hack-juction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "url = \"https://api.hypr.cl/raw/\"\n",
    "\n",
    "headers = {\n",
    " 'x-api-key': \"**************************************\",\n",
    " 'time_start': \"2019-08-01T12:00:00Z\", 'time_stop': \"2019-08-01T12:00:10Z\", 'Accept': \"*/*\",\n",
    " 'Cache-Control': \"no-cache\", 'Host': \"api.hypr.cl\",\n",
    " 'Accept-Encoding': \"gzip, deflate\",\n",
    " 'Content-Length': \"0\", 'Connection': \"keep-alive\",\n",
    " 'cache-control': \"no-cache\" }\n",
    "\n",
    "response = requests.request(\"POST\", url, headers=headers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url1 = \"https://api.hypr.cl/station\"\n",
    "\n",
    "headers1 = {\n",
    " 'x-api-key': \"**************************************\",\n",
    " 'command': \"list\",\n",
    " 'Accept': \"*/*\", 'Cache-Control': \"no-cache\", 'Host': \"api.hypr.cl\", 'Accept-Encoding': \"gzip, deflate\", 'Content-Length': \"0\",\n",
    " 'Connection': \"keep-alive\",\n",
    " 'cache-control': \"no-cache\" }\n",
    "\n",
    "response1 = requests.request('POST',url1,headers=headers1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.status_code\n",
    "#content = response1.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "content = response.json()\n",
    "content1 = response1.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['status', 'raw'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "content.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(content['raw'])\n",
    "df1 = pd.DataFrame(content1['list'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>hash</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>serial</th>\n",
       "      <th>distance</th>\n",
       "      <th>rssi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
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       "      <td>60.447947</td>\n",
       "      <td>22.258656</td>\n",
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       "      <td>11.70</td>\n",
       "      <td>-85</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
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       "      <td>-85</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...</td>\n",
       "      <td>60.165440</td>\n",
       "      <td>24.953980</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>17.40</td>\n",
       "      <td>-90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...</td>\n",
       "      <td>60.165440</td>\n",
       "      <td>24.953980</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...</td>\n",
       "      <td>60.165440</td>\n",
       "      <td>24.953980</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>18167fae09beb238eb0a355497a7cd8695b98c6f1838c1...</td>\n",
       "      <td>60.153800</td>\n",
       "      <td>24.955950</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>21.87</td>\n",
       "      <td>-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>225b201dfd9c44747634aa0dd031c09fded36df2879982...</td>\n",
       "      <td>60.168290</td>\n",
       "      <td>24.942140</td>\n",
       "      <td>00000000afef4555</td>\n",
       "      <td>16.10</td>\n",
       "      <td>-89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>2681becf1795042fa372c89e61856da5519a49a90e230f...</td>\n",
       "      <td>60.164480</td>\n",
       "      <td>24.973230</td>\n",
       "      <td>0000000053c6c2be</td>\n",
       "      <td>25.36</td>\n",
       "      <td>-95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>2a93b61a3863ace54349d7a0937e4134fcc6d4dfcf064e...</td>\n",
       "      <td>60.154320</td>\n",
       "      <td>24.921100</td>\n",
       "      <td>0000000019fb59c4</td>\n",
       "      <td>16.10</td>\n",
       "      <td>-89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>2cbdb709bb456bc8bca60a86bfa0fd9e97905158240100...</td>\n",
       "      <td>60.168970</td>\n",
       "      <td>24.952220</td>\n",
       "      <td>00000000aa852af1</td>\n",
       "      <td>21.87</td>\n",
       "      <td>-93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>2efd972897e318100638d8474e49f7d4cbfb4d97c6ee23...</td>\n",
       "      <td>60.168970</td>\n",
       "      <td>24.952220</td>\n",
       "      <td>00000000aa852af1</td>\n",
       "      <td>18.79</td>\n",
       "      <td>-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>35c6573a3fdd07d91dacb331df71f349703fbecb508756...</td>\n",
       "      <td>60.167620</td>\n",
       "      <td>24.954150</td>\n",
       "      <td>000000006b44cce7</td>\n",
       "      <td>4.50</td>\n",
       "      <td>-74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>42631cd011d41b5c2fecbbb39cc03d1f8e0bef8c28e3a5...</td>\n",
       "      <td>60.167620</td>\n",
       "      <td>24.954150</td>\n",
       "      <td>000000006b44cce7</td>\n",
       "      <td>20.28</td>\n",
       "      <td>-92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>496922153ce26b520018c7ba74cec1350805116d24dea4...</td>\n",
       "      <td>60.447947</td>\n",
       "      <td>22.258656</td>\n",
       "      <td>00000000378bd7ea</td>\n",
       "      <td>17.40</td>\n",
       "      <td>-90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>4c90d667a8bf7ea1281fcb14f35adcd1497fb1ba95366b...</td>\n",
       "      <td>60.166290</td>\n",
       "      <td>24.959570</td>\n",
       "      <td>00000000fb7600be</td>\n",
       "      <td>23.56</td>\n",
       "      <td>-94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>509dbd515126267ff7d1b2ead2334d39e65c40e52a51d1...</td>\n",
       "      <td>60.153800</td>\n",
       "      <td>24.955950</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>29.32</td>\n",
       "      <td>-97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>50f91b4f4cb92911d319b35f4de2279b7aa649ddefff13...</td>\n",
       "      <td>60.447947</td>\n",
       "      <td>22.258656</td>\n",
       "      <td>00000000378bd7ea</td>\n",
       "      <td>18.79</td>\n",
       "      <td>-91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>53c82544a723cbe2c1f3d422588a5ab2a1c86bd3e2b385...</td>\n",
       "      <td>60.447947</td>\n",
       "      <td>22.258656</td>\n",
       "      <td>00000000378bd7ea</td>\n",
       "      <td>10.78</td>\n",
       "      <td>-84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
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       "      <td>60.153800</td>\n",
       "      <td>24.955950</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>550534e45b2c130b5fe0c57978a29a8efc3d42d657bdff...</td>\n",
       "      <td>60.173000</td>\n",
       "      <td>24.925240</td>\n",
       "      <td>0000000096918cfa</td>\n",
       "      <td>17.40</td>\n",
       "      <td>-90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                    time                                               hash  \\\n",
       "0   2019-08-01T12:00:00Z  0a494322afeaeaa8fb810fbeeb7daa14dd0fc9af5ebe2b...   \n",
       "1   2019-08-01T12:00:00Z  0debdde77b998414b2d39bfb515928337ae9e84c41faef...   \n",
       "2   2019-08-01T12:00:00Z  144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...   \n",
       "3   2019-08-01T12:00:00Z  14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...   \n",
       "4   2019-08-01T12:00:00Z  165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...   \n",
       "5   2019-08-01T12:00:00Z  18167fae09beb238eb0a355497a7cd8695b98c6f1838c1...   \n",
       "6   2019-08-01T12:00:00Z  225b201dfd9c44747634aa0dd031c09fded36df2879982...   \n",
       "7   2019-08-01T12:00:00Z  2681becf1795042fa372c89e61856da5519a49a90e230f...   \n",
       "8   2019-08-01T12:00:00Z  2a93b61a3863ace54349d7a0937e4134fcc6d4dfcf064e...   \n",
