facepy iteration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/thomascmann/flatten-data\n",
    "import copy\n",
    "from collections.abc import MutableMapping, MutableSequence\n",
    "\n",
    "def flatten_data(data, simple=1):\n",
    "    out = []\n",
    "\n",
    "    def flatten(x, name='',subgroup=None):\n",
    "        if subgroup is None:\n",
    "            subgroup = [{}]\n",
    "        if isinstance(x, MutableMapping):\n",
    "            for a in x:\n",
    "                subgroup = flatten(x[a], ''.join([name, str(a), \"_\"]), subgroup)\n",
    "        elif isinstance(x, MutableSequence):\n",
    "            new_dicts = []\n",
    "            for a in x:\n",
    "                for dic in subgroup:\n",
    "                    newdict = flatten(a, name, copy.deepcopy([dic]))\n",
    "                    for i in newdict:\n",
    "                        new_dicts.append(i)\n",
    "            subgroup = new_dicts   \n",
    "        else:\n",
    "            for dic in subgroup:\n",
    "                dic[name[:-1]] = x\n",
    "        return subgroup\n",
    "\n",
    "    if simple == 1:\n",
    "        formatted_data = flatten(data)\n",
    "        for i in formatted_data:\n",
    "            out.append(i)\n",
    "    else:\n",
    "        for record in data:\n",
    "            if isinstance(data, MutableSequence):\n",
    "                formatted_data = flatten(record)\n",
    "            elif isinstance(data, MutableMapping):\n",
    "                formatted_data = flatten(data[record])\n",
    "                for i in formatted_data:\n",
    "                    i[\"_ID_VAR_\"] = record\n",
    "\n",
    "            for i in formatted_data:\n",
    "                out.append(i)\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: flatten_json in /usr/local/lib/python3.6/dist-packages (0.1.6)\n",
      "Requirement already satisfied: facepy in /usr/local/lib/python3.6/dist-packages (1.0.9)\n",
      "Requirement already satisfied: six>=1.6 in /usr/local/lib/python3.6/dist-packages (from facepy) (1.12.0)\n",
      "Requirement already satisfied: requests>=0.8 in /usr/local/lib/python3.6/dist-packages (from facepy) (2.21.0)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=0.8->facepy) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=0.8->facepy) (2018.11.29)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=0.8->facepy) (1.24.1)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=0.8->facepy) (3.0.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install flatten_json\n",
    "!pip install facepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from facepy import GraphAPI\n",
    "import pandas as pd\n",
    "from pandas.io.json import json_normalize\n",
    "from flatten_json import flatten #https://medium.com/@amirziai/flatten-json-on-python-package-index-pypi-9e4951693a5a\n",
    "# Initialize the Graph API with a valid access token (optional,\n",
    "# but will allow you to do all sorts of fun stuff).\n",
    "graph = GraphAPI('YOUR_KEY')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial result shape (90, 13)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(174,)\n",
      "(232,)\n",
      "(297,)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-93-9168f4d16b73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m             \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m600\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import time\n",
    "#added limit at the end of post query. 100 seems to be max for each call\n",
    "#&after= was added to iteration\n",
    "query='germany'\n",
    "places=graph.post('search?method=GET&type=place&q='+str(query)+'&fields=name,location,rating_count,category_list&center=52.5500361,13.3372237&limit=90')\n",
    "pager=places['paging']['next'][places['paging']['next'].find('after=')+len('after='):]\n",
    "#pager=places_tmp['paging']['cursors']['after']\n",
    "if 'headers' in places:\n",
    "    del places['headers']\n",
    "if 'paging' in places:\n",
    "    del places['paging']\n",
    "flatdf=json_normalize(flatten_data(places))\n",
    "#https://stackoverflow.com/questions/45918559/python-merging-rows-with-same-value-in-one-column\n",
    "cat_df_id=flatdf.groupby('data_id').data_category_list_name.apply(lambda x : x.tolist()).to_frame()['data_category_list_name'].apply(pd.Series).add_prefix('Category_').fillna(' ')\n",
    "#delete categories columns\n",
    "flatdf = flatdf.drop(['data_category_list_id', 'data_category_list_name'], axis=1)\n",
    "flatdf=flatdf.drop_duplicates()\n",
    "#merge datasets\n",
    "result=pd.merge(flatdf,cat_df_id, left_on='data_id', right_on='data_id')\n",
    "print('Initial result shape',result.shape)\n",
    "i=0\n",
    "n=20\n",
    "size=0\n",
    "t=0\n",
    "#for limits see this post. It's a little old but makes sense.\n",
    "#https://stackoverflow.com/questions/14092989/facebook-api-4-application-request-limit-reached\n",
    "for i in range(i,n):\n",
    "    print(result['data_id'].unique().shape)\n",
    "    if size-90*t <= 510:\n",
    "        places_tmp=graph.post('search?method=GET&type=place&q='+str(query)+'&fields=name,location,rating_count,category_list&limit=90&after='+str(pager))\n",
    "        #print(places_tmp['paging']['cursors']) #KeyError: 'after' means there are no more pages\n",
    "        #print(pager)\n",
    "        try:\n",
    "            #print(places_tmp['paging']['cursors']['after'])\n",
    "            #pager=places_tmp['paging']['next'][places_tmp['paging']['next'].find('after=')+len('after='):]\n",
    "            pager=places_tmp['paging']['cursors']['after'] \n",
    "            del places_tmp['headers']\n",
    "            del places_tmp['paging']\n",
    "            flatdf_tmp=json_normalize(flatten_data(places_tmp))\n",
    "            cat_tmp=flatdf_tmp.groupby('data_id').data_category_list_name.apply(lambda x : x.tolist()).to_frame()['data_category_list_name'].apply(pd.Series).add_prefix('Category_').fillna(' ')\n",
    "            flatdf_tmp = flatdf_tmp.drop(['data_category_list_id', 'data_category_list_name'], axis=1)\n",
    "            result_tmp=pd.merge(flatdf_tmp,cat_tmp, left_on='data_id', right_on='data_id')\n",
    "            result=result.append(result_tmp)\n",
    "            #print(result['data_id'].unique().shape)\n",
    "            result.to_csv(str(i)+'_fb_germany_places.csv')\n",
    "            size=result.shape[0]\n",
    "        except: \n",
    "            print('end', i)\n",
    "            break\n",
    "    else:\n",
    "        time.sleep(600)\n",
    "        t=t+1\n",
    "    \n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['data_id'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'OTkZD'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tests\n",
    "places=graph.get('search?method=GET&type=place&q=berlin&fields=name,location,rating_count,category_list&center=52.5500361,13.3372237&limit=100')\n",
    "places['paging']['next'][places['paging']['next'].find('after=')+len('after='):]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(552, 14)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "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>Category_0</th>\n",
