getting_data/extracting_occupation_and_employer_data_from_wikidata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extracting Occupation and Employer data from Wikidata\n",
    "This notebook extracts data from the Wikidata dump. It takes a long time to run and isn't very instructive, but it *does* allow the data gathering portion of this experiment to be easily repeated.\n",
    "To run this notebook:\n",
    "* first, download the wikidata json.bz2 dump\n",
    "* second, download the [Kensho Wikimedia derived dataset](https://www.kaggle.com/kenshoresearch/kensho-derived-wikimedia-data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bz2\n",
    "import json\n",
    "import os\n",
    "import pickle\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "# ! pip install Wikidata\n",
    "from wikidata.client import Client\n",
    "from random import sample\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 48G\r\n",
      "-rw-rw-r-- 1 todd todd 48G Mar 23 07:39 latest-all.json.bz2\r\n"
     ]
    }
   ],
   "source": [
    "! ls -lh /home/todd/projects/data/wikidata/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wikidata(filename):\n",
    "    with bz2.open(filename, mode='rt') as f:\n",
    "        f.read(2) # skip first two bytes: \"{\\n\"\n",
    "        for line in f:\n",
    "            try:\n",
    "                yield json.loads(line.rstrip(',\\n'))\n",
    "            except json.decoder.JSONDecodeError:\n",
    "                continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_cnt = Counter()\n",
    "emp_cnt = Counter()\n",
    "pos_cnt = Counter()\n",
    "\n",
    "def check_data (obj):\n",
    "    try:\n",
    "        if 'P106' in obj.get('claims',[]):   \n",
    "            for item in obj['claims']['P106']:\n",
    "                key =  item.get('mainsnak',{}).get('datavalue', {}).get('value',{}).get('id')\n",
    "                if key:\n",
    "                    occ_cnt.update({ key : 1 })          \n",
    "        if 'P101' in obj.get('claims',[]):   \n",
    "            for item in obj['claims']['P101']:\n",
    "                key =  item.get('mainsnak',{}).get('datavalue', {}).get('value',{}).get('id')\n",
    "                if key:\n",
    "                    pos_cnt.update({ key:1 })          \n",
    "        if 'P108' in obj.get('claims',[]):   \n",
    "            for item in obj['claims']['P108']:\n",
    "                key =  item.get('mainsnak',{}).get('datavalue', {}).get('value',{}).get('id')\n",
    "                if key:\n",
    "                    pos_cnt.update({ key : 1 })          \n",
    "    except Exception:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "78951018it [7:03:58, 3103.65it/s] \n"
     ]
    }
   ],
   "source": [
    "for record in tqdm(wikidata(os.path.expanduser('~/projects/data/wikidata/latest-all.json.bz2')):\n",
    "    check_data(record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12754, 66885, 0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(occ_cnt), len(pos_cnt), len(emp_cnt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Q82955', 606008),\n",
       " ('Q1650915', 488772),\n",
       " ('Q937857', 259012),\n",
       " ('Q36180', 245025),\n",
       " ('Q33999', 220615),\n",
       " ('Q1028181', 145927),\n",
       " ('Q1930187', 117705),\n",
       " ('Q1622272', 101818),\n",
       " ('Q177220', 92198),\n",
       " ('Q49757', 79887)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ_cnt.most_common(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('occupations.counts.wikidata.2020.03.22.pkl', 'wb') as fout:\n",
    "    pickle.dump(occ_cnt, fout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('positions.counts.wikidata.2020.03.22.pkl', 'wb') as fout:\n",
    "    pickle.dump(pos_cnt, fout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "occ_qcodes_counts ={ key[1:]: value for key, value in occ_cnt.items()}\n",
    "\n",
    "pos_qcode_counts = {key[1:]: value for key, value in pos_cnt.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_items</th>\n",
       "      <th>occupation_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>82955</td>\n",
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       "      <th>1</th>\n",
       "      <td>189290</td>\n",
       "      <td>33481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131512</td>\n",
       "      <td>8887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1734662</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294126</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  occupation_items  occupation_counts\n",
       "0            82955             606008\n",
       "1           189290              33481\n",
       "2           131512               8887\n",
       "3          1734662               2012\n",
       "4           294126                323"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys, cnts = zip(*occ_qcodes_counts.items())\n",
    "\n",
    "occ_df = pd.DataFrame({ \"occupation_items\": keys, \"occupation_counts\": cnts  }  )\n",
    "occ_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation_items     object\n",
       "occupation_counts     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "occupation_items     int32\n",
       "occupation_counts    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "occ_df = occ_df.astype({'occupation_items': 'int32'})\n",
    "occ_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Universe</td>\n",
       "      <td>totality of space and all contents</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Earth</td>\n",
       "      <td>third planet from the Sun in the Solar System</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>life</td>\n",
       "      <td>matter capable of extracting energy from the e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>death</td>\n",
       "      <td>permanent cessation of vital functions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>human</td>\n",
       "      <td>common name of Homo sapiens, unique extant spe...</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   item_id  en_label                                     en_description\n",
