)\n",
    "    print(last_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "_R7XlUKtyjaN",
    "outputId": "3b75da1a-e079-4792-dca4-683d778d5d59"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted last names</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Борашуев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Кашапейко</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Евортин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Насолин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Содоговшулов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ивастуиник</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Дериков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Сутов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Родойногулин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Улагов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Шанов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Крамов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Чернышсов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Хомнов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Безоретоментороробих</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Солговарьнин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Волеткин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Кнувеев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Харшин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Бойко</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Адалов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Солуженко</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Папинов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Брроздендоблунов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Клянов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Яебюрдериков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Порбушкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Конлицкостухов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Шапин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Циц</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ялезозчоков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Галастюнинаводиев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Высчеркин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Авлев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Курпиев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Шегтубьянкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Жиркин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Нугданов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Оснроваледенев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Радев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Парушкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Стешнявов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Варбин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Бахругых</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Сербатонникрреницкий</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Вицкий</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Олеев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ярологин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Халазок</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Вонякин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Волов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Орогкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Анеш</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Васюшев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Востов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Копалов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Рябаров</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Азжанов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Сурак</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Носдошкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Зелозников</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Лехибов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Сознипов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Аментин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Солкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Иров</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Шаринцев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Стеянин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Анпрошкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Максинкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Жашев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Заклитонов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Голадаренов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Костов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Абошняк</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Соваройчук</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Смып</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Кульмуньченко</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Поратеваленов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Алишгов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Рышин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Васев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Копетков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Канов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Васюлининородкин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Воволов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Сойпон</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Атдейчичов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Осопьчков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Иньевйчков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Хохин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Агранельпов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Антель</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Тиманыев</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Саков</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Филчи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Диунов</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Анасевыхин</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Бафуловский</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Роменкуриковсков</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    predicted last names\n",
       "0               Борашуев\n",
       "1              Кашапейко\n",
       "2                Евортин\n",
       "3                Насолин\n",
       "4           Содоговшулов\n",
       "5             Ивастуиник\n",