       "      <th>Category_1</th>\n",
       "      <th>Category_2</th>\n",
       "      <th>Category_3</th>\n",
       "      <th>data_id</th>\n",
       "      <th>data_location_city</th>\n",
       "      <th>data_location_country</th>\n",
       "      <th>data_location_latitude</th>\n",
       "      <th>data_location_located_in</th>\n",
       "      <th>data_location_longitude</th>\n",
       "      <th>data_location_street</th>\n",
       "      <th>data_location_zip</th>\n",
       "      <th>data_name</th>\n",
       "      <th>data_rating_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Country</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>279516318736353</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alemania</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Region</td>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td>213549102002249</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.518391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.401251</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Berlín</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>City</td>\n",
       "      <td>National Park</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>187343588090963</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.516380914216</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.378899151666</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandenburg Gate, Berlin, Germany</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>327812700574720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.395474014562</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.518712790547</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brandenburg, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>602289129838094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.4985949</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.3847854</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Berlin, Germany - Europe</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>345774065433032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.894107570617</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.583000803261</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bayern, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Airport Terminal</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>320746585017122</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.552082365401</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.291771015536</td>\n",
       "      <td>andel's</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tegel Airport, Berlin, Germany</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>278456442208672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.430394114111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.2463729858642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Baden-Württemberg, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>220328594660274</td>\n",
       "      <td>Tiergarten</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.3667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiergarten, Berlin, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>252621764809436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.41939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.404526</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nordrhein-Westfalen, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>Geo Entity</td>\n",
       "      <td>213497202008107</td>\n",
       "      <td>Prenzlauer Berg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.5333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Prenzlauer Berg, Berlin, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>365323436817673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.568884855168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0296906801049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hessen, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>327038383983055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52.196537074042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.461939636541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Niedersachsen, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>223665927647896</td>\n",
       "      <td>Kreuzberg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.4965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.4116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kreuzberg, Berlin, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Region</td>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td>224899364192519</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>53.55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hamburgo</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>250614515007657</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.9142197796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.0914401814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thüringen, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>225406770806694</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Múnich</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>252874131450649</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.1124041071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.1914410554</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sachsen, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>216020361818814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.9309710419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51207846423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rheinland-Pfalz, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209890965698578</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.1154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fráncfort del Meno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>260149827384740</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.32013168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.09639292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Saarland, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>220921891251928</td>\n",
       "      <td>Cologne</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.9667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Colonia (Alemania)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>Geo Entity</td>\n",
       "      <td>199168250125618</td>\n",
       "      <td>Bogenhausen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.152051374255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.616418843701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bogenhausen, Bayern, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>200260546675783</td>\n",