       "9   2019-08-01T12:00:00Z  2cbdb709bb456bc8bca60a86bfa0fd9e97905158240100...   \n",
       "10  2019-08-01T12:00:00Z  2efd972897e318100638d8474e49f7d4cbfb4d97c6ee23...   \n",
       "11  2019-08-01T12:00:00Z  35c6573a3fdd07d91dacb331df71f349703fbecb508756...   \n",
       "12  2019-08-01T12:00:00Z  42631cd011d41b5c2fecbbb39cc03d1f8e0bef8c28e3a5...   \n",
       "13  2019-08-01T12:00:00Z  496922153ce26b520018c7ba74cec1350805116d24dea4...   \n",
       "14  2019-08-01T12:00:00Z  4c90d667a8bf7ea1281fcb14f35adcd1497fb1ba95366b...   \n",
       "15  2019-08-01T12:00:00Z  509dbd515126267ff7d1b2ead2334d39e65c40e52a51d1...   \n",
       "16  2019-08-01T12:00:00Z  50f91b4f4cb92911d319b35f4de2279b7aa649ddefff13...   \n",
       "17  2019-08-01T12:00:00Z  53c82544a723cbe2c1f3d422588a5ab2a1c86bd3e2b385...   \n",
       "18  2019-08-01T12:00:00Z  53dbe255fea37f5090b8b35afdd743747b903c68c20ae4...   \n",
       "19  2019-08-01T12:00:00Z  550534e45b2c130b5fe0c57978a29a8efc3d42d657bdff...   \n",
       "\n",
       "     latitude  longitude            serial  distance  rssi  \n",
       "0   60.447947  22.258656  00000000378bd7ea     11.70   -85  \n",
       "1   60.447947  22.258656  00000000378bd7ea     11.70   -85  \n",
       "2   60.165440  24.953980  0000000038d83d41     17.40   -90  \n",
       "3   60.165440  24.953980  0000000038d83d41     27.28   -96  \n",
       "4   60.165440  24.953980  0000000038d83d41     27.28   -96  \n",
       "5   60.153800  24.955950  0000000038bf9618     21.87   -93  \n",
       "6   60.168290  24.942140  00000000afef4555     16.10   -89  \n",
       "7   60.164480  24.973230  0000000053c6c2be     25.36   -95  \n",
       "8   60.154320  24.921100  0000000019fb59c4     16.10   -89  \n",
       "9   60.168970  24.952220  00000000aa852af1     21.87   -93  \n",
       "10  60.168970  24.952220  00000000aa852af1     18.79   -91  \n",
       "11  60.167620  24.954150  000000006b44cce7      4.50   -74  \n",
       "12  60.167620  24.954150  000000006b44cce7     20.28   -92  \n",
       "13  60.447947  22.258656  00000000378bd7ea     17.40   -90  \n",
       "14  60.166290  24.959570  00000000fb7600be     23.56   -94  \n",
       "15  60.153800  24.955950  0000000038bf9618     29.32   -97  \n",
       "16  60.447947  22.258656  00000000378bd7ea     18.79   -91  \n",
       "17  60.447947  22.258656  00000000378bd7ea     10.78   -84  \n",
       "18  60.153800  24.955950  0000000038bf9618     27.28   -96  \n",
       "19  60.173000  24.925240  0000000096918cfa     17.40   -90  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serial</th>\n",
       "      <th>address</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>group</th>\n",
       "      <th>alias</th>\n",
       "      <th>size</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0000000006dd41f6</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHC</td>\n",
       "      <td>60.14951</td>\n",
       "      <td>24.92138</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>000000000f4919c9</td>\n",
       "      <td>Mannerheimintie 22-24</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Amos Rex</td>\n",
       "      <td>60.17065</td>\n",
       "      <td>24.93641</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0000000019fb59c4</td>\n",
       "      <td>Tyynenmerenkatu 8</td>\n",
       "      <td>00220</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Lansiterminaali 1</td>\n",
       "      <td>60.15432</td>\n",
       "      <td>24.92110</td>\n",
       "      <td>lansiterminaali</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>00000000342570c2</td>\n",
       "      <td>Sibeliuksen puisto</td>\n",
       "      <td>00250</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Sibelius Monumentti</td>\n",
       "      <td>60.18203</td>\n",
       "      <td>24.91341</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>Mattolaituri</td>\n",
       "      <td>00140</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kaivopuiston ranta</td>\n",
       "      <td>60.15380</td>\n",
       "      <td>24.95595</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>000000004ef3150d</td>\n",
       "      <td>Tyynenmerenkatu 14</td>\n",
       "      <td>00180</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Lansiterminaali 2</td>\n",
       "      <td>60.14947</td>\n",
       "      <td>24.91438</td>\n",
       "      <td>lansiterminaali</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0000000053c6c2be</td>\n",
       "      <td>Katajanokanlaituri, valopylvas</td>\n",
       "      <td>00160</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Katajanokan laituri 2</td>\n",
       "      <td>60.16448</td>\n",
       "      <td>24.97323</td>\n",
       "      <td>katajanokka</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>00000000675d5200</td>\n",
       "      <td>paasisaankaynti</td>\n",
       "      <td>00530</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hakaniemen kauppahalli 1</td>\n",
       "      <td>60.17918</td>\n",
       "      <td>24.95141</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>000000006b087f40</td>\n",
       "      <td>Kaivokatu 2</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Ateneum</td>\n",
       "      <td>60.17002</td>\n",
       "      <td>24.94407</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>000000006b44cce7</td>\n",
       "      <td>Etelaranta</td>\n",
       "      <td>00170</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kauppatori</td>\n",
       "      <td>60.16762</td>\n",
       "      <td>24.95415</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0000000074f765a7</td>\n",
       "      <td>Toolonlahdenranta 1</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Oodi</td>\n",
       "      <td>60.17368</td>\n",
       "      <td>24.93810</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>000000007b5207b6</td>\n",
       "      <td>Torikadun sisaankaynti</td>\n",
       "      <td>00530</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hakaniemen kauppahalli 2</td>\n",
       "      <td>60.17919</td>\n",
       "      <td>24.95181</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0000000096918cfa</td>\n",
       "      <td>Lutherinkatu 3</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Temppeliaukion kirkko</td>\n",
       "      <td>60.17300</td>\n",
       "      <td>24.92524</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>00000000a53ed894</td>\n",
       "      <td>Hernesaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHD</td>\n",
       "      <td>60.14699</td>\n",
       "      <td>24.92200</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>00000000aa852af1</td>\n",
       "      <td>Alexanterinkatu 24</td>\n",
       "      <td>00170</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Senaatintori lantinen</td>\n",
       "      <td>60.16897</td>\n",
       "      <td>24.95222</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>00000000afef4555</td>\n",
       "      <td>Pohjoisespa 41</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Stockmann</td>\n",
       "      <td>60.16829</td>\n",
       "      <td>24.94214</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>00000000b33a8357</td>\n",
       "      <td>Narikkatorin sisaankaynti</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kamppi</td>\n",
       "      <td>60.16900</td>\n",
       "      <td>24.93288</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>00000000cd16b8ef</td>\n",
       "      <td>Alexanterinkatu 20</td>\n",
       "      <td>00170</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Senaatintori itainen</td>\n",
       "      <td>60.16899</td>\n",