       "6                Дериков\n",
       "7                  Сутов\n",
       "8           Родойногулин\n",
       "9                 Улагов\n",
       "10                 Шанов\n",
       "11                Крамов\n",
       "12             Чернышсов\n",
       "13                Хомнов\n",
       "14  Безоретоментороробих\n",
       "15          Солговарьнин\n",
       "16              Волеткин\n",
       "17               Кнувеев\n",
       "18                Харшин\n",
       "19                 Бойко\n",
       "20                Адалов\n",
       "21             Солуженко\n",
       "22               Папинов\n",
       "23      Брроздендоблунов\n",
       "24                Клянов\n",
       "25          Яебюрдериков\n",
       "26             Порбушкин\n",
       "27        Конлицкостухов\n",
       "28                 Шапин\n",
       "29                   Циц\n",
       "30           Ялезозчоков\n",
       "31     Галастюнинаводиев\n",
       "32             Высчеркин\n",
       "33                 Авлев\n",
       "34               Курпиев\n",
       "35          Шегтубьянкин\n",
       "36                Жиркин\n",
       "37              Нугданов\n",
       "38        Оснроваледенев\n",
       "39                 Радев\n",
       "40              Парушкин\n",
       "41             Стешнявов\n",
       "42                Варбин\n",
       "43              Бахругых\n",
       "44  Сербатонникрреницкий\n",
       "45                Вицкий\n",
       "46                 Олеев\n",
       "47              Ярологин\n",
       "48               Халазок\n",
       "49               Вонякин\n",
       "50                 Волов\n",
       "51               Орогкин\n",
       "52                  Анеш\n",
       "53               Васюшев\n",
       "54                Востов\n",
       "55               Копалов\n",
       "56               Рябаров\n",
       "57               Азжанов\n",
       "58                 Сурак\n",
       "59             Носдошкин\n",
       "60            Зелозников\n",
       "61               Лехибов\n",
       "62              Сознипов\n",
       "63               Аментин\n",
       "64                Солкин\n",
       "65                  Иров\n",
       "66              Шаринцев\n",
       "67               Стеянин\n",
       "68             Анпрошкин\n",
       "69             Максинкин\n",
       "70                 Жашев\n",
       "71            Заклитонов\n",
       "72           Голадаренов\n",
       "73                Костов\n",
       "74               Абошняк\n",
       "75            Соваройчук\n",
       "76                  Смып\n",
       "77         Кульмуньченко\n",
       "78         Поратеваленов\n",
       "79               Алишгов\n",
       "80                 Рышин\n",
       "81                 Васев\n",
       "82              Копетков\n",
       "83                 Канов\n",
       "84      Васюлининородкин\n",
       "85               Воволов\n",
       "86                Сойпон\n",
       "87            Атдейчичов\n",
       "88             Осопьчков\n",
       "89            Иньевйчков\n",
       "90                 Хохин\n",
       "91           Агранельпов\n",
       "92                Антель\n",
       "93              Тиманыев\n",
       "94                 Саков\n",
       "95                 Филчи\n",
       "96                Диунов\n",
       "97            Анасевыхин\n",
       "98           Бафуловский\n",
       "99      Роменкуриковсков"
      ]
     },
     "execution_count": 473,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a DataFrame with a single column \"predicted last names\"\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({\"predicted last names\": predicted_last_names})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {
    "id": "AnxUATIk0tLO"
   },
   "outputs": [],
   "source": [
    "df.to_csv('predicted_last_names.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R49J4v4Wv3UG",
    "outputId": "f607393c-5bf0-4dd6-9c47-2e1bcf68c76e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.91"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluation 1: The presence of common suffixes in a list of generated last names.\n",
    "#Sample list of suffixes\n",
    "suffixes = [\"ов\", \"ев\", \"ёв\", \"ин\", \"ич\", \"ын\", \"их\", \"ых\", \"ский\", \"цкий\", \"ый\", \"ой\", \"ий\", \"ян\", \"ник\", \"енко\", \"юк\", \"чук\", \"ман\", \"берг\", \"о\"]\n",
    "\n",
    "# Function to check for the presence of suffixes in a generated last name\n",
    "def check_suffixes(last_name):\n",
    "    for suffix in suffixes:\n",
    "        if last_name.endswith(suffix):\n",
    "            return True, suffix\n",
    "    return False, None\n",
    "# The result (True or False) is appended to the suffixes_eval list.\n",
    "suffixes_eval = []\n",
    "for last_name in predicted_last_names:\n",
    "  has_suffix, suffix = check_suffixes(last_name)\n",
    "  suffixes_eval.append(has_suffix)\n",
    "# Calculates the mean of the column, which represents the proportion of last names that contain one of the suffixes.\n",
    "df[\"has_suffix\"] = suffixes_eval\n",
    "df[\"has_suffix\"].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "id": "8wn7YaPh01e-",
    "outputId": "d2381ded-c766-45f9-db20-a77c22917b84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "233000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hg/l_2zgzbj1rv5jwmflpyb5y300000gn/T/ipykernel_17863/1759483325.py:11: DeprecationWarning: The 'text' argument to find()-type methods is deprecated. Use 'string' instead.\n",
      "  total_results_text = soup.find(\"div\", {\"id\": \"result-stats\"}).find(text=True, recursive=False) # this will give you the outer text which is like 'About 1,410,000,000 results'\n"
     ]
    }
   ],
   "source": [
    "# Evaluation 2: Search generated last names in Google.\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "#Specifies the URL of the Google search results page for the query \"NLP\" (example).\n",
    "headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36\"}\n",
    "URL     = \"https://www.google.com/search?q=NLP\"\n",
    "result = requests.get(URL, headers=headers)\n",
    "\n",
    "soup = BeautifulSoup(result.content, 'html.parser')\n",
    "#Finds the <div> element with the id \"result-stats\", which contains the total number of search results.\n",
    "total_results_text = soup.find(\"div\", {\"id\": \"result-stats\"}).find(text=True, recursive=False) # this will give you the outer text which is like 'About 1,410,000,000 results'\n",
    "results_num = ''.join([num for num in total_results_text if num.isdigit()]) # now will clean it up and remove all the characters that are not a number.\n",