       "      <td>Dresden</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.0416</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.7333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dresde</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>219791978047209</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.7823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.1739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stuttgart</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td>Neighborhood</td>\n",
       "      <td>Geo Entity</td>\n",
       "      <td>201660089869571</td>\n",
       "      <td>Wilmersdorf</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.4833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.3167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wilmersdorf, Berlin, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>215523831810313</td>\n",
       "      <td>Hanover</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.3727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hannover</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209905559033024</td>\n",
       "      <td>Leipzig</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.3396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.3771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Leipzig</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Region</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>288169214566205</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.210642531421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.7728945945643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Schleswig-Holstein, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>216034838426105</td>\n",
       "      <td>Potsdam</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Potsdam</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Apparel &amp; Clothing</td>\n",
       "      <td>Clothing (Brand)</td>\n",
       "      <td>Business Service</td>\n",
       "      <td></td>\n",
       "      <td>1578882385473767</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.09102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.59931</td>\n",
       "      <td>Stroofstr. 10</td>\n",
       "      <td>65933</td>\n",
       "      <td>LIN BEESER FASHION</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Media/News Company</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>952784478185115</td>\n",
       "      <td>Adlershof</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.42774</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.54491</td>\n",
       "      <td>Am Studio 16</td>\n",
       "      <td>12489</td>\n",
       "      <td>RT Deutsch</td>\n",
       "      <td>2769.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>848827278488534</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>53.582033333333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.084261111111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22885</td>\n",
       "      <td>Wandsbek</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Shopping &amp; Retail</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>134036950064707</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>Germany</td>\n",
       "      <td>53.549599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.947339</td>\n",
       "      <td>Virchowstrasse 2 / Hinterhof</td>\n",
       "      <td>22767</td>\n",
       "      <td>Allike Store</td>\n",
       "      <td>262.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Neighborhood</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>820895637974669</td>\n",
       "      <td>Saarbrücken</td>\n",
       "      <td>Germany</td>\n",
       "      <td>49.2414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.99056</td>\n",
       "      <td>Am Hauptbahnhof 6-12</td>\n",
       "      <td>66111</td>\n",
       "      <td>Saarbrücken Hauptbahnhof</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209890965698578</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.1154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fráncfort del Meno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209890965698578</td>\n",
       "      <td>Frankfurt</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.1154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.6709</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fráncfort del Meno</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Performance &amp; Event Venue</td>\n",
       "      <td>Halfway House</td>\n",
       "      <td>Stadium, Arena &amp; Sports Venue</td>\n",
       "      <td></td>\n",
       "      <td>300355903349822</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.174898764397</td>\n",
       "      <td>328584450515374</td>\n",
       "      <td>11.550224195079</td>\n",
       "      <td>Spiridon-Louis-Ring 21</td>\n",
       "      <td>80809</td>\n",
       "      <td>Olympiahalle München</td>\n",
       "      <td>3972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Performance &amp; Event Venue</td>\n",
       "      <td>Halfway House</td>\n",
       "      <td>Stadium, Arena &amp; Sports Venue</td>\n",
       "      <td></td>\n",
       "      <td>300355903349822</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.174898764397</td>\n",
       "      <td>328584450515374</td>\n",
       "      <td>11.550224195079</td>\n",
       "      <td>Spiridon-Louis-Ring 21</td>\n",
       "      <td>80809</td>\n",
       "      <td>Olympiahalle München</td>\n",
       "      <td>3972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Performance &amp; Event Venue</td>\n",
       "      <td>Halfway House</td>\n",
       "      <td>Stadium, Arena &amp; Sports Venue</td>\n",
       "      <td></td>\n",
       "      <td>300355903349822</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.174898764397</td>\n",
       "      <td>328584450515374</td>\n",
       "      <td>11.550224195079</td>\n",
       "      <td>Spiridon-Louis-Ring 21</td>\n",
       "      <td>80809</td>\n",
       "      <td>Olympiahalle München</td>\n",
       "      <td>3972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209549255732022</td>\n",
       "      <td>Aachen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.7753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aquisgrán</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>209549255732022</td>\n",
       "      <td>Aachen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.7753</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0828</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Aquisgrán</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>217866064907748</td>\n",
       "      <td>Hagen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.3667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hagen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>217866064907748</td>\n",
       "      <td>Hagen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.3667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hagen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>213131915377395</td>\n",
       "      <td>Garmisch-Partenkirchen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>47.49291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.08681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Garmisch-Partenkirchen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>213131915377395</td>\n",