       "      <td>24.95337</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>00000000d747f075</td>\n",
       "      <td>Katajanokanlaituri 8</td>\n",
       "      <td>00160</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Katajanokan terminaali</td>\n",
       "      <td>60.16380</td>\n",
       "      <td>24.96846</td>\n",
       "      <td>katajanokka</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>00000000e8a064a4</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHB</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>00000000f1124bca</td>\n",
       "      <td>Olympiaranta 1</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Olympiaterminaali</td>\n",
       "      <td>60.16075</td>\n",
       "      <td>24.95883</td>\n",
       "      <td>olympia</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>00000000fb7600be</td>\n",
       "      <td>Katajanokka</td>\n",
       "      <td>00160</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Katajanokan laituri</td>\n",
       "      <td>60.16629</td>\n",
       "      <td>24.95957</td>\n",
       "      <td>katajanokka</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>00000000fdda10fe</td>\n",
       "      <td>Erottajankatu 11</td>\n",
       "      <td>00130</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Dianapuisto</td>\n",
       "      <td>60.16531</td>\n",
       "      <td>24.94420</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>00000000fffb8cf0</td>\n",
       "      <td>Paalaituri</td>\n",
       "      <td>00190</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Suomenlinna</td>\n",
       "      <td>60.14900</td>\n",
       "      <td>24.98430</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              serial                         address postalcode      city  \\\n",
       "0   0000000006dd41f6             Munkkisaarenlaituri      00150  Helsinki   \n",
       "1   000000000f4919c9           Mannerheimintie 22-24      00100  Helsinki   \n",
       "2   0000000019fb59c4               Tyynenmerenkatu 8      00220  Helsinki   \n",
       "3   00000000342570c2              Sibeliuksen puisto      00250  Helsinki   \n",
       "4   0000000038bf9618                    Mattolaituri      00140  Helsinki   \n",
       "5   0000000038d83d41             Pakkahuoneenlaituri      00100  Helsinki   \n",
       "6   000000004ef3150d              Tyynenmerenkatu 14      00180  Helsinki   \n",
       "7   0000000053c6c2be  Katajanokanlaituri, valopylvas      00160  Helsinki   \n",
       "8   00000000675d5200                 paasisaankaynti      00530  Helsinki   \n",
       "9   000000006b087f40                     Kaivokatu 2      00100  Helsinki   \n",
       "10  000000006b44cce7                      Etelaranta      00170  Helsinki   \n",
       "11  0000000074f765a7             Toolonlahdenranta 1      00100  Helsinki   \n",
       "12  000000007b5207b6          Torikadun sisaankaynti      00530  Helsinki   \n",
       "13  0000000096918cfa                  Lutherinkatu 3      00100  Helsinki   \n",
       "14  00000000a53ed894              Hernesaarenlaituri      00150  Helsinki   \n",
       "15  00000000aa852af1              Alexanterinkatu 24      00170  Helsinki   \n",
       "16  00000000afef4555                  Pohjoisespa 41      00100  Helsinki   \n",
       "17  00000000b33a8357       Narikkatorin sisaankaynti      00100  Helsinki   \n",
       "18  00000000cd16b8ef              Alexanterinkatu 20      00170  Helsinki   \n",
       "19  00000000d747f075            Katajanokanlaituri 8      00160  Helsinki   \n",
       "20  00000000e8a064a4             Munkkisaarenlaituri      00150  Helsinki   \n",
       "21  00000000f1124bca                  Olympiaranta 1      00100  Helsinki   \n",
       "22  00000000fb7600be                     Katajanokka      00160  Helsinki   \n",
       "23  00000000fdda10fe                Erottajankatu 11      00130  Helsinki   \n",
       "24  00000000fffb8cf0                      Paalaituri      00190  Helsinki   \n",
       "\n",
       "    country               description  latitude  longitude            group  \\\n",
       "0   Finland            Hernesaari LHC  60.14951   24.92138       hernesaari   \n",
       "1   Finland                  Amos Rex  60.17065   24.93641          default   \n",
       "2   Finland         Lansiterminaali 1  60.15432   24.92110  lansiterminaali   \n",
       "3   Finland       Sibelius Monumentti  60.18203   24.91341          default   \n",
       "4   Finland        Kaivopuiston ranta  60.15380   24.95595          default   \n",
       "5   Finland       Pakkahuoneenlaituri  60.16544   24.95398          default   \n",
       "6   Finland         Lansiterminaali 2  60.14947   24.91438  lansiterminaali   \n",
       "7   Finland     Katajanokan laituri 2  60.16448   24.97323      katajanokka   \n",
       "8   Finland  Hakaniemen kauppahalli 1  60.17918   24.95141          default   \n",
       "9   Finland                   Ateneum  60.17002   24.94407          default   \n",
       "10  Finland                Kauppatori  60.16762   24.95415          default   \n",
       "11  Finland                      Oodi  60.17368   24.93810          default   \n",
       "12  Finland  Hakaniemen kauppahalli 2  60.17919   24.95181          default   \n",
       "13  Finland     Temppeliaukion kirkko  60.17300   24.92524          default   \n",
       "14  Finland            Hernesaari LHD  60.14699   24.92200       hernesaari   \n",
       "15  Finland     Senaatintori lantinen  60.16897   24.95222          default   \n",
       "16  Finland                 Stockmann  60.16829   24.94214          default   \n",
       "17  Finland                    Kamppi  60.16900   24.93288          default   \n",
       "18  Finland      Senaatintori itainen  60.16899   24.95337          default   \n",
       "19  Finland    Katajanokan terminaali  60.16380   24.96846      katajanokka   \n",
       "20  Finland            Hernesaari LHB  60.15133   24.92329       hernesaari   \n",
       "21  Finland         Olympiaterminaali  60.16075   24.95883          olympia   \n",
       "22  Finland       Katajanokan laituri  60.16629   24.95957      katajanokka   \n",
       "23  Finland               Dianapuisto  60.16531   24.94420          default   \n",
       "24  Finland               Suomenlinna  60.14900   24.98430          default   \n",
       "\n",
       "   alias  size  \n",
       "0   None    30  \n",
       "1   None    30  \n",
       "2   None    30  \n",
       "3   None    30  \n",
       "4   None    30  \n",
       "5   None    30  \n",
       "6   None    30  \n",
       "7   None    30  \n",
       "8   None    30  \n",
       "9   None    30  \n",
       "10  None    30  \n",
       "11  None    30  \n",
       "12  None    30  \n",
       "13  None    30  \n",
       "14  None    30  \n",
       "15  None    30  \n",
       "16  None    30  \n",
       "17  None    30  \n",
       "18  None    30  \n",
       "19  None    30  \n",
       "20  None    30  \n",
       "21  None    30  \n",
       "22  None    30  \n",
       "23  None    30  \n",
       "24  None    30  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['size'] = 30\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.express as px\n",
    "\n",
    "# px.set_mapbox_access_token('pk.eyJ1IjoiYmxlc3NlZDY2IiwiYSI6ImNrMWhudTkzbDBoYzMzaHBjZm90OGZkcWMifQ.opsS4eksuFXKYgh4x_T4Sg')\n",
    "# px.scatter_mapbox(df1,\n",
    "#                   size = 'size',\n",
    "#                  lat='latitude',\n",
    "#                  lon='longitude')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the datapoints and sum the number of people per minute\n",
    "\n",