    "print(results_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "id": "jTKVY_We2FHT"
   },
   "outputs": [],
   "source": [
    "# A function retrieves the number of search results for a given query name using Google search.\n",
    "\n",
    "def get_num_search(name):\n",
    "    headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.149 Safari/537.36\"}\n",
    "    URL     = \"https://www.google.com/search?q=\" + name\n",
    "    # Send a GET request to the constructed URL\n",
    "    result = requests.get(URL, headers=headers)\n",
    "    # Parse the HTML content of the search results page\n",
    "    soup = BeautifulSoup(result.content, 'html.parser')\n",
    "    # Find the total number of search results\n",
    "    total_results_text = soup.find(\"div\", {\"id\": \"result-stats\"}).find(text=True, recursive=False)\n",
    "    try:\n",
    "        # Extract the total number of search results\n",
    "        results_num = ''.join([num for num in total_results_text if num.isdigit()])\n",
    "    except:\n",
    "        print(total_results_text)\n",
    "        results_num = 0\n",
    "    return int(results_num)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 184
    },
    "id": "XCidRMz_4WGX",
    "outputId": "440ebfa4-6dbc-4ecd-968b-ecfe9a181419"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hg/l_2zgzbj1rv5jwmflpyb5y300000gn/T/ipykernel_17863/3661125507.py:11: DeprecationWarning: The 'text' argument to find()-type methods is deprecated. Use 'string' instead.\n",
      "  total_results_text = soup.find(\"div\", {\"id\": \"result-stats\"}).find(text=True, recursive=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "None\n",
      "None\n",
      "None\n",
      "None\n",
      "None\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Iterates through a list of last names called predicted_last_names and retrieves the number of Google search results for each last name.\n",
    "google_search_results = []\n",
    "for last_name in predicted_last_names:\n",
    "  google_search_results.append(get_num_search(last_name))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "X07AKVng8u_X",
    "outputId": "3fc876cf-cd5c-4d62-b424-a7a5cb4dbaa7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted last names</th>\n",
       "      <th>has_suffix</th>\n",
       "      <th>google_search_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Борашуев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Кашапейко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Евортин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Насолин</td>\n",
       "      <td>True</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Содоговшулов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ивастуиник</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Дериков</td>\n",
       "      <td>True</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Сутов</td>\n",
       "      <td>True</td>\n",
       "      <td>11200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Родойногулин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Улагов</td>\n",
       "      <td>True</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Шанов</td>\n",
       "      <td>True</td>\n",
       "      <td>101000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Крамов</td>\n",
       "      <td>True</td>\n",
       "      <td>32200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Чернышсов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Хомнов</td>\n",
       "      <td>True</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Безоретоментороробих</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Солговарьнин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Волеткин</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Кнувеев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Харшин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Бойко</td>\n",
       "      <td>True</td>\n",
       "      <td>15400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Адалов</td>\n",
       "      <td>True</td>\n",
       "      <td>5780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Солуженко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Папинов</td>\n",
       "      <td>True</td>\n",
       "      <td>4230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Брроздендоблунов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Клянов</td>\n",
       "      <td>True</td>\n",
       "      <td>4900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Яебюрдериков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Порбушкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Конлицкостухов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Шапин</td>\n",
       "      <td>True</td>\n",
       "      <td>35600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Циц</td>\n",
       "      <td>False</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ялезозчоков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Галастюнинаводиев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Высчеркин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Авлев</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Курпиев</td>\n",
       "      <td>True</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Шегтубьянкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Жиркин</td>\n",
       "      <td>True</td>\n",
       "      <td>8210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Нугданов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Оснроваледенев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Радев</td>\n",
       "      <td>True</td>\n",
       "      <td>7500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Парушкин</td>\n",
       "      <td>True</td>\n",
       "      <td>7380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Стешнявов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Варбин</td>\n",
       "      <td>True</td>\n",
       "      <td>4790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Бахругых</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Сербатонникрреницкий</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Вицкий</td>\n",
       "      <td>True</td>\n",
       "      <td>19700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Олеев</td>\n",
       "      <td>True</td>\n",
       "      <td>9510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ярологин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Халазок</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Вонякин</td>\n",
       "      <td>True</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Волов</td>\n",