       "      <td>Garmisch-Partenkirchen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>47.49291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.08681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Garmisch-Partenkirchen</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Just For Fun</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>685540861610085</td>\n",
       "      <td>Stadtallendorf</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.816</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0128299</td>\n",
       "      <td>niederkleiner Str 51</td>\n",
       "      <td>35260</td>\n",
       "      <td>Soferi din Romania</td>\n",
       "      <td>733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>202776866427991</td>\n",
       "      <td>Berau</td>\n",
       "      <td>Germany</td>\n",
       "      <td>47.694696379967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2567278111553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Berau, Baden-Wurttemberg, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>202776866427991</td>\n",
       "      <td>Berau</td>\n",
       "      <td>Germany</td>\n",
       "      <td>47.694696379967</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2567278111553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Berau, Baden-Wurttemberg, Germany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>224419624240849</td>\n",
       "      <td>Koblenz</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.3597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Coblenza</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>224419624240849</td>\n",
       "      <td>Koblenz</td>\n",
       "      <td>Germany</td>\n",
       "      <td>50.3597</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.5978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Coblenza</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>Music Chart</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1311126592393971</td>\n",
       "      <td>Ahlen</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.76316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.88636</td>\n",
       "      <td>weststr.92</td>\n",
       "      <td>59227</td>\n",
       "      <td>نبضات سهر</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>215414725153207</td>\n",
       "      <td>Paderborn</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.717086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.754933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paderborn</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>215414725153207</td>\n",
       "      <td>Paderborn</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.717086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.754933</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Paderborn</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Arts &amp; Entertainment</td>\n",
       "      <td>Tattoo &amp; Piercing Shop</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>728108033947217</td>\n",
       "      <td>Eging</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.5736</td>\n",
       "      <td>1703566273255164</td>\n",
       "      <td>13.4212399</td>\n",
       "      <td>Regensburger Straße 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tattoo Circle Worldwide</td>\n",
       "      <td>985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Arts &amp; Entertainment</td>\n",
       "      <td>Tattoo &amp; Piercing Shop</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>728108033947217</td>\n",
       "      <td>Eging</td>\n",
       "      <td>Germany</td>\n",
       "      <td>48.5736</td>\n",
       "      <td>1703566273255164</td>\n",
       "      <td>13.4212399</td>\n",
       "      <td>Regensburger Straße 34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tattoo Circle Worldwide</td>\n",
       "      <td>985.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>215523831810313</td>\n",
       "      <td>Hanover</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.3727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hannover</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>215523831810313</td>\n",
       "      <td>Hanover</td>\n",
       "      <td>Germany</td>\n",
       "      <td>52.3727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.738</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Hannover</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>199905860050441</td>\n",
       "      <td>Wuppertal</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.2667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.18333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wuppertal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>City</td>\n",
       "      <td>City</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>199905860050441</td>\n",
       "      <td>Wuppertal</td>\n",
       "      <td>Germany</td>\n",
       "      <td>51.2667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.18333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wuppertal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>552 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Category_0              Category_1  \\\n",
       "0                      Country                           \n",
       "1                       Region                    City   \n",
       "2                         City           National Park   \n",
       "3                       Region                           \n",
       "4                         City                           \n",
       "5                       Region                           \n",
       "6             Airport Terminal                    City   \n",
       "7                       Region                           \n",
       "8                         City                    City   \n",
       "9                       Region                           \n",
       "10                        City                    City   \n",
       "11                      Region                           \n",
       "12                      Region                           \n",
       "13                        City                    City   \n",
       "14                      Region                    City   \n",
       "15                      Region                           \n",
       "16                        City                    City   \n",
       "17                      Region                           \n",
       "18                      Region                           \n",
       "19                        City                    City   \n",
       "20                      Region                           \n",
       "21                        City                    City   \n",
       "22                        City                    City   \n",
       "23                        City                    City   \n",
       "24                        City                    City   \n",