    "df2 = df.merge(df1,on='serial',how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3875, 17)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4132, 7)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "691"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df2['hash'].nunique()  #use nunique to count the number of unique entries of hashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>time</th>\n",
       "      <th>hash</th>\n",
       "      <th>latitude_x</th>\n",
       "      <th>longitude_x</th>\n",
       "      <th>serial</th>\n",
       "      <th>distance</th>\n",
       "      <th>rssi</th>\n",
       "      <th>address</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>latitude_y</th>\n",
       "      <th>longitude_y</th>\n",
       "      <th>group</th>\n",
       "      <th>alias</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>17.40</td>\n",
       "      <td>-90</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>f0658ab84466fd2b70b305787976c9cc96062ec03a8307...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>11.70</td>\n",
       "      <td>-85</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>0e4c038648ee23899b76befdddd03584c991c201f5450a...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>20.28</td>\n",
       "      <td>-92</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3713</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>ecb8064e50753cbf52bf2558814bce87d260f010c9a8a9...</td>\n",
       "      <td>60.14947</td>\n",
       "      <td>24.91438</td>\n",
       "      <td>000000004ef3150d</td>\n",
       "      <td>8.38</td>\n",
       "      <td>-81</td>\n",
       "      <td>Tyynenmerenkatu 14</td>\n",
       "      <td>00180</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Lansiterminaali 2</td>\n",
       "      <td>60.14947</td>\n",
       "      <td>24.91438</td>\n",
       "      <td>lansiterminaali</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3839</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>84af3c810fda51d1462127f4d42037309bb44daef7463c...</td>\n",
       "      <td>60.14699</td>\n",
       "      <td>24.92200</td>\n",
       "      <td>00000000a53ed894</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "      <td>Hernesaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHD</td>\n",
       "      <td>60.14699</td>\n",
       "      <td>24.92200</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3858</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>8c14d4ef0bfc5570b4049535fee86d357fed359801c922...</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>00000000e8a064a4</td>\n",
       "      <td>29.32</td>\n",
       "      <td>-97</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHB</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3859</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>d6ef0b345b6dd3590a47e2159ea4c45d0244ffc023887e...</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>00000000e8a064a4</td>\n",
       "      <td>29.32</td>\n",
       "      <td>-97</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHB</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3860</td>\n",
       "      <td>2019-08-01T12:00:00Z</td>\n",
       "      <td>d80f7d5b70f3e00064cbfa1ca0c5c6d056cae99e47735a...</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>00000000e8a064a4</td>\n",
       "      <td>18.79</td>\n",
       "      <td>-91</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHB</td>\n",
       "      <td>60.15133</td>\n",
       "      <td>24.92329</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      time                                               hash  \\\n",
       "0     2019-08-01T12:00:00Z  144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...   \n",
       "1     2019-08-01T12:00:00Z  14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...   \n",
       "2     2019-08-01T12:00:00Z  165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...   \n",
       "3     2019-08-01T12:00:00Z  f0658ab84466fd2b70b305787976c9cc96062ec03a8307...   \n",
       "4     2019-08-01T12:00:00Z  0e4c038648ee23899b76befdddd03584c991c201f5450a...   \n",
       "...                    ...                                                ...   \n",
       "3713  2019-08-01T12:00:00Z  ecb8064e50753cbf52bf2558814bce87d260f010c9a8a9...   \n",
       "3839  2019-08-01T12:00:00Z  84af3c810fda51d1462127f4d42037309bb44daef7463c...   \n",
       "3858  2019-08-01T12:00:00Z  8c14d4ef0bfc5570b4049535fee86d357fed359801c922...   \n",
       "3859  2019-08-01T12:00:00Z  d6ef0b345b6dd3590a47e2159ea4c45d0244ffc023887e...   \n",
       "3860  2019-08-01T12:00:00Z  d80f7d5b70f3e00064cbfa1ca0c5c6d056cae99e47735a...   \n",
       "\n",
       "      latitude_x  longitude_x            serial  distance  rssi  \\\n",
       "0       60.16544     24.95398  0000000038d83d41     17.40   -90   \n",
       "1       60.16544     24.95398  0000000038d83d41     27.28   -96   \n",
       "2       60.16544     24.95398  0000000038d83d41     27.28   -96   \n",
       "3       60.16544     24.95398  0000000038d83d41     11.70   -85   \n",
       "4       60.16544     24.95398  0000000038d83d41     20.28   -92   \n",
       "...          ...          ...               ...       ...   ...   \n",
       "3713    60.14947     24.91438  000000004ef3150d      8.38   -81   \n",
       "3839    60.14699     24.92200  00000000a53ed894     27.28   -96   \n",
       "3858    60.15133     24.92329  00000000e8a064a4     29.32   -97   \n",
       "3859    60.15133     24.92329  00000000e8a064a4     29.32   -97   \n",
       "3860    60.15133     24.92329  00000000e8a064a4     18.79   -91   \n",
       "\n",
       "                  address postalcode      city  country          description  \\\n",
       "0     Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "1     Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "2     Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "3     Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "4     Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "...                   ...        ...       ...      ...                  ...   \n",
       "3713   Tyynenmerenkatu 14      00180  Helsinki  Finland    Lansiterminaali 2   \n",
       "3839   Hernesaarenlaituri      00150  Helsinki  Finland       Hernesaari LHD   \n",
       "3858  Munkkisaarenlaituri      00150  Helsinki  Finland       Hernesaari LHB   \n",
       "3859  Munkkisaarenlaituri      00150  Helsinki  Finland       Hernesaari LHB   \n",
       "3860  Munkkisaarenlaituri      00150  Helsinki  Finland       Hernesaari LHB   \n",
       "\n",
       "      latitude_y  longitude_y            group alias  size  \n",
       "0       60.16544     24.95398          default  None    30  \n",
       "1       60.16544     24.95398          default  None    30  \n",
       "2       60.16544     24.95398          default  None    30  \n",
       "3       60.16544     24.95398          default  None    30  \n",
       "4       60.16544     24.95398          default  None    30  \n",
       "...          ...          ...              ...   ...   ...  \n",
       "3713    60.14947     24.91438  lansiterminaali  None    30  \n",
       "3839    60.14699     24.92200       hernesaari  None    30  \n",
       "3858    60.15133     24.92329       hernesaari  None    30  \n",
       "3859    60.15133     24.92329       hernesaari  None    30  \n",
       "3860    60.15133     24.92329       hernesaari  None    30  \n",
       "\n",
       "[358 rows x 17 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "df2[df2['time'] == '2019-08-01T12:00:00Z']\n",
    "#df2['time'] =  pd.to_datetime(df2['time'], format='%d%b%Y:%H:%M:%S.%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can add a counts column to count each hash for a serial\n",