       "      <td>True</td>\n",
       "      <td>1100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Орогкин</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Анеш</td>\n",
       "      <td>False</td>\n",
       "      <td>14500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Васюшев</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Востов</td>\n",
       "      <td>True</td>\n",
       "      <td>77800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Копалов</td>\n",
       "      <td>True</td>\n",
       "      <td>36200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Рябаров</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Азжанов</td>\n",
       "      <td>True</td>\n",
       "      <td>5130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Сурак</td>\n",
       "      <td>False</td>\n",
       "      <td>532000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Носдошкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Зелозников</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Лехибов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Сознипов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Аментин</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Солкин</td>\n",
       "      <td>True</td>\n",
       "      <td>41200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Иров</td>\n",
       "      <td>True</td>\n",
       "      <td>306000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Шаринцев</td>\n",
       "      <td>True</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Стеянин</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Анпрошкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Максинкин</td>\n",
       "      <td>True</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Жашев</td>\n",
       "      <td>True</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Заклитонов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Голадаренов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Костов</td>\n",
       "      <td>True</td>\n",
       "      <td>5000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Абошняк</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Соваройчук</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Смып</td>\n",
       "      <td>False</td>\n",
       "      <td>620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Кульмуньченко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Поратеваленов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Алишгов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Рышин</td>\n",
       "      <td>True</td>\n",
       "      <td>578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Васев</td>\n",
       "      <td>True</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Копетков</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Канов</td>\n",
       "      <td>True</td>\n",
       "      <td>197000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Васюлининородкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Воволов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Сойпон</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Атдейчичов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Осопьчков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Иньевйчков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Хохин</td>\n",
       "      <td>True</td>\n",
       "      <td>9550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Агранельпов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Антель</td>\n",
       "      <td>False</td>\n",
       "      <td>17500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Тиманыев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Саков</td>\n",
       "      <td>True</td>\n",
       "      <td>681000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Филчи</td>\n",
       "      <td>False</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Диунов</td>\n",
       "      <td>True</td>\n",
       "      <td>16200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Анасевыхин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Бафуловский</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Роменкуриковсков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    predicted last names  has_suffix  google_search_results\n",
       "0               Борашуев        True                      0\n",
       "1              Кашапейко        True                      0\n",
       "2                Евортин        True                      0\n",
       "3                Насолин        True                    335\n",
       "4           Содоговшулов        True                      0\n",
       "5             Ивастуиник        True                      0\n",
       "6                Дериков        True                   6100\n",
       "7                  Сутов        True                  11200\n",
       "8           Родойногулин        True                      0\n",
       "9                 Улагов        True                    801\n",
       "10                 Шанов        True                 101000\n",
       "11                Крамов        True                  32200\n",
       "12             Чернышсов        True                      0\n",
       "13                Хомнов        True                     52\n",
       "14  Безоретоментороробих        True                      0\n",
       "15          Солговарьнин        True                      0\n",
       "16              Волеткин        True                      1\n",
       "17               Кнувеев        True                      0\n",
       "18                Харшин        True                      0\n",
       "19                 Бойко        True               15400000\n",
       "20                Адалов        True                   5780\n",
       "21             Солуженко        True                      0\n",
       "22               Папинов        True                   4230\n",
       "23      Брроздендоблунов        True                      0\n",
       "24                Клянов        True                   4900\n",
       "25          Яебюрдериков        True                      0\n",
       "26             Порбушкин        True                      0\n",
       "27        Конлицкостухов        True                      0\n",
       "28                 Шапин        True                  35600\n",
       "29                   Циц       False                 160000\n",
       "30           Ялезозчоков        True                      0\n",
       "31     Галастюнинаводиев        True                      0\n",
       "32             Высчеркин        True                      0\n",