       "0        1  Universe                 totality of space and all contents\n",
       "1        2     Earth      third planet from the Sun in the Solar System\n",
       "2        3      life  matter capable of extracting energy from the e...\n",
       "3        4     death             permanent cessation of vital functions\n",
       "4        5     human  common name of Homo sapiens, unique extant spe..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df =  pd.read_csv('/home/todd/projects/data/kensho_wikidata/tmp/item.csv')\n",
    "items_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item_id            int64\n",
       "en_label          object\n",
       "en_description    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>occupation_items</th>\n",
       "      <th>occupation_counts</th>\n",
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       "      <td>person involved in politics, person who holds ...</td>\n",
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       "      <th>1</th>\n",
       "      <td>189290</td>\n",
       "      <td>33481</td>\n",
       "      <td>189290</td>\n",
       "      <td>military officer</td>\n",
       "      <td>member of an armed force or uniformed service ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131512</td>\n",
       "      <td>8887</td>\n",
       "      <td>131512</td>\n",
       "      <td>farmer</td>\n",
       "      <td>person that works in agriculture</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2012</td>\n",
       "      <td>1734662</td>\n",
       "      <td>cartographer</td>\n",
       "      <td>person preparing geographical maps</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294126</td>\n",
       "      <td>323</td>\n",
       "      <td>294126</td>\n",
       "      <td>land surveyor</td>\n",
       "      <td>profession</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   occupation_items  occupation_counts  item_id          en_label  \\\n",
       "0             82955             606008    82955        politician   \n",
       "1            189290              33481   189290  military officer   \n",
       "2            131512               8887   131512            farmer   \n",
       "3           1734662               2012  1734662      cartographer   \n",
       "4            294126                323   294126     land surveyor   \n",
       "\n",
       "                                      en_description  \n",
       "0  person involved in politics, person who holds ...  \n",
       "1  member of an armed force or uniformed service ...  \n",
       "2                   person that works in agriculture  \n",
       "3                 person preparing geographical maps  \n",
       "4                                         profession  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_occs = pd.merge(occ_df, items_df, left_on='occupation_items', right_on='item_id')\n",
    "new_occs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_item_id</th>\n",
       "      <th>occupation_counts</th>\n",
       "      <th>item_id</th>\n",
       "      <th>en_label</th>\n",
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       "      <td>military officer</td>\n",
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       "      <th>2</th>\n",
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       "      <td>farmer</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1734662</td>\n",
       "      <td>2012</td>\n",
       "      <td>1734662</td>\n",
       "      <td>cartographer</td>\n",
       "      <td>person preparing geographical maps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294126</td>\n",
       "      <td>323</td>\n",
       "      <td>294126</td>\n",
       "      <td>land surveyor</td>\n",
       "      <td>profession</td>\n",
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      ],
      "text/plain": [
       "   occupation_item_id  occupation_counts  item_id          en_label  \\\n",
       "0               82955             606008    82955        politician   \n",
       "1              189290              33481   189290  military officer   \n",
       "2              131512               8887   131512            farmer   \n",
       "3             1734662               2012  1734662      cartographer   \n",
       "4              294126                323   294126     land surveyor   \n",
       "\n",
       "                                      en_description  \n",
       "0  person involved in politics, person who holds ...  \n",
       "1  member of an armed force or uniformed service ...  \n",
       "2                   person that works in agriculture  \n",
       "3                 person preparing geographical maps  \n",
       "4                                         profession  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_occs  = new_occs.rename(columns={'occupation_items': 'occupation_item_id'})\n",
    "new_occs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11430"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_occs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "  position_item_id  position_counts\n",
       "0          2944031                9\n",
       "1           214126               53\n",
       "2            37230              493\n",
       "3             9531             1520\n",
       "4           134995               57"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys, cnts = zip(*pos_qcode_counts.items())\n",
    "\n",
    "pos_df = pd.DataFrame({ \"position_item_id\": keys, \"position_counts\": cnts  }  )\n",
    "pos_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "position_item_id    int64\n",
       "position_counts     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_df = pos_df.astype({'position_item_id': 'int64'})\n",
    "pos_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>position_item_id</th>\n",
       "      <th>position_counts</th>\n",
       "      <th>item_id</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2944031</td>\n",
       "      <td>9</td>\n",
       "      <td>2944031</td>\n",
       "      <td>Counter Terrorist Unit</td>\n",