    "\n",
    "df2['counts'] = df2.groupby(['serial'])['hash'].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2019-08-01T12:00:00Z', '2019-08-01T12:00:01Z',\n",
       "       '2019-08-01T12:00:02Z', '2019-08-01T12:00:03Z',\n",
       "       '2019-08-01T12:00:04Z', '2019-08-01T12:00:05Z',\n",
       "       '2019-08-01T12:00:06Z', '2019-08-01T12:00:07Z',\n",
       "       '2019-08-01T12:00:08Z', '2019-08-01T12:00:09Z',\n",
       "       '2019-08-01T12:00:10Z'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['time'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop unwanted columns \n",
    "df2 = df2.drop(['latitude_y','longitude_y','group','alias'],axis=1)\n",
    "\n",
    "#lets do some cleaning of the time.\n",
    "#df2['time'] = df2['time'].replace({'Z':' ','T':' '},inplace=False,regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the time column\n",
    "df2['time'] = df2['time'].str.split('T')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create seperate date and times column\n",
    "df2[['date','times']] = pd.DataFrame(df2.time.values.tolist(), index= df2.index)\n",
    "\n",
    "#use strftime to change the columns to recognizable date-time\n",
    "df2['date'] = pd.to_datetime(df2['date'],format='%Y-%m-%d')\n",
    "\n",
    "#lets drop the Z letter\n",
    "df2['times'] = df2['times'].replace('Z','',regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>time</th>\n",
       "      <th>hash</th>\n",
       "      <th>latitude_x</th>\n",
       "      <th>longitude_x</th>\n",
       "      <th>serial</th>\n",
       "      <th>distance</th>\n",
       "      <th>rssi</th>\n",
       "      <th>address</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>size</th>\n",
       "      <th>counts</th>\n",
       "      <th>date</th>\n",
       "      <th>times</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[2019-08-01, 12:00:00Z]</td>\n",
       "      <td>144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>17.40</td>\n",
       "      <td>-90</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[2019-08-01, 12:00:00Z]</td>\n",
       "      <td>14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[2019-08-01, 12:00:00Z]</td>\n",
       "      <td>165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>27.28</td>\n",
       "      <td>-96</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[2019-08-01, 12:00:00Z]</td>\n",
       "      <td>f0658ab84466fd2b70b305787976c9cc96062ec03a8307...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>11.70</td>\n",
       "      <td>-85</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[2019-08-01, 12:00:00Z]</td>\n",
       "      <td>0e4c038648ee23899b76befdddd03584c991c201f5450a...</td>\n",
       "      <td>60.16544</td>\n",
       "      <td>24.95398</td>\n",
       "      <td>0000000038d83d41</td>\n",
       "      <td>20.28</td>\n",
       "      <td>-92</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>00100</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Pakkahuoneenlaituri</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>12:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      time                                               hash  \\\n",
       "0  [2019-08-01, 12:00:00Z]  144afc51fc2443c7031a2912566eff2ce60226a49d6d2b...   \n",
       "1  [2019-08-01, 12:00:00Z]  14c7e1511b70f4dd3359cbece1377aebb4d70531f5489e...   \n",
       "2  [2019-08-01, 12:00:00Z]  165ebbb4090d47319ae370f0d0854fdc1f6b79499f8b2e...   \n",
       "3  [2019-08-01, 12:00:00Z]  f0658ab84466fd2b70b305787976c9cc96062ec03a8307...   \n",
       "4  [2019-08-01, 12:00:00Z]  0e4c038648ee23899b76befdddd03584c991c201f5450a...   \n",
       "\n",
       "   latitude_x  longitude_x            serial  distance  rssi  \\\n",
       "0    60.16544     24.95398  0000000038d83d41     17.40   -90   \n",
       "1    60.16544     24.95398  0000000038d83d41     27.28   -96   \n",
       "2    60.16544     24.95398  0000000038d83d41     27.28   -96   \n",
       "3    60.16544     24.95398  0000000038d83d41     11.70   -85   \n",
       "4    60.16544     24.95398  0000000038d83d41     20.28   -92   \n",
       "\n",
       "               address postalcode      city  country          description  \\\n",
       "0  Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "1  Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "2  Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "3  Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "4  Pakkahuoneenlaituri      00100  Helsinki  Finland  Pakkahuoneenlaituri   \n",
       "\n",
       "   size  counts       date     times  \n",
       "0    30     130 2019-08-01  12:00:00  \n",
       "1    30     130 2019-08-01  12:00:00  \n",
       "2    30     130 2019-08-01  12:00:00  \n",
       "3    30     130 2019-08-01  12:00:00  \n",
       "4    30     130 2019-08-01  12:00:00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.express as px\n",
    "\n",
    "# px.set_mapbox_access_token('pk.eyJ1IjoiYmxlc3NlZDY2IiwiYSI6ImNrMWhudTkzbDBoYzMzaHBjZm90OGZkcWMifQ.opsS4eksuFXKYgh4x_T4Sg')\n",
    "# px.scatter_mapbox(df2,\n",
    "#                   size = 'counts',\n",
    "#                  lat='latitude_x',\n",
    "#                  lon='longitude_x',\n",
    "#                  animation_frame='hash')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "url2 = \"http://opendata.fmi.fi/wfs\"\n",
    "url2 = 'https://p.hsl.fi/api/v1/facilities.json'\n",
    "\n",
    "response2 = requests.request('GET',url=url2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response2.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/admin/teaching-monolith/python/top-down'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>piste</th>\n",
       "      <th>nimi</th>\n",
       "      <th>x_gk25</th>\n",
       "      <th>y_gk25</th>\n",
       "      <th>aika</th>\n",
       "      <th>ha</th>\n",
       "      <th>pa</th>\n",
       "      <th>ka</th>\n",
       "      <th>ra</th>\n",
       "      <th>la</th>\n",
       "      <th>mp</th>\n",
       "      <th>rv</th>\n",
       "      <th>year</th>\n",
       "      <th>direction</th>\n",
       "      <th>car_pool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>A01</td>\n",
       "      <td>LAUTTASAAREN SILTA</td>\n",
       "      <td>25494426</td>\n",
       "      <td>6672169</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>A01</td>\n",
       "      <td>LAUTTASAAREN SILTA</td>\n",
       "      <td>25494426</td>\n",
       "      <td>6672169</td>\n",
       "      <td>100</td>\n",
       "      <td>65</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>A01</td>\n",
       "      <td>LAUTTASAAREN SILTA</td>\n",
       "      <td>25494426</td>\n",
       "      <td>6672169</td>\n",
       "      <td>200</td>\n",
       "      <td>61</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>A01</td>\n",
       "      <td>LAUTTASAAREN SILTA</td>\n",
       "      <td>25494426</td>\n",
       "      <td>6672169</td>\n",
       "      <td>300</td>\n",
       "      <td>52</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>A01</td>\n",
       "      <td>LAUTTASAAREN SILTA</td>\n",
       "      <td>25494426</td>\n",
       "      <td>6672169</td>\n",
       "      <td>400</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2011</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  piste                nimi    x_gk25   y_gk25  aika  ha  pa  ka  ra  la  mp  \\\n",
       "0   A01  LAUTTASAAREN SILTA  25494426  6672169     0  76   5   1   0   5   0   \n",
       "1   A01  LAUTTASAAREN SILTA  25494426  6672169   100  65   5   1   0   4   0   \n",
       "2   A01  LAUTTASAAREN SILTA  25494426  6672169   200  61   4   1   0   4   0   \n",