       "33                 Авлев        True                   1930\n",
       "34               Курпиев        True                    844\n",
       "35          Шегтубьянкин        True                      0\n",
       "36                Жиркин        True                   8210\n",
       "37              Нугданов        True                      0\n",
       "38        Оснроваледенев        True                      0\n",
       "39                 Радев        True                7500000\n",
       "40              Парушкин        True                   7380\n",
       "41             Стешнявов        True                      0\n",
       "42                Варбин        True                   4790\n",
       "43              Бахругых        True                      0\n",
       "44  Сербатонникрреницкий        True                      0\n",
       "45                Вицкий        True                  19700\n",
       "46                 Олеев        True                   9510\n",
       "47              Ярологин        True                      0\n",
       "48               Халазок       False                      0\n",
       "49               Вонякин        True                    114\n",
       "50                 Волов        True                1100000\n",
       "51               Орогкин        True                      1\n",
       "52                  Анеш       False                  14500\n",
       "53               Васюшев        True                      1\n",
       "54                Востов        True                  77800\n",
       "55               Копалов        True                  36200\n",
       "56               Рябаров        True                      2\n",
       "57               Азжанов        True                   5130\n",
       "58                 Сурак       False                 532000\n",
       "59             Носдошкин        True                      0\n",
       "60            Зелозников        True                      0\n",
       "61               Лехибов        True                      0\n",
       "62              Сознипов        True                      0\n",
       "63               Аментин        True                     57\n",
       "64                Солкин        True                  41200\n",
       "65                  Иров        True                 306000\n",
       "66              Шаринцев        True                    305\n",
       "67               Стеянин        True                      2\n",
       "68             Анпрошкин        True                      0\n",
       "69             Максинкин        True                     37\n",
       "70                 Жашев        True                    194\n",
       "71            Заклитонов        True                      0\n",
       "72           Голадаренов        True                      0\n",
       "73                Костов        True                5000000\n",
       "74               Абошняк       False                      1\n",
       "75            Соваройчук        True                      0\n",
       "76                  Смып       False                    620\n",
       "77         Кульмуньченко        True                      0\n",
       "78         Поратеваленов        True                      0\n",
       "79               Алишгов        True                      0\n",
       "80                 Рышин        True                    578\n",
       "81                 Васев        True                 400000\n",
       "82              Копетков        True                     63\n",
       "83                 Канов        True                 197000\n",
       "84      Васюлининородкин        True                      0\n",
       "85               Воволов        True                      0\n",
       "86                Сойпон       False                      0\n",
       "87            Атдейчичов        True                      0\n",
       "88             Осопьчков        True                      0\n",
       "89            Иньевйчков        True                      0\n",
       "90                 Хохин        True                   9550\n",
       "91           Агранельпов        True                      0\n",
       "92                Антель       False                  17500\n",
       "93              Тиманыев        True                      0\n",
       "94                 Саков        True                 681000\n",
       "95                 Филчи       False                    294\n",
       "96                Диунов        True                  16200\n",
       "97            Анасевыхин        True                      0\n",
       "98           Бафуловский        True                      0\n",
       "99      Роменкуриковсков        True                      0"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding a new column named \"google_search_results\" to the DataFrame.\n",
    "df[\"google_search_results\"] = google_search_results\n",
    "pd.set_option(\"display.max_row\", None)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "bcAoMO7p95xB",
    "outputId": "02f0fa79-48e7-4370-ffd8-664aaef259d0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted last names</th>\n",
       "      <th>has_suffix</th>\n",
       "      <th>google_search_results</th>\n",
       "      <th>searches_exceed_1000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Борашуев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Кашапейко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Евортин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Насолин</td>\n",
       "      <td>True</td>\n",
       "      <td>335</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Содоговшулов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ивастуиник</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Дериков</td>\n",
       "      <td>True</td>\n",
       "      <td>6100</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Сутов</td>\n",
       "      <td>True</td>\n",
       "      <td>11200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Родойногулин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Улагов</td>\n",
       "      <td>True</td>\n",
       "      <td>801</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Шанов</td>\n",
       "      <td>True</td>\n",
       "      <td>101000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Крамов</td>\n",
       "      <td>True</td>\n",
       "      <td>32200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Чернышсов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Хомнов</td>\n",
       "      <td>True</td>\n",