       "25                        City                    City   \n",
       "26                        City                    City   \n",
       "27                        City                    City   \n",
       "28                      Region                           \n",
       "29                        City                    City   \n",
       "..                         ...                     ...   \n",
       "114         Apparel & Clothing        Clothing (Brand)   \n",
       "115         Media/News Company                           \n",
       "116               Neighborhood                           \n",
       "117          Shopping & Retail                           \n",
       "118               Neighborhood                           \n",
       "119                       City                    City   \n",
       "120                       City                    City   \n",
       "121  Performance & Event Venue           Halfway House   \n",
       "122  Performance & Event Venue           Halfway House   \n",
       "123  Performance & Event Venue           Halfway House   \n",
       "124                       City                    City   \n",
       "125                       City                    City   \n",
       "126                       City                    City   \n",
       "127                       City                    City   \n",
       "128                       City                    City   \n",
       "129                       City                    City   \n",
       "130               Just For Fun                           \n",
       "131                       City                    City   \n",
       "132                       City                    City   \n",
       "133                       City                    City   \n",
       "134                       City                    City   \n",
       "135                Music Chart                           \n",
       "136                       City                    City   \n",
       "137                       City                    City   \n",
       "138       Arts & Entertainment  Tattoo & Piercing Shop   \n",
       "139       Arts & Entertainment  Tattoo & Piercing Shop   \n",
       "140                       City                    City   \n",
       "141                       City                    City   \n",
       "142                       City                    City   \n",
       "143                       City                    City   \n",
       "\n",
       "                        Category_2  Category_3           data_id  \\\n",
       "0                                                279516318736353   \n",
       "1                             City               213549102002249   \n",
       "2                                                187343588090963   \n",
       "3                                                327812700574720   \n",
       "4                                                602289129838094   \n",
       "5                                                345774065433032   \n",
       "6                                                320746585017122   \n",
       "7                                                278456442208672   \n",
       "8                                                220328594660274   \n",
       "9                                                252621764809436   \n",
       "10                    Neighborhood  Geo Entity   213497202008107   \n",
       "11                                               365323436817673   \n",
       "12                                               327038383983055   \n",
       "13                                               223665927647896   \n",
       "14                            City               224899364192519   \n",
       "15                                               250614515007657   \n",
       "16                                               225406770806694   \n",
       "17                                               252874131450649   \n",
       "18                                               216020361818814   \n",
       "19                                               209890965698578   \n",
       "20                                               260149827384740   \n",
       "21                                               220921891251928   \n",
       "22                    Neighborhood  Geo Entity   199168250125618   \n",
       "23                                               200260546675783   \n",
       "24                                               219791978047209   \n",
       "25                    Neighborhood  Geo Entity   201660089869571   \n",
       "26                                               215523831810313   \n",
       "27                                               209905559033024   \n",
       "28                                               288169214566205   \n",
       "29                                               216034838426105   \n",
       "..                             ...         ...               ...   \n",
       "114               Business Service              1578882385473767   \n",
       "115                                              952784478185115   \n",
       "116                                              848827278488534   \n",
       "117                                              134036950064707   \n",
       "118                                              820895637974669   \n",
       "119                                              209890965698578   \n",
       "120                                              209890965698578   \n",
       "121  Stadium, Arena & Sports Venue               300355903349822   \n",
       "122  Stadium, Arena & Sports Venue               300355903349822   \n",
       "123  Stadium, Arena & Sports Venue               300355903349822   \n",
       "124                                              209549255732022   \n",
       "125                                              209549255732022   \n",
       "126                                              217866064907748   \n",
       "127                                              217866064907748   \n",
       "128                                              213131915377395   \n",
       "129                                              213131915377395   \n",
       "130                                              685540861610085   \n",
       "131                                              202776866427991   \n",
       "132                                              202776866427991   \n",
       "133                                              224419624240849   \n",
       "134                                              224419624240849   \n",
       "135                                             1311126592393971   \n",
       "136                                              215414725153207   \n",
       "137                                              215414725153207   \n",
       "138                                              728108033947217   \n",
       "139                                              728108033947217   \n",