       "      <td>fictional branch of the CIA from the televisio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214126</td>\n",
       "      <td>53</td>\n",
       "      <td>214126</td>\n",
       "      <td>Los Angeles Police Department</td>\n",
       "      <td>municipal police</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37230</td>\n",
       "      <td>493</td>\n",
       "      <td>37230</td>\n",
       "      <td>Central Intelligence Agency</td>\n",
       "      <td>national intelligence agency of the United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9531</td>\n",
       "      <td>1520</td>\n",
       "      <td>9531</td>\n",
       "      <td>BBC</td>\n",
       "      <td>British public service broadcaster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134995</td>\n",
       "      <td>57</td>\n",
       "      <td>134995</td>\n",
       "      <td>bibliography</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   position_item_id  position_counts  item_id                       en_label  \\\n",
       "0           2944031                9  2944031         Counter Terrorist Unit   \n",
       "1            214126               53   214126  Los Angeles Police Department   \n",
       "2             37230              493    37230    Central Intelligence Agency   \n",
       "3              9531             1520     9531                            BBC   \n",
       "4            134995               57   134995                   bibliography   \n",
       "\n",
       "                                      en_description  \n",
       "0  fictional branch of the CIA from the televisio...  \n",
       "1                                   municipal police  \n",
       "2  national intelligence agency of the United States  \n",
       "3                 British public service broadcaster  \n",
       "4                                                NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pos = pd.merge(pos_df, items_df, left_on='position_item_id', right_on='item_id')\n",
    "new_pos.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_pos.to_csv('wikidata.employers.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "position_item_id                                               645663\n",
       "position_counts                                                   745\n",
       "item_id                                                        645663\n",
       "en_label                                           University of Pisa\n",
       "en_description      Italian public research university located in ...\n",
       "Name: 1110, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pos.iloc[1110]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_occs.to_csv('wikidata.occupations.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64733"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_item_id</th>\n",
       "      <th>occupation_counts</th>\n",
       "      <th>item_id</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9947</th>\n",
       "      <td>1537376</td>\n",
       "      <td>1</td>\n",
       "      <td>1537376</td>\n",
       "      <td>grave robbery</td>\n",
       "      <td>act of uncovering a tomb or crypt to steal art...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4603</th>\n",
       "      <td>193036</td>\n",
       "      <td>1</td>\n",
       "      <td>193036</td>\n",
       "      <td>acrobatics</td>\n",
       "      <td>Performance of extraordinary human feats of ba...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10971</th>\n",
       "      <td>45176174</td>\n",
       "      <td>1</td>\n",
       "      <td>45176174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>2732142</td>\n",
       "      <td>2390</td>\n",
       "      <td>2732142</td>\n",
       "      <td>statistician</td>\n",
       "      <td>person who works with theoretical or applied s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8805</th>\n",
       "      <td>452961</td>\n",
       "      <td>2</td>\n",
       "      <td>452961</td>\n",
       "      <td>script</td>\n",
       "      <td>document describing the narrative and dialogue...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       occupation_item_id  occupation_counts   item_id       en_label  \\\n",
       "9947              1537376                  1   1537376  grave robbery   \n",
       "4603               193036                  1    193036     acrobatics   \n",
       "10971            45176174                  1  45176174            NaN   \n",
       "512               2732142               2390   2732142   statistician   \n",
       "8805               452961                  2    452961         script   \n",
       "\n",
       "                                          en_description  \n",
       "9947   act of uncovering a tomb or crypt to steal art...  \n",
       "4603   Performance of extraordinary human feats of ba...  \n",
       "10971                                                NaN  \n",
       "512    person who works with theoretical or applied s...  \n",
       "8805   document describing the narrative and dialogue...  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_occs.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>position_item_id</th>\n",
       "      <th>position_counts</th>\n",
       "      <th>item_id</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53577</th>\n",
       "      <td>30279877</td>\n",
       "      <td>1</td>\n",
       "      <td>30279877</td>\n",
       "      <td>Ostschweizer Kinderspital</td>\n",
       "      <td>healthcare organization in St. Gallen, Switzer...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15104</th>\n",
       "      <td>3254948</td>\n",
       "      <td>2</td>\n",
       "      <td>3254948</td>\n",
       "      <td>Diario de Cádiz</td>\n",
       "      <td>Spanish-language newspaper published in Cádiz,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34893</th>\n",
       "      <td>6971380</td>\n",
       "      <td>9</td>\n",
       "      <td>6971380</td>\n",
       "      <td>National Center for Toxicological Research</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38378</th>\n",