       "3   A01  LAUTTASAAREN SILTA  25494426  6672169   300  52   4   1   0   3   0   \n",
       "4   A01  LAUTTASAAREN SILTA  25494426  6672169   400  31   2   0   0   2   0   \n",
       "\n",
       "   rv  year  direction  car_pool  \n",
       "0   0  2011        1.0        87  \n",
       "1   0  2011        1.0        75  \n",
       "2   0  2011        1.0        70  \n",
       "3   0  2011        1.0        60  \n",
       "4   0  2011        1.0        35  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_volumes = pd.read_csv('hki_liikennemaarat.csv',encoding='latin-1', sep=';')\n",
    "\n",
    "#translate\n",
    "traffic_volumes['year'] = traffic_volumes['vuosi']\n",
    "traffic_volumes['direction'] = traffic_volumes['suunta']\n",
    "traffic_volumes['car_pool'] = traffic_volumes['autot']\n",
    "\n",
    "traffic_volumes = traffic_volumes.drop(['vuosi','suunta','autot'],axis=1)\n",
    "traffic_volumes.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_volumes['year'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3875,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['hash'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather = pd.read_csv('Helsinki weather data_english.csv',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Time</th>\n",
       "      <th>Time Zone</th>\n",
       "      <th>Amount of clouds  (1/8)</th>\n",
       "      <th>Air pressure (msl) (hPa)</th>\n",
       "      <th>Relative humidity (%)</th>\n",
       "      <th>Rain intensity (mm/h)</th>\n",
       "      <th>Depth of snow (cm)</th>\n",
       "      <th>Air temperature (degC)</th>\n",
       "      <th>Dev point temperature (degC)</th>\n",
       "      <th>Visibility (m)</th>\n",
       "      <th>Wind direction (deg)</th>\n",
       "      <th>Gust speed (m/s)</th>\n",
       "      <th>Wind speed (m/s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UTC</td>\n",
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       "      <td>996.01.00</td>\n",
       "      <td>82.0</td>\n",
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       "      <td>16.04</td>\n",
       "      <td>13.03</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>5.03</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>UTC</td>\n",
       "      <td>3.0</td>\n",
       "      <td>996</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.05</td>\n",
       "      <td>13.05</td>\n",
       "      <td>47280.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>6.01</td>\n",
       "      <td>3.08</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>UTC</td>\n",
       "      <td>3.0</td>\n",
       "      <td>995.08.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.02</td>\n",
       "      <td>13.05</td>\n",
       "      <td>49920.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>6.04</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.3</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995.06.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.05</td>\n",
       "      <td>39970.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.4</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995.07.00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.04</td>\n",
       "      <td>43530.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>5.08</td>\n",
       "      <td>3.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995.04.00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.02</td>\n",
       "      <td>13.01</td>\n",
       "      <td>43860.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>7.01</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995.01.00</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.04</td>\n",
       "      <td>13.01</td>\n",
       "      <td>47740.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>3.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>UTC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>995.01.00</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.03</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>UTC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>995</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.04</td>\n",
       "      <td>41370.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>UTC</td>\n",
       "      <td>5.0</td>\n",
       "      <td>995</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.02</td>\n",
       "      <td>13.04</td>\n",
       "      <td>39850.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>3.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>994.07.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13.03</td>\n",
       "      <td>44950.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>5.01</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>994.03.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13.03</td>\n",
       "      <td>44170.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>994.03.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.01</td>\n",
       "      <td>13.03</td>\n",
       "      <td>45080.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>5.02</td>\n",
       "      <td>2.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.1</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>994.01.00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.01</td>\n",
       "      <td>13.03</td>\n",
       "      <td>41740.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>4.03</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.2</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.08.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13.03</td>\n",
       "      <td>45660.0</td>\n",
       "      <td>182.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.06.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>15.09</td>\n",
       "      <td>13.04</td>\n",
       "      <td>38500.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.07.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16</td>\n",
       "      <td>13.06</td>\n",
       "      <td>46570.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>4.09</td>\n",
       "      <td>2.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.05.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.03</td>\n",
       "      <td>13.06</td>\n",
       "      <td>34950.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>6.02</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.05.00</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.05</td>\n",
       "      <td>13.06</td>\n",
       "      <td>47000.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>5.07</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>2019</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>UTC</td>\n",
       "      <td>1.0</td>\n",
       "      <td>993.02.00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.04</td>\n",
       "      <td>13.07</td>\n",
       "      <td>39920.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>5.04</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Month  Day  Time Time Zone  Amount of clouds  (1/8)  \\\n",
       "0   2019      7    1   0.0       UTC                      3.0   \n",
       "1   2019      7    1   0.1       UTC                      3.0   \n",
       "2   2019      7    1   0.2       UTC                      3.0   \n",
       "3   2019      7    1   0.3       UTC                      1.0   \n",
       "4   2019      7    1   0.4       UTC                      1.0   \n",
       "5   2019      7    1   0.5       UTC                      1.0   \n",
       "6   2019      7    1   1.0       UTC                      1.0   \n",
       "7   2019      7    1   1.1       UTC                      5.0   \n",