       "140                                              215523831810313   \n",
       "141                                              215523831810313   \n",
       "142                                              199905860050441   \n",
       "143                                              199905860050441   \n",
       "\n",
       "         data_location_city data_location_country data_location_latitude  \\\n",
       "0                       NaN                   NaN                     51   \n",
       "1                    Berlin               Germany              52.518391   \n",
       "2                       NaN                   NaN        52.516380914216   \n",
       "3                       NaN                   NaN        52.395474014562   \n",
       "4                       NaN                   NaN             52.4985949   \n",
       "5                       NaN                   NaN        48.894107570617   \n",
       "6                    Berlin               Germany        52.552082365401   \n",
       "7                       NaN                   NaN        48.430394114111   \n",
       "8                Tiergarten               Germany                52.5167   \n",
       "9                       NaN                   NaN               51.41939   \n",
       "10          Prenzlauer Berg               Germany                52.5333   \n",
       "11                      NaN                   NaN        50.568884855168   \n",
       "12                      NaN                   NaN        52.196537074042   \n",
       "13                Kreuzberg               Germany                52.4965   \n",
       "14                  Hamburg               Germany                  53.55   \n",
       "15                      NaN                   NaN          50.9142197796   \n",
       "16                   Munich               Germany                  48.15   \n",
       "17                      NaN                   NaN          51.1124041071   \n",
       "18                      NaN                   NaN          49.9309710419   \n",
       "19                Frankfurt               Germany                50.1154   \n",
       "20                      NaN                   NaN            49.32013168   \n",
       "21                  Cologne               Germany                  50.95   \n",
       "22              Bogenhausen               Germany        48.152051374255   \n",
       "23                  Dresden               Germany                51.0416   \n",
       "24                Stuttgart               Germany                48.7823   \n",
       "25              Wilmersdorf               Germany                52.4833   \n",
       "26                  Hanover               Germany                52.3727   \n",
       "27                  Leipzig               Germany                51.3396   \n",
       "28                      NaN                   NaN        54.210642531421   \n",
       "29                  Potsdam               Germany                   52.4   \n",
       "..                      ...                   ...                    ...   \n",
       "114               Frankfurt               Germany               50.09102   \n",
       "115               Adlershof               Germany               52.42774   \n",
       "116                 Hamburg               Germany        53.582033333333   \n",
       "117                 Hamburg               Germany              53.549599   \n",
       "118             Saarbrücken               Germany                49.2414   \n",
       "119               Frankfurt               Germany                50.1154   \n",
       "120               Frankfurt               Germany                50.1154   \n",
       "121                  Munich               Germany        48.174898764397   \n",
       "122                  Munich               Germany        48.174898764397   \n",
       "123                  Munich               Germany        48.174898764397   \n",
       "124                  Aachen               Germany                50.7753   \n",
       "125                  Aachen               Germany                50.7753   \n",
       "126                   Hagen               Germany                51.3667   \n",
       "127                   Hagen               Germany                51.3667   \n",
       "128  Garmisch-Partenkirchen               Germany               47.49291   \n",
       "129  Garmisch-Partenkirchen               Germany               47.49291   \n",
       "130          Stadtallendorf               Germany                 50.816   \n",
       "131                   Berau               Germany        47.694696379967   \n",
       "132                   Berau               Germany        47.694696379967   \n",
       "133                 Koblenz               Germany                50.3597   \n",
       "134                 Koblenz               Germany                50.3597   \n",
       "135                   Ahlen               Germany               51.76316   \n",
       "136               Paderborn               Germany              51.717086   \n",
       "137               Paderborn               Germany              51.717086   \n",
       "138                   Eging               Germany                48.5736   \n",
       "139                   Eging               Germany                48.5736   \n",
       "140                 Hanover               Germany                52.3727   \n",
       "141                 Hanover               Germany                52.3727   \n",
       "142               Wuppertal               Germany                51.2667   \n",
       "143               Wuppertal               Germany                51.2667   \n",
       "\n",
       "    data_location_located_in data_location_longitude  \\\n",
       "0                        NaN                      10   \n",
       "1                        NaN               13.401251   \n",
       "2                        NaN         13.378899151666   \n",
       "3                        NaN         13.518712790547   \n",
       "4                        NaN              13.3847854   \n",
       "5                        NaN         11.583000803261   \n",
       "6                        NaN         13.291771015536   \n",
       "7                        NaN         9.2463729858642   \n",
       "8                        NaN                 13.3667   \n",
       "9                        NaN                7.404526   \n",
       "10                       NaN                    13.4   \n",
       "11                       NaN         9.0296906801049   \n",
       "12                       NaN         10.461939636541   \n",
       "13                       NaN                 13.4116   \n",
       "14                       NaN                 10.0014   \n",
       "15                       NaN           11.0914401814   \n",
       "16                       NaN                 11.5833   \n",
       "17                       NaN           13.1914410554   \n",
       "18                       NaN           7.51207846423   \n",
       "19                       NaN                  8.6709   \n",