       "      <td>11622605</td>\n",
       "      <td>1</td>\n",
       "      <td>11622605</td>\n",
       "      <td>Ranshadō</td>\n",
       "      <td>private school in Yokohama city</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36004</th>\n",
       "      <td>9014504</td>\n",
       "      <td>1</td>\n",
       "      <td>9014504</td>\n",
       "      <td>Polaris Media</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       position_item_id  position_counts   item_id  \\\n",
       "53577          30279877                1  30279877   \n",
       "15104           3254948                2   3254948   \n",
       "34893           6971380                9   6971380   \n",
       "38378          11622605                1  11622605   \n",
       "36004           9014504                1   9014504   \n",
       "\n",
       "                                         en_label  \\\n",
       "53577                   Ostschweizer Kinderspital   \n",
       "15104                             Diario de Cádiz   \n",
       "34893  National Center for Toxicological Research   \n",
       "38378                                    Ranshadō   \n",
       "36004                               Polaris Media   \n",
       "\n",
       "                                          en_description  \n",
       "53577  healthcare organization in St. Gallen, Switzer...  \n",
       "15104  Spanish-language newspaper published in Cádiz,...  \n",
       "34893                                                NaN  \n",
       "38378                    private school in Yokohama city  \n",
       "36004                                                NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pos.sample(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(64733, 11430)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(new_pos), len(new_occs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12754, 66885)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(occ_qcodes_counts), len(pos_qcode_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find the missing item_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1324"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_occ_item_ids = {int(key) for key in occ_qcodes_counts}\n",
    "len(all_occ_item_ids)\n",
    "\n",
    "occ_item_id_from_df = new_occs['occupation_item_id'].tolist()\n",
    "\n",
    "len(occ_item_id_from_df)\n",
    "\n",
    "missing_occs  = set(all_occ_item_ids) - set(occ_item_id_from_df)\n",
    "len(missing_occs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2152"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_emp_item_ids = { int(key) for key in pos_qcode_counts}\n",
    "emp_item_id_from_df = new_pos['position_item_id'].tolist()\n",
    "missing_emps = set(all_emp_item_ids) - set(emp_item_id_from_df)\n",
    "len(missing_emps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetch missing occupations and employers from wikidata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
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     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "results =[]\n",
    "for item_id in tqdm(missing_occs, total=len(missing_occs)):\n",
    "    try:\n",
    "        entity = client.get(f\"Q{item_id}\", load=True)\n",
    "        cnt = occ_qcodes_counts.get(str(item_id ),0)\n",
    "        results.append( (item_id, str(entity.label) , str(entity.description), cnt))\n",
    "    except Exception as ex:\n",
    "        print(\"fail \", str(ex))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids, labels, descs, cnts= zip(*results)\n",
    "\n",
    "newer_occs = pd.DataFrame({\"occupation_item_id\": ids, \"occupation_counts\": cnts,\n",
    "                          \"en_label\": labels, \"en_description\": descs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_item_id</th>\n",
       "      <th>occupation_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>11293355</td>\n",
       "      <td>71</td>\n",
       "      <td>オートレース選手</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>1171796</td>\n",
       "      <td>1</td>\n",
       "      <td>Interventionism</td>\n",
       "      <td>Wikipedia disambiguation page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>10550654</td>\n",
       "      <td>1</td>\n",
       "      <td>Marinejeger</td>\n",
       "      <td>norsk militär</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>5973933</td>\n",
       "      <td>7</td>\n",
       "      <td>Letrados</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>11650597</td>\n",
       "      <td>2</td>\n",
       "      <td>録音技師</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      occupation_item_id  occupation_counts         en_label  \\\n",
       "850             11293355                 71         オートレース選手   \n",
       "60               1171796                  1  Interventionism   \n",
       "1232            10550654                  1      Marinejeger   \n",
       "333              5973933                  7         Letrados   \n",
       "274             11650597                  2             録音技師   \n",
       "\n",
       "                     en_description  \n",
       "850                                  \n",
       "60    Wikipedia disambiguation page  \n",
       "1232                  norsk militär  \n",
       "333                                  \n",
       "274                                  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newer_occs.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_item_id</th>\n",
       "      <th>occupation_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82955</td>\n",
       "      <td>606008</td>\n",
       "      <td>politician</td>\n",
       "      <td>person involved in politics, person who holds ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189290</td>\n",
       "      <td>33481</td>\n",
       "      <td>military officer</td>\n",
       "      <td>member of an armed force or uniformed service ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131512</td>\n",
       "      <td>8887</td>\n",