       "      <td>52</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Безоретоментороробих</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Солговарьнин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Волеткин</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Кнувеев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Харшин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Бойко</td>\n",
       "      <td>True</td>\n",
       "      <td>15400000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Адалов</td>\n",
       "      <td>True</td>\n",
       "      <td>5780</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Солуженко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Папинов</td>\n",
       "      <td>True</td>\n",
       "      <td>4230</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Брроздендоблунов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Клянов</td>\n",
       "      <td>True</td>\n",
       "      <td>4900</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Яебюрдериков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Порбушкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Конлицкостухов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Шапин</td>\n",
       "      <td>True</td>\n",
       "      <td>35600</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Циц</td>\n",
       "      <td>False</td>\n",
       "      <td>160000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Ялезозчоков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Галастюнинаводиев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Высчеркин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Авлев</td>\n",
       "      <td>True</td>\n",
       "      <td>1930</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Курпиев</td>\n",
       "      <td>True</td>\n",
       "      <td>844</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Шегтубьянкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Жиркин</td>\n",
       "      <td>True</td>\n",
       "      <td>8210</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Нугданов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Оснроваледенев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Радев</td>\n",
       "      <td>True</td>\n",
       "      <td>7500000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Парушкин</td>\n",
       "      <td>True</td>\n",
       "      <td>7380</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Стешнявов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Варбин</td>\n",
       "      <td>True</td>\n",
       "      <td>4790</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Бахругых</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Сербатонникрреницкий</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Вицкий</td>\n",
       "      <td>True</td>\n",
       "      <td>19700</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Олеев</td>\n",
       "      <td>True</td>\n",
       "      <td>9510</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Ярологин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Халазок</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Вонякин</td>\n",
       "      <td>True</td>\n",
       "      <td>114</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Волов</td>\n",
       "      <td>True</td>\n",
       "      <td>1100000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Орогкин</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Анеш</td>\n",
       "      <td>False</td>\n",
       "      <td>14500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Васюшев</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Востов</td>\n",
       "      <td>True</td>\n",
       "      <td>77800</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Копалов</td>\n",
       "      <td>True</td>\n",
       "      <td>36200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Рябаров</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Азжанов</td>\n",
       "      <td>True</td>\n",
       "      <td>5130</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Сурак</td>\n",
       "      <td>False</td>\n",
       "      <td>532000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Носдошкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Зелозников</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Лехибов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Сознипов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Аментин</td>\n",
       "      <td>True</td>\n",
       "      <td>57</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Солкин</td>\n",
       "      <td>True</td>\n",
       "      <td>41200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Иров</td>\n",
       "      <td>True</td>\n",
       "      <td>306000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Шаринцев</td>\n",
       "      <td>True</td>\n",
       "      <td>305</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Стеянин</td>\n",
       "      <td>True</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Анпрошкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Максинкин</td>\n",
       "      <td>True</td>\n",
       "      <td>37</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Жашев</td>\n",
       "      <td>True</td>\n",
       "      <td>194</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Заклитонов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Голадаренов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Костов</td>\n",
       "      <td>True</td>\n",
       "      <td>5000000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Абошняк</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Соваройчук</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Смып</td>\n",
       "      <td>False</td>\n",
       "      <td>620</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Кульмуньченко</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Поратеваленов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Алишгов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Рышин</td>\n",
       "      <td>True</td>\n",
       "      <td>578</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Васев</td>\n",
       "      <td>True</td>\n",
       "      <td>400000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Копетков</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Канов</td>\n",
       "      <td>True</td>\n",
       "      <td>197000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Васюлининородкин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Воволов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Сойпон</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Атдейчичов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Осопьчков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Иньевйчков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Хохин</td>\n",