       "20                       NaN              7.09639292   \n",
       "21                       NaN                  6.9667   \n",
       "22                       NaN         11.616418843701   \n",
       "23                       NaN                 13.7333   \n",
       "24                       NaN                  9.1739   \n",
       "25                       NaN                 13.3167   \n",
       "26                       NaN                   9.738   \n",
       "27                       NaN                 12.3771   \n",
       "28                       NaN         9.7728945945643   \n",
       "29                       NaN                 13.0667   \n",
       "..                       ...                     ...   \n",
       "114                      NaN                 8.59931   \n",
       "115                      NaN                13.54491   \n",
       "116                      NaN         10.084261111111   \n",
       "117                      NaN                9.947339   \n",
       "118                      NaN                 6.99056   \n",
       "119                      NaN                  8.6709   \n",
       "120                      NaN                  8.6709   \n",
       "121          328584450515374         11.550224195079   \n",
       "122          328584450515374         11.550224195079   \n",
       "123          328584450515374         11.550224195079   \n",
       "124                      NaN                  6.0828   \n",
       "125                      NaN                  6.0828   \n",
       "126                      NaN                  7.4833   \n",
       "127                      NaN                  7.4833   \n",
       "128                      NaN                11.08681   \n",
       "129                      NaN                11.08681   \n",
       "130                      NaN               9.0128299   \n",
       "131                      NaN         8.2567278111553   \n",
       "132                      NaN         8.2567278111553   \n",
       "133                      NaN                  7.5978   \n",
       "134                      NaN                  7.5978   \n",
       "135                      NaN                 7.88636   \n",
       "136                      NaN                8.754933   \n",
       "137                      NaN                8.754933   \n",
       "138         1703566273255164              13.4212399   \n",
       "139         1703566273255164              13.4212399   \n",
       "140                      NaN                   9.738   \n",
       "141                      NaN                   9.738   \n",
       "142                      NaN                 7.18333   \n",
       "143                      NaN                 7.18333   \n",
       "\n",
       "             data_location_street data_location_zip  \\\n",
       "0                             NaN               NaN   \n",
       "1                             NaN               NaN   \n",
       "2                             NaN               NaN   \n",
       "3                             NaN               NaN   \n",
       "4                             NaN               NaN   \n",
       "5                             NaN               NaN   \n",
       "6                         andel's               NaN   \n",
       "7                             NaN               NaN   \n",
       "8                             NaN               NaN   \n",
       "9                             NaN               NaN   \n",
       "10                            NaN               NaN   \n",
       "11                            NaN               NaN   \n",
       "12                            NaN               NaN   \n",
       "13                            NaN               NaN   \n",
       "14                            NaN               NaN   \n",
       "15                            NaN               NaN   \n",
       "16                            NaN               NaN   \n",
       "17                            NaN               NaN   \n",
       "18                            NaN               NaN   \n",
       "19                            NaN               NaN   \n",
       "20                            NaN               NaN   \n",
       "21                            NaN               NaN   \n",
       "22                            NaN               NaN   \n",
       "23                            NaN               NaN   \n",
       "24                            NaN               NaN   \n",
       "25                            NaN               NaN   \n",
       "26                            NaN               NaN   \n",
       "27                            NaN               NaN   \n",
       "28                            NaN               NaN   \n",
       "29                            NaN               NaN   \n",
       "..                            ...               ...   \n",
       "114                 Stroofstr. 10             65933   \n",
       "115                  Am Studio 16             12489   \n",
       "116                           NaN             22885   \n",
       "117  Virchowstrasse 2 / Hinterhof             22767   \n",
       "118          Am Hauptbahnhof 6-12             66111   \n",
       "119                           NaN               NaN   \n",
       "120                           NaN               NaN   \n",
       "121        Spiridon-Louis-Ring 21             80809   \n",
       "122        Spiridon-Louis-Ring 21             80809   \n",
       "123        Spiridon-Louis-Ring 21             80809   \n",
       "124                           NaN               NaN   \n",
       "125                           NaN               NaN   \n",
       "126                           NaN               NaN   \n",
       "127                           NaN               NaN   \n",
       "128                           NaN               NaN   \n",
       "129                           NaN               NaN   \n",
       "130          niederkleiner Str 51             35260   \n",
       "131                           NaN               NaN   \n",
       "132                           NaN               NaN   \n",
       "133                           NaN               NaN   \n",
       "134                           NaN               NaN   \n",
       "135                    weststr.92             59227   \n",
       "136                           NaN               NaN   \n",
       "137                           NaN               NaN   \n",
       "138        Regensburger Straße 34               NaN   \n",
       "139        Regensburger Straße 34               NaN   \n",
       "140                           NaN               NaN   \n",
       "141                           NaN               NaN   \n",
       "142                           NaN               NaN   \n",
       "143                           NaN               NaN   \n",
       "\n",
       "                             data_name  data_rating_count  \n",
       "0                             Alemania                NaN  \n",
       "1                               Berlín                NaN  \n",