       "      <td>farmer</td>\n",
       "      <td>person that works in agriculture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1734662</td>\n",
       "      <td>2012</td>\n",
       "      <td>cartographer</td>\n",
       "      <td>person preparing geographical maps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294126</td>\n",
       "      <td>323</td>\n",
       "      <td>land surveyor</td>\n",
       "      <td>profession</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   occupation_item_id  occupation_counts          en_label  \\\n",
       "0               82955             606008        politician   \n",
       "1              189290              33481  military officer   \n",
       "2              131512               8887            farmer   \n",
       "3             1734662               2012      cartographer   \n",
       "4              294126                323     land surveyor   \n",
       "\n",
       "                                      en_description  \n",
       "0  person involved in politics, person who holds ...  \n",
       "1  member of an armed force or uniformed service ...  \n",
       "2                   person that works in agriculture  \n",
       "3                 person preparing geographical maps  \n",
       "4                                         profession  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_occs = pd.concat([new_occs, newer_occs], join=\"inner\")\n",
    "all_occs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_occs.to_csv('wikidata.occupations.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation_item_id</th>\n",
       "      <th>occupation_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82955</td>\n",
       "      <td>606008</td>\n",
       "      <td>politician</td>\n",
       "      <td>person involved in politics, person who holds ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>189290</td>\n",
       "      <td>33481</td>\n",
       "      <td>military officer</td>\n",
       "      <td>member of an armed force or uniformed service ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131512</td>\n",
       "      <td>8887</td>\n",
       "      <td>farmer</td>\n",
       "      <td>person that works in agriculture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1734662</td>\n",
       "      <td>2012</td>\n",
       "      <td>cartographer</td>\n",
       "      <td>person preparing geographical maps</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>294126</td>\n",
       "      <td>323</td>\n",
       "      <td>land surveyor</td>\n",
       "      <td>profession</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   occupation_item_id  occupation_counts          en_label  \\\n",
       "0               82955             606008        politician   \n",
       "1              189290              33481  military officer   \n",
       "2              131512               8887            farmer   \n",
       "3             1734662               2012      cartographer   \n",
       "4              294126                323     land surveyor   \n",
       "\n",
       "                                      en_description  \n",
       "0  person involved in politics, person who holds ...  \n",
       "1  member of an armed force or uniformed service ...  \n",
       "2                   person that works in agriculture  \n",
       "3                 person preparing geographical maps  \n",
       "4                                         profession  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_occs = pd.read_csv('wikidata.occupations.csv', sep='\\t')\n",
    "tmp_occs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "occupations in wikidata & kensho 11,430 occupations only in wikidata 1,324\n"
     ]
    }
   ],
   "source": [
    "print(f\"occupations in wikidata & kensho {len(new_occs):,} occupations only in wikidata {len(newer_occs):,}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_occs.to_csv('occupations.wikidata.csv', index=False, sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 18%|█▊        | 396/2152 [26:37<2:11:07,  4.48s/it]\u001B[A\u001B[A\n",
      "\n",
      " 18%|█▊        | 397/2152 [26:40<2:04:06,  4.24s/it]\u001B[A\u001B[A\n",
      "\n",
      " 18%|█▊        | 398/2152 [26:47<2:28:13,  5.07s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▊        | 399/2152 [26:51<2:12:35,  4.54s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▊        | 400/2152 [26:54<2:02:50,  4.21s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▊        | 401/2152 [26:57<1:51:06,  3.81s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▊        | 402/2152 [27:00<1:46:31,  3.65s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▊        | 403/2152 [27:04<1:43:17,  3.54s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 404/2152 [27:07<1:45:39,  3.63s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 405/2152 [27:10<1:36:27,  3.31s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 406/2152 [27:13<1:36:27,  3.31s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 407/2152 [27:23<2:37:00,  5.40s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 408/2152 [27:29<2:34:29,  5.32s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 409/2152 [27:35<2:46:36,  5.74s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 410/2152 [27:39<2:25:08,  5.00s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 411/2152 [27:42<2:12:14,  4.56s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 412/2152 [27:45<2:01:26,  4.19s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 413/2152 [27:49<1:51:46,  3.86s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 414/2152 [27:53<1:58:05,  4.08s/it]\u001B[A\u001B[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail  HTTP Error 404: Not Found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " 19%|█▉        | 415/2152 [27:57<1:54:09,  3.94s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
      " 19%|█▉        | 418/2152 [28:10<2:06:42,  4.38s/it]\u001B[A\u001B[A\n",
      "\n",