       "8   2019      7    1   1.2       UTC                      5.0   \n",
       "9   2019      7    1   1.3       UTC                      5.0   \n",
       "10  2019      7    1   1.4       UTC                      1.0   \n",
       "11  2019      7    1   1.5       UTC                      1.0   \n",
       "12  2019      7    1   2.0       UTC                      1.0   \n",
       "13  2019      7    1   2.1       UTC                      1.0   \n",
       "14  2019      7    1   2.2       UTC                      1.0   \n",
       "15  2019      7    1   2.3       UTC                      1.0   \n",
       "16  2019      7    1   2.4       UTC                      1.0   \n",
       "17  2019      7    1   2.5       UTC                      1.0   \n",
       "18  2019      7    1   3.0       UTC                      1.0   \n",
       "19  2019      7    1   3.1       UTC                      1.0   \n",
       "\n",
       "   Air pressure (msl) (hPa)  Relative humidity (%) Rain intensity (mm/h)  \\\n",
       "0                 996.01.00                   82.0                     0   \n",
       "1                       996                   83.0                     0   \n",
       "2                 995.08.00                   84.0                     0   \n",
       "3                 995.06.00                   84.0                     0   \n",
       "4                 995.07.00                   83.0                     0   \n",
       "5                 995.04.00                   82.0                     0   \n",
       "6                 995.01.00                   81.0                     0   \n",
       "7                 995.01.00                   82.0                     0   \n",
       "8                       995                   83.0                     0   \n",
       "9                       995                   83.0                     0   \n",
       "10                994.07.00                   84.0                     0   \n",
       "11                994.03.00                   84.0                     0   \n",
       "12                994.03.00                   84.0                     0   \n",
       "13                994.01.00                   83.0                     0   \n",
       "14                993.08.00                   84.0                     0   \n",
       "15                993.06.00                   85.0                     0   \n",
       "16                993.07.00                   85.0                     0   \n",
       "17                993.05.00                   84.0                     0   \n",
       "18                993.05.00                   83.0                     0   \n",
       "19                993.02.00                   84.0                     0   \n",
       "\n",
       "    Depth of snow (cm) Air temperature (degC) Dev point temperature (degC)  \\\n",
       "0                 -1.0                  16.04                        13.03   \n",
       "1                 -1.0                  16.05                        13.05   \n",
       "2                 -1.0                  16.02                        13.05   \n",
       "3                 -1.0                  16.03                        13.05   \n",
       "4                 -1.0                  16.03                        13.04   \n",
       "5                 -1.0                  16.02                        13.01   \n",
       "6                 -1.0                  16.04                        13.01   \n",
       "7                 -1.0                  16.03                        13.03   \n",
       "8                 -1.0                  16.03                        13.04   \n",
       "9                 -1.0                  16.02                        13.04   \n",
       "10                -1.0                     16                        13.03   \n",
       "11                -1.0                     16                        13.03   \n",
       "12                -1.0                  16.01                        13.03   \n",
       "13                -1.0                  16.01                        13.03   \n",
       "14                -1.0                     16                        13.03   \n",
       "15                -1.0                  15.09                        13.04   \n",
       "16                -1.0                     16                        13.06   \n",
       "17                -1.0                  16.03                        13.06   \n",
       "18                -1.0                  16.05                        13.06   \n",
       "19                -1.0                  16.04                        13.07   \n",
       "\n",
       "    Visibility (m)  Wind direction (deg)  Gust speed (m/s)  Wind speed (m/s)  \n",
       "0          50000.0                 190.0              5.03              3.02  \n",
       "1          47280.0                 196.0              6.01              3.08  \n",
       "2          49920.0                 196.0              6.04              3.03  \n",
       "3          39970.0                 192.0              5.04              3.02  \n",
       "4          43530.0                 194.0              5.08              3.07  \n",
       "5          43860.0                 206.0              7.01              4.00  \n",
       "6          47740.0                 195.0              5.07              3.03  \n",
       "7          50000.0                 192.0              6.02              3.02  \n",
       "8          41370.0                 188.0              5.04              3.01  \n",
       "9          39850.0                 197.0              5.07              3.05  \n",
       "10         44950.0                 182.0              5.01              3.02  \n",
       "11         44170.0                 182.0              4.07              2.08  \n",
       "12         45080.0                 180.0              5.02              2.09  \n",
       "13         41740.0                 191.0              4.03              2.06  \n",
       "14         45660.0                 182.0              4.09              3.00  \n",
       "15         38500.0                 172.0              4.00              2.05  \n",
       "16         46570.0                 169.0              4.09              2.06  \n",
       "17         34950.0                 177.0              6.02              3.02  \n",
       "18         47000.0                 181.0              5.07              3.01  \n",
       "19         39920.0                 187.0              5.04              3.02  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FID</th>\n",
       "      <th>ID</th>\n",
       "      <th>Nimi</th>\n",
       "      <th>Namn</th>\n",
       "      <th>Name</th>\n",
       "      <th>Osoite</th>\n",
       "      <th>Adress</th>\n",
       "      <th>Kaupunki</th>\n",
       "      <th>Stad</th>\n",
       "      <th>Operaattor</th>\n",
       "      <th>Kapasiteet</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1001</td>\n",
       "      <td>Rillitori</td>\n",
       "      <td>Rilltorget</td>\n",
       "      <td>Rillitori</td>\n",
       "      <td>Tikkurilantie 30</td>\n",
       "      <td>Dickursbyvägen 30</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.051580</td>\n",
       "      <td>60.293027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1003</td>\n",
       "      <td>Männistönpolku</td>\n",
       "      <td>Männistöstigen</td>\n",
       "      <td>Männistönpolku</td>\n",
       "      <td>Männistönpolku 3</td>\n",
       "      <td>Männistöstigen 3</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.042426</td>\n",
       "      <td>60.293216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1005</td>\n",