       "      <td>True</td>\n",
       "      <td>9550</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Агранельпов</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Антель</td>\n",
       "      <td>False</td>\n",
       "      <td>17500</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Тиманыев</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Саков</td>\n",
       "      <td>True</td>\n",
       "      <td>681000</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Филчи</td>\n",
       "      <td>False</td>\n",
       "      <td>294</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Диунов</td>\n",
       "      <td>True</td>\n",
       "      <td>16200</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Анасевыхин</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Бафуловский</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Роменкуриковсков</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    predicted last names  has_suffix  google_search_results  \\\n",
       "0               Борашуев        True                      0   \n",
       "1              Кашапейко        True                      0   \n",
       "2                Евортин        True                      0   \n",
       "3                Насолин        True                    335   \n",
       "4           Содоговшулов        True                      0   \n",
       "5             Ивастуиник        True                      0   \n",
       "6                Дериков        True                   6100   \n",
       "7                  Сутов        True                  11200   \n",
       "8           Родойногулин        True                      0   \n",
       "9                 Улагов        True                    801   \n",
       "10                 Шанов        True                 101000   \n",
       "11                Крамов        True                  32200   \n",
       "12             Чернышсов        True                      0   \n",
       "13                Хомнов        True                     52   \n",
       "14  Безоретоментороробих        True                      0   \n",
       "15          Солговарьнин        True                      0   \n",
       "16              Волеткин        True                      1   \n",
       "17               Кнувеев        True                      0   \n",
       "18                Харшин        True                      0   \n",
       "19                 Бойко        True               15400000   \n",
       "20                Адалов        True                   5780   \n",
       "21             Солуженко        True                      0   \n",
       "22               Папинов        True                   4230   \n",
       "23      Брроздендоблунов        True                      0   \n",
       "24                Клянов        True                   4900   \n",
       "25          Яебюрдериков        True                      0   \n",
       "26             Порбушкин        True                      0   \n",
       "27        Конлицкостухов        True                      0   \n",
       "28                 Шапин        True                  35600   \n",
       "29                   Циц       False                 160000   \n",
       "30           Ялезозчоков        True                      0   \n",
       "31     Галастюнинаводиев        True                      0   \n",
       "32             Высчеркин        True                      0   \n",
       "33                 Авлев        True                   1930   \n",
       "34               Курпиев        True                    844   \n",
       "35          Шегтубьянкин        True                      0   \n",
       "36                Жиркин        True                   8210   \n",
       "37              Нугданов        True                      0   \n",
       "38        Оснроваледенев        True                      0   \n",
       "39                 Радев        True                7500000   \n",
       "40              Парушкин        True                   7380   \n",
       "41             Стешнявов        True                      0   \n",
       "42                Варбин        True                   4790   \n",
       "43              Бахругых        True                      0   \n",
       "44  Сербатонникрреницкий        True                      0   \n",
       "45                Вицкий        True                  19700   \n",
       "46                 Олеев        True                   9510   \n",
       "47              Ярологин        True                      0   \n",
       "48               Халазок       False                      0   \n",
       "49               Вонякин        True                    114   \n",
       "50                 Волов        True                1100000   \n",
       "51               Орогкин        True                      1   \n",
       "52                  Анеш       False                  14500   \n",
       "53               Васюшев        True                      1   \n",
       "54                Востов        True                  77800   \n",
       "55               Копалов        True                  36200   \n",
       "56               Рябаров        True                      2   \n",
       "57               Азжанов        True                   5130   \n",
       "58                 Сурак       False                 532000   \n",
       "59             Носдошкин        True                      0   \n",
       "60            Зелозников        True                      0   \n",
       "61               Лехибов        True                      0   \n",
       "62              Сознипов        True                      0   \n",
       "63               Аментин        True                     57   \n",
       "64                Солкин        True                  41200   \n",
       "65                  Иров        True                 306000   \n",
       "66              Шаринцев        True                    305   \n",
       "67               Стеянин        True                      2   \n",
       "68             Анпрошкин        True                      0   \n",
       "69             Максинкин        True                     37   \n",
       "70                 Жашев        True                    194   \n",
       "71            Заклитонов        True                      0   \n",
       "72           Голадаренов        True                      0   \n",