      " 19%|█▉        | 419/2152 [28:13<1:57:17,  4.06s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 420/2152 [28:16<1:50:33,  3.83s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 421/2152 [28:19<1:45:49,  3.67s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 422/2152 [28:22<1:39:58,  3.47s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 423/2152 [28:26<1:38:38,  3.42s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 424/2152 [28:31<1:50:20,  3.83s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 425/2152 [28:34<1:46:14,  3.69s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 426/2152 [28:37<1:42:33,  3.56s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 427/2152 [28:41<1:40:21,  3.49s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 428/2152 [28:44<1:38:37,  3.43s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 429/2152 [28:47<1:37:27,  3.39s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|█▉        | 430/2152 [28:50<1:36:26,  3.36s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|██        | 431/2152 [28:54<1:35:46,  3.34s/it]\u001B[A\u001B[A\n",
      "\n",
      " 20%|██        | 432/2152 [28:57<1:35:37,  3.34s/it]\u001B[A\u001B[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail  HTTP Error 404: Not Found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " 20%|██        | 433/2152 [29:00<1:35:30,  3.33s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 22%|██▏       | 464/2152 [30:52<1:27:31,  3.11s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
      " 22%|██▏       | 467/2152 [31:03<1:35:14,  3.39s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 468/2152 [31:12<2:22:07,  5.06s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 469/2152 [31:15<2:07:22,  4.54s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 470/2152 [31:18<1:57:00,  4.17s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 471/2152 [31:22<1:49:59,  3.93s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 472/2152 [31:26<1:56:56,  4.18s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 473/2152 [31:30<1:49:38,  3.92s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 474/2152 [31:33<1:44:37,  3.74s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 22%|██▏       | 478/2152 [31:46<1:33:06,  3.34s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
      " 22%|██▏       | 480/2152 [31:56<2:00:27,  4.32s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 481/2152 [32:00<1:58:36,  4.26s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 482/2152 [32:04<1:54:52,  4.13s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 483/2152 [32:07<1:48:01,  3.88s/it]\u001B[A\u001B[A\n",
      "\n",
      " 22%|██▏       | 484/2152 [32:18<2:47:29,  6.02s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 485/2152 [32:23<2:38:59,  5.72s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 486/2152 [32:27<2:18:59,  5.01s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 487/2152 [32:30<2:04:58,  4.50s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 488/2152 [32:33<1:52:22,  4.05s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 489/2152 [32:36<1:46:29,  3.84s/it]\u001B[A\u001B[A\n",
      "\n",
      " 23%|██▎       | 490/2152 [32:39<1:41:56,  3.68s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 23%|██▎       | 494/2152 [32:52<1:29:58,  3.26s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 24%|██▎       | 511/2152 [34:11<2:05:01,  4.57s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 25%|██▍       | 528/2152 [35:16<1:39:19,  3.67s/it]\u001B[A\u001B[A\n",
      "\n",
      " 25%|██▍       | 529/2152 [35:22<1:59:22,  4.41s/it]\u001B[A\u001B[A\n",
      "\n",
      " 25%|██▍       | 530/2152 [35:28<2:10:58,  4.85s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 705/2152 [47:01<1:22:19,  3.41s/it]\u001B[A\u001B[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail  HTTP Error 404: Not Found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 53%|█████▎    | 1142/2152 [1:18:04<55:56,  3.32s/it]\u001B[A\u001B[A\n",
      "\n",
      " 53%|█████▎    | 1143/2152 [1:18:09<1:00:29,  3.60s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 53%|█████▎    | 1148/2152 [1:18:34<1:18:16,  4.68s/it]\u001B[A\u001B[A\n",
      "\n",
      " 53%|█████▎    | 1149/2152 [1:18:36<1:00:20,  3.61s/it]\u001B[A\u001B[A\n",
      "\n",
      " 53%|█████▎    | 1150/2152 [1:18:39<58:49,  3.52s/it]  \u001B[A\u001B[A\n",
      "\n",
      " 53%|█████▎    | 1151/2152 [1:18:42<57:44,  3.46s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▎    | 1152/2152 [1:18:46<56:52,  3.41s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▎    | 1153/2152 [1:18:49<56:21,  3.38s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▎    | 1154/2152 [1:18:53<58:14,  3.50s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▎    | 1155/2152 [1:18:56<57:08,  3.44s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▎    | 1156/2152 [1:18:59<56:23,  3.40s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1157/2152 [1:19:02<54:23,  3.28s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1158/2152 [1:19:06<54:24,  3.28s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1159/2152 [1:19:14<1:21:00,  4.89s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1160/2152 [1:19:17<1:13:04,  4.42s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1161/2152 [1:19:20<1:05:57,  3.99s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1162/2152 [1:19:24<1:02:39,  3.80s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1163/2152 [1:19:27<1:00:10,  3.65s/it]\u001B[A\u001B[A\n",
      "\n",
      " 54%|█████▍    | 1164/2152 [1:19:30<58:31,  3.55s/it]  \u001B[A\u001B[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail  HTTP Error 404: Not Found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 86%|████████▌ | 1850/2152 [2:08:49<16:48,  3.34s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 86%|████████▋ | 1859/2152 [2:09:35<32:29,  6.65s/it]\u001B[A\u001B[A\n",
      "\n",
      " 86%|████████▋ | 1860/2152 [2:09:38<27:26,  5.64s/it]\u001B[A\u001B[A\n",
      "\n",