       "2    Brandenburg Gate, Berlin, Germany                8.0  \n",
       "3                 Brandenburg, Germany                NaN  \n",
       "4             Berlin, Germany - Europe               11.0  \n",
       "5                      Bayern, Germany                NaN  \n",
       "6       Tegel Airport, Berlin, Germany               26.0  \n",
       "7           Baden-Württemberg, Germany                NaN  \n",
       "8          Tiergarten, Berlin, Germany                NaN  \n",
       "9         Nordrhein-Westfalen, Germany                NaN  \n",
       "10    Prenzlauer Berg, Berlin, Germany                NaN  \n",
       "11                     Hessen, Germany                NaN  \n",
       "12              Niedersachsen, Germany                NaN  \n",
       "13          Kreuzberg, Berlin, Germany                NaN  \n",
       "14                            Hamburgo                NaN  \n",
       "15                  Thüringen, Germany                NaN  \n",
       "16                              Múnich                NaN  \n",
       "17                    Sachsen, Germany                NaN  \n",
       "18            Rheinland-Pfalz, Germany                NaN  \n",
       "19                  Fráncfort del Meno                NaN  \n",
       "20                   Saarland, Germany                NaN  \n",
       "21                  Colonia (Alemania)                NaN  \n",
       "22        Bogenhausen, Bayern, Germany                NaN  \n",
       "23                              Dresde                NaN  \n",
       "24                           Stuttgart                NaN  \n",
       "25        Wilmersdorf, Berlin, Germany                NaN  \n",
       "26                            Hannover                NaN  \n",
       "27                             Leipzig                NaN  \n",
       "28         Schleswig-Holstein, Germany                NaN  \n",
       "29                             Potsdam                NaN  \n",
       "..                                 ...                ...  \n",
       "114                 LIN BEESER FASHION                NaN  \n",
       "115                         RT Deutsch             2769.0  \n",
       "116                           Wandsbek                NaN  \n",
       "117                       Allike Store              262.0  \n",
       "118           Saarbrücken Hauptbahnhof                NaN  \n",
       "119                 Fráncfort del Meno                NaN  \n",
       "120                 Fráncfort del Meno                NaN  \n",
       "121               Olympiahalle München             3972.0  \n",
       "122               Olympiahalle München             3972.0  \n",
       "123               Olympiahalle München             3972.0  \n",
       "124                          Aquisgrán                NaN  \n",
       "125                          Aquisgrán                NaN  \n",
       "126                              Hagen                NaN  \n",
       "127                              Hagen                NaN  \n",
       "128             Garmisch-Partenkirchen                NaN  \n",
       "129             Garmisch-Partenkirchen                NaN  \n",
       "130                 Soferi din Romania              733.0  \n",
       "131  Berau, Baden-Wurttemberg, Germany                NaN  \n",
       "132  Berau, Baden-Wurttemberg, Germany                NaN  \n",
       "133                           Coblenza                NaN  \n",
       "134                           Coblenza                NaN  \n",
       "135                          نبضات سهر                NaN  \n",
       "136                          Paderborn                NaN  \n",
       "137                          Paderborn                NaN  \n",
       "138            Tattoo Circle Worldwide              985.0  \n",
       "139            Tattoo Circle Worldwide              985.0  \n",
       "140                           Hannover                NaN  \n",
       "141                           Hannover                NaN  \n",
       "142                          Wuppertal                NaN  \n",
       "143                          Wuppertal                NaN  \n",
       "\n",
       "[552 rows x 14 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "r1= result.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(297, 14)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 128 cities, 2 countries\n"
     ]
    }
   ],
   "source": [
    "print('There are {} cities, {} countries' .format(r1['data_location_city'].unique().shape[0],r1['data_location_country'].unique().shape[0]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Berlin', 'Tiergarten', 'Prenzlauer Berg', 'Kreuzberg',\n",
       "       'Hamburg', 'Munich', 'Frankfurt', 'Cologne', 'Bogenhausen',\n",
       "       'Dresden', 'Stuttgart', 'Wilmersdorf', 'Hanover', 'Leipzig',\n",
       "       'Potsdam', 'Steglitz', 'Bremen', 'Neukölln', 'Roßlau',\n",
       "       'Schöneberg', 'Bamberg', 'Lichtenrade', 'Mannheim',\n",
       "       'Märkisches Viertel', 'Oldenburg', 'Hagen', 'Bremerhaven', 'Belin',\n",
       "       'Siegen', 'Heidelberg', 'Bonn', 'Bielefeld', 'Magdeburg', 'Trier',\n",
       "       'Hildesheim', 'Cottbus-Schmellwitz', 'Kassel', 'Augsburg',\n",
       "       'Erfurt', 'Aachen', 'Regensburg', 'Düsseldorf', 'Koblenz', 'Mainz',\n",
       "       'Karlsruhe', 'Eisleben', 'Wuppertal', 'Ingolstadt', 'Ulm',\n",
       "       'Pankow', 'Paderborn', 'Tessin', 'Plötzensee', 'Krefeld', 'Barlin',\n",
       "       'Chemnitz', 'Bochum', 'Rostock', 'Wolfsburg', 'Karow', 'Strasburg',\n",
       "       'Gera', 'Dessau', 'Fichtenwalde', 'Luckau', 'Bottrop', 'Wolfen',\n",
       "       'Greifswald', 'Weißensee', 'Oberhausen', 'Gelsenkirchen',\n",
       "       'Marzahn', 'Ahlen', 'Nuremberg', 'Dortmund', 'Rockenhausen',\n",
       "       'Celle', 'Konstanz', 'Osnabrück', 'Schwangau', 'Sindelfingen',\n",
       "       'Essen', 'Hamburg-Nord', 'Wenden', 'Freiburg im Breisgau',\n",
       "       'Hockenheim', 'Poppenbüttel', 'Rothenburg ob der Tauber', 'Solms',\n",
       "       'Nürburg', 'Garching', 'Bernkastel-Kues', 'Weimar',\n",
       "       'Herzogenaurach', 'Langenhagen', 'Breitenbrunn', 'Werlte',\n",
       "       'Amerika', 'Bornheim', 'Heilbronn', 'Esslingen', 'Halle',\n",
       "       'Kaiserslautern', 'Hamm', 'Hamminkeln', 'Herne', 'Adelsdorf',\n",
       "       'Stadt Haan', 'Dresden-Altstadt', 'Wiesbaden', 'Verden',\n",
       "       'Karlsfeld', 'Hofheim', 'Sankt Wendel', 'Nordstemmen', 'Kiel',\n",
       "       'Neu-Ulm', 'Kempten', 'Passau', 'Duisburg', 'Tübingen',\n",
       "       'Adlershof', 'Saarbrücken', 'Garmisch-Partenkirchen',\n",
       "       'Stadtallendorf', 'Berau', 'Eging'], dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1['data_location_city'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Germany'], dtype=object)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1['data_location_country'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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