       "73                Костов        True                5000000   \n",
       "74               Абошняк       False                      1   \n",
       "75            Соваройчук        True                      0   \n",
       "76                  Смып       False                    620   \n",
       "77         Кульмуньченко        True                      0   \n",
       "78         Поратеваленов        True                      0   \n",
       "79               Алишгов        True                      0   \n",
       "80                 Рышин        True                    578   \n",
       "81                 Васев        True                 400000   \n",
       "82              Копетков        True                     63   \n",
       "83                 Канов        True                 197000   \n",
       "84      Васюлининородкин        True                      0   \n",
       "85               Воволов        True                      0   \n",
       "86                Сойпон       False                      0   \n",
       "87            Атдейчичов        True                      0   \n",
       "88             Осопьчков        True                      0   \n",
       "89            Иньевйчков        True                      0   \n",
       "90                 Хохин        True                   9550   \n",
       "91           Агранельпов        True                      0   \n",
       "92                Антель       False                  17500   \n",
       "93              Тиманыев        True                      0   \n",
       "94                 Саков        True                 681000   \n",
       "95                 Филчи       False                    294   \n",
       "96                Диунов        True                  16200   \n",
       "97            Анасевыхин        True                      0   \n",
       "98           Бафуловский        True                      0   \n",
       "99      Роменкуриковсков        True                      0   \n",
       "\n",
       "    searches_exceed_1000  \n",
       "0                  False  \n",
       "1                  False  \n",
       "2                  False  \n",
       "3                  False  \n",
       "4                  False  \n",
       "5                  False  \n",
       "6                   True  \n",
       "7                   True  \n",
       "8                  False  \n",
       "9                  False  \n",
       "10                  True  \n",
       "11                  True  \n",
       "12                 False  \n",
       "13                 False  \n",
       "14                 False  \n",
       "15                 False  \n",
       "16                 False  \n",
       "17                 False  \n",
       "18                 False  \n",
       "19                  True  \n",
       "20                  True  \n",
       "21                 False  \n",
       "22                  True  \n",
       "23                 False  \n",
       "24                  True  \n",
       "25                 False  \n",
       "26                 False  \n",
       "27                 False  \n",
       "28                  True  \n",
       "29                  True  \n",
       "30                 False  \n",
       "31                 False  \n",
       "32                 False  \n",
       "33                  True  \n",
       "34                 False  \n",
       "35                 False  \n",
       "36                  True  \n",
       "37                 False  \n",
       "38                 False  \n",
       "39                  True  \n",
       "40                  True  \n",
       "41                 False  \n",
       "42                  True  \n",
       "43                 False  \n",
       "44                 False  \n",
       "45                  True  \n",
       "46                  True  \n",
       "47                 False  \n",
       "48                 False  \n",
       "49                 False  \n",
       "50                  True  \n",
       "51                 False  \n",
       "52                  True  \n",
       "53                 False  \n",
       "54                  True  \n",
       "55                  True  \n",
       "56                 False  \n",
       "57                  True  \n",
       "58                  True  \n",
       "59                 False  \n",
       "60                 False  \n",
       "61                 False  \n",
       "62                 False  \n",
       "63                 False  \n",
       "64                  True  \n",
       "65                  True  \n",
       "66                 False  \n",
       "67                 False  \n",
       "68                 False  \n",
       "69                 False  \n",
       "70                 False  \n",
       "71                 False  \n",
       "72                 False  \n",
       "73                  True  \n",
       "74                 False  \n",
       "75                 False  \n",
       "76                 False  \n",
       "77                 False  \n",
       "78                 False  \n",
       "79                 False  \n",
       "80                 False  \n",
       "81                  True  \n",
       "82                 False  \n",
       "83                  True  \n",
       "84                 False  \n",
       "85                 False  \n",
       "86                 False  \n",
       "87                 False  \n",
       "88                 False  \n",
       "89                 False  \n",
       "90                  True  \n",
       "91                 False  \n",
       "92                  True  \n",
       "93                 False  \n",
       "94                  True  \n",
       "95                 False  \n",
       "96                  True  \n",
       "97                 False  \n",
       "98                 False  \n",
       "99                 False  "
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column \"searches_exceed_1000\" with Boolean values indicating whether the corresponding number of Google search results is greater than or equal to 1000 for each last name.\n",
    "df[\"searches_exceed_1000\"] = df[\"google_search_results\"]>=1000\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d8PegPHW-2iN",
    "outputId": "9eb049da-922c-430b-cbbd-0fceb4b9f6ab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#proportion of last names for which the number of Google search results exceeds or equals 1000\n",
    "df[\"searches_exceed_1000\"].sum()/len(df)"
   ]
  }
 ],
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