      " 86%|████████▋ | 1861/2152 [2:09:41<23:32,  4.85s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1862/2152 [2:09:44<21:22,  4.42s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1863/2152 [2:09:47<19:07,  3.97s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1864/2152 [2:09:50<18:05,  3.77s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1865/2152 [2:09:55<19:45,  4.13s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1866/2152 [2:09:57<15:43,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1867/2152 [2:10:00<15:40,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1868/2152 [2:10:03<15:36,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1869/2152 [2:10:07<15:34,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1870/2152 [2:10:15<22:32,  4.80s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1871/2152 [2:10:18<20:20,  4.34s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      " 87%|████████▋ | 1876/2152 [2:10:35<16:04,  3.50s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1877/2152 [2:10:39<15:46,  3.44s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1878/2152 [2:10:43<17:33,  3.85s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1879/2152 [2:10:47<17:08,  3.77s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1880/2152 [2:10:50<16:34,  3.66s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1881/2152 [2:10:55<18:30,  4.10s/it]\u001B[A\u001B[A\n",
      "\n",
      " 87%|████████▋ | 1882/2152 [2:10:57<14:33,  3.24s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1883/2152 [2:11:00<14:37,  3.26s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1884/2152 [2:11:03<14:33,  3.26s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1885/2152 [2:11:15<25:42,  5.78s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1886/2152 [2:11:18<22:19,  5.04s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1887/2152 [2:11:21<19:56,  4.52s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1888/2152 [2:11:25<18:17,  4.16s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1889/2152 [2:11:28<17:05,  3.90s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1890/2152 [2:11:31<16:13,  3.72s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1891/2152 [2:11:36<17:01,  3.91s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1892/2152 [2:11:38<14:44,  3.40s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1893/2152 [2:11:41<14:32,  3.37s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1894/2152 [2:11:44<14:10,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1895/2152 [2:11:48<14:09,  3.30s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1896/2152 [2:11:51<14:06,  3.31s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1897/2152 [2:11:54<14:12,  3.34s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1898/2152 [2:11:58<14:04,  3.32s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1899/2152 [2:12:01<14:14,  3.38s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1900/2152 [2:12:04<13:28,  3.21s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1901/2152 [2:12:07<13:30,  3.23s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1902/2152 [2:12:20<24:58,  5.99s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1903/2152 [2:12:23<21:28,  5.17s/it]\u001B[A\u001B[A\n",
      "\n",
      " 88%|████████▊ | 1904/2152 [2:12:26<19:02,  4.61s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▊ | 1905/2152 [2:12:30<17:26,  4.24s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▊ | 1906/2152 [2:12:33<16:14,  3.96s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▊ | 1907/2152 [2:12:36<15:03,  3.69s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▊ | 1908/2152 [2:12:40<15:10,  3.73s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▊ | 1909/2152 [2:12:42<13:38,  3.37s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1910/2152 [2:12:46<13:29,  3.35s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1911/2152 [2:12:49<13:27,  3.35s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1912/2152 [2:12:55<16:59,  4.25s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1913/2152 [2:12:59<16:00,  4.02s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1914/2152 [2:13:04<17:09,  4.32s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1915/2152 [2:13:07<15:51,  4.02s/it]\u001B[A\u001B[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fail  HTTP Error 404: Not Found\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " 89%|████████▉ | 1916/2152 [2:13:11<14:57,  3.81s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1917/2152 [2:13:20<21:18,  5.44s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1918/2152 [2:13:23<18:40,  4.79s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1919/2152 [2:13:26<16:52,  4.34s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1920/2152 [2:13:30<16:11,  4.19s/it]\u001B[A\u001B[A\n",
      "\n",
      " 89%|████████▉ | 1921/2152 [2:13:34<15:07,  3.93s/it]\u001B[A\u001B[A\n",
      "\n",
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      "\n",
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     ]
    }
   ],
   "source": [
    "results =[]\n",
    "for item_id in tqdm(missing_emps, total=len(missing_emps)):\n",
    "    try:\n",
    "        entity = client.get(f\"Q{item_id}\", load=True)\n",
    "        cnt = pos_qcode_counts.get(str(item_id ),0)\n",
    "        results.append( (item_id, str(entity.label) , str(entity.description), cnt))\n",
    "    except Exception as ex:\n",
    "        print(\"fail \", str(ex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employer_item_id</th>\n",
       "      <th>employer_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4268034</td>\n",
       "      <td>2</td>\n",
       "      <td>лугаводства</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58286088</td>\n",
       "      <td>1</td>\n",
       "      <td>iT law</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42303499</td>\n",
       "      <td>1</td>\n",
       "      <td>Краснабярэжскі дзяржаўны аграрны каледж</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>69812236</td>\n",
       "      <td>1</td>\n",
       "      <td>Мінскпраектмэбля</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84303891</td>\n",
       "      <td>1</td>\n",
       "      <td>Beaver Watershed Alliance</td>\n",
       "      <td>watershed conservation alliance in Arkansas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employer_item_id  employer_counts                                 en_label  \\\n",
       "0           4268034                2                              лугаводства   \n",
       "1          58286088                1                                   iT law   \n",
       "2          42303499                1  Краснабярэжскі дзяржаўны аграрны каледж   \n",
       "3          69812236                1                         Мінскпраектмэбля   \n",
       "4          84303891                1                Beaver Watershed Alliance   \n",
       "\n",
       "                                en_description  \n",
       "0                                               \n",
       "1                                               \n",
       "2                                               \n",
       "3                                               \n",
       "4  watershed conservation alliance in Arkansas  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids, labels, descs, cnts= zip(*results)\n",
    "\n",
    "newer_emps = pd.DataFrame({\"employer_item_id\": ids, \"employer_counts\": cnts,\n",
    "                          \"en_label\": labels, \"en_description\": descs})\n",
    "newer_emps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employer_item_id</th>\n",
       "      <th>employer_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2944031</td>\n",
       "      <td>9</td>\n",
       "      <td>Counter Terrorist Unit</td>\n",
       "      <td>fictional branch of the CIA from the televisio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214126</td>\n",
       "      <td>53</td>\n",
       "      <td>Los Angeles Police Department</td>\n",
       "      <td>municipal police</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37230</td>\n",
       "      <td>493</td>\n",
       "      <td>Central Intelligence Agency</td>\n",
       "      <td>national intelligence agency of the United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9531</td>\n",
       "      <td>1520</td>\n",
       "      <td>BBC</td>\n",
       "      <td>British public service broadcaster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134995</td>\n",
       "      <td>57</td>\n",
       "      <td>bibliography</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employer_item_id  employer_counts                       en_label  \\\n",
       "0           2944031                9         Counter Terrorist Unit   \n",
       "1            214126               53  Los Angeles Police Department   \n",
       "2             37230              493    Central Intelligence Agency   \n",
       "3              9531             1520                            BBC   \n",
       "4            134995               57                   bibliography   \n",
       "\n",
       "                                      en_description  \n",
       "0  fictional branch of the CIA from the televisio...  \n",
       "1                                   municipal police  \n",
       "2  national intelligence agency of the United States  \n",
       "3                 British public service broadcaster  \n",
       "4                                                NaN  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_pos = new_pos.drop(['item_id'], axis=1)\n",
    "new_emps  = new_pos.rename(columns={'position_item_id': 'employer_item_id',\n",
    "                                   'position_counts': 'employer_counts'            })\n",
    "new_emps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "employers in wikidata & kensho 64,733 employers only in wikidata 2,147\n"
     ]
    }
   ],
   "source": [
    "print(f\"employers in wikidata & kensho {len(new_emps):,} employers only in wikidata {len(newer_emps):,}\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>employer_item_id</th>\n",
       "      <th>employer_counts</th>\n",
       "      <th>en_label</th>\n",
       "      <th>en_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2944031</td>\n",
       "      <td>9</td>\n",
       "      <td>Counter Terrorist Unit</td>\n",
       "      <td>fictional branch of the CIA from the televisio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>214126</td>\n",
       "      <td>53</td>\n",
       "      <td>Los Angeles Police Department</td>\n",
       "      <td>municipal police</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37230</td>\n",
       "      <td>493</td>\n",
       "      <td>Central Intelligence Agency</td>\n",
       "      <td>national intelligence agency of the United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9531</td>\n",
       "      <td>1520</td>\n",
       "      <td>BBC</td>\n",
       "      <td>British public service broadcaster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134995</td>\n",
       "      <td>57</td>\n",
       "      <td>bibliography</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   employer_item_id  employer_counts                       en_label  \\\n",
       "0           2944031                9         Counter Terrorist Unit   \n",
       "1            214126               53  Los Angeles Police Department   \n",
       "2             37230              493    Central Intelligence Agency   \n",
       "3              9531             1520                            BBC   \n",
       "4            134995               57                   bibliography   \n",
       "\n",
       "                                      en_description  \n",
       "0  fictional branch of the CIA from the televisio...  \n",
       "1                                   municipal police  \n",
       "2  national intelligence agency of the United States  \n",
       "3                 British public service broadcaster  \n",
       "4                                                NaN  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_emps  = pd.concat([new_emps, newer_emps], join=\"inner\")\n",
    "all_emps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_emps.to_csv('employers.wikidata.csv', index=False, sep='\\t')"
   ]
  }
 ],
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