       "      <td>Tikkurilan asema</td>\n",
       "      <td>Dickursby station</td>\n",
       "      <td>Tikkurila station</td>\n",
       "      <td>Ratatie 13</td>\n",
       "      <td>Banvägen 13</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>30</td>\n",
       "      <td>25.044587</td>\n",
       "      <td>60.294775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1007</td>\n",
       "      <td>Tikkuraitti</td>\n",
       "      <td>Dickurstråket</td>\n",
       "      <td>Tikkuraitti</td>\n",
       "      <td>Tikkuraitti 18</td>\n",
       "      <td>Dickurstråket 18</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.036411</td>\n",
       "      <td>60.293413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1009</td>\n",
       "      <td>Maalitori</td>\n",
       "      <td>Målfärgstorget</td>\n",
       "      <td>Maalitori</td>\n",
       "      <td>Maalipolku 11</td>\n",
       "      <td>Målfärgsstigen 11</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.044952</td>\n",
       "      <td>60.291863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1011</td>\n",
       "      <td>Virnatie</td>\n",
       "      <td>Vickervägen</td>\n",
       "      <td>Virnatie</td>\n",
       "      <td>Virnatie 1</td>\n",
       "      <td>Vickervägen 1</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.033286</td>\n",
       "      <td>60.287829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1013</td>\n",
       "      <td>Peltolantori</td>\n",
       "      <td>Peltolatorget</td>\n",
       "      <td>Peltolantori</td>\n",
       "      <td>Peltolantie 4a</td>\n",
       "      <td>Peltolavägen 4a</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.029950</td>\n",
       "      <td>60.293565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1015</td>\n",
       "      <td>Kurjenpolvenpuisto</td>\n",
       "      <td>Skogsnävsparken</td>\n",
       "      <td>Kurjenpolvenpuisto</td>\n",
       "      <td>Peltolantie 19</td>\n",
       "      <td>Peltolavägen 19</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.018708</td>\n",
       "      <td>60.294026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1277</td>\n",
       "      <td>Tikkurilan urheilupuisto</td>\n",
       "      <td>Dickursby idrottspark</td>\n",
       "      <td>Tikkurila sports park</td>\n",
       "      <td>Läntinen Valkoisenlähteentie 5</td>\n",
       "      <td>Västra Vitbäcksvägen</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.028427</td>\n",
       "      <td>60.299610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1017</td>\n",
       "      <td>Suopursunaukio</td>\n",
       "      <td>Getporsplatsen</td>\n",
       "      <td>Suopursunaukio</td>\n",
       "      <td>Kielotie 23</td>\n",
       "      <td>Konvaljvägen 23</td>\n",
       "      <td>Vantaa</td>\n",
       "      <td>Vanda</td>\n",
       "      <td>CityBike Vantaa</td>\n",
       "      <td>20</td>\n",
       "      <td>25.041870</td>\n",
       "      <td>60.299485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FID    ID                      Nimi                   Namn  \\\n",
       "0    1  1001                 Rillitori             Rilltorget   \n",
       "1    2  1003            Männistönpolku         Männistöstigen   \n",
       "2    3  1005          Tikkurilan asema      Dickursby station   \n",
       "3    4  1007               Tikkuraitti          Dickurstråket   \n",
       "4    5  1009                 Maalitori         Målfärgstorget   \n",
       "5    6  1011                  Virnatie            Vickervägen   \n",
       "6    7  1013              Peltolantori          Peltolatorget   \n",
       "7    8  1015        Kurjenpolvenpuisto        Skogsnävsparken   \n",
       "8    9  1277  Tikkurilan urheilupuisto  Dickursby idrottspark   \n",
       "9   10  1017            Suopursunaukio         Getporsplatsen   \n",
       "\n",
       "                    Name                          Osoite  \\\n",
       "0              Rillitori                Tikkurilantie 30   \n",
       "1         Männistönpolku                Männistönpolku 3   \n",
       "2      Tikkurila station                      Ratatie 13   \n",
       "3            Tikkuraitti                  Tikkuraitti 18   \n",
       "4              Maalitori                   Maalipolku 11   \n",
       "5               Virnatie                      Virnatie 1   \n",
       "6           Peltolantori                  Peltolantie 4a   \n",
       "7     Kurjenpolvenpuisto                  Peltolantie 19   \n",
       "8  Tikkurila sports park  Läntinen Valkoisenlähteentie 5   \n",
       "9         Suopursunaukio                     Kielotie 23   \n",
       "\n",
       "                 Adress Kaupunki   Stad       Operaattor  Kapasiteet  \\\n",
       "0     Dickursbyvägen 30   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "1      Männistöstigen 3   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "2           Banvägen 13   Vantaa  Vanda  CityBike Vantaa          30   \n",
       "3      Dickurstråket 18   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "4     Målfärgsstigen 11   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "5         Vickervägen 1   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "6       Peltolavägen 4a   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "7       Peltolavägen 19   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "8  Västra Vitbäcksvägen   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "9       Konvaljvägen 23   Vantaa  Vanda  CityBike Vantaa          20   \n",
       "\n",
       "           x          y  \n",
       "0  25.051580  60.293027  \n",
       "1  25.042426  60.293216  \n",
       "2  25.044587  60.294775  \n",
       "3  25.036411  60.293413  \n",
       "4  25.044952  60.291863  \n",
       "5  25.033286  60.287829  \n",
       "6  25.029950  60.293565  \n",
       "7  25.018708  60.294026  \n",
       "8  25.028427  60.299610  \n",
       "9  25.041870  60.299485  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_bikes = pd.read_csv('Vantaan_kaupunkipyöräasemat.csv')\n",
    "city_bikes.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "no1 = df2[df2['hash'] == '144afc51fc2443c7031a2912566eff2ce60226a49d6d2bd7c2923700']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.set_mapbox_access_token('pk.eyJ1IjoiYmxlc3NlZDY2IiwiYSI6ImNrMWhudTkzbDBoYzMzaHBjZm90OGZkcWMifQ.opsS4eksuFXKYgh4x_T4Sg')\n",
    "px.scatter_mapbox(no1,\n",
    "                 lat='latitude_x',\n",
    "                 lon='longitude_x',\n",
    "                 animation_frame='times')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 13)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_bikes.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hashed = list(df2['hash'].unique())\n",
    "tidy = pd.DataFrame()\n",
    "\n",
    "for hashes,i in zip(hashed,len(hashed)):\n",
    "    'init_{}'.format(i) = df1.assign(X=df2['latitude_x'], Y=df2['longitude_x'],extreme='position_{}'.format(i),hashes = hashes)\n",
    "    tidy = pd.concat([tidy,'init_{}'.format(i)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(5):\n",
    "    'fe_{} = 2'.format(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "with open('graph.json', 'r') as f:\n",
    "    data = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys = ['station', 'depth', 'weight', 'stayed']\n",
    "values = data.values()\n",
    "\n",
    "dict_1 = {}\n",
    "\n",
    "for key in keys:\n",
    "    dict_1[key] = data[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_1 = {'station': 'root',\n",
    " 'depth': 0,\n",
    " 'weight': 1,\n",
    " 'stayed': 0}\n",
    "dict_2 = {'depth': 1,\n",
    "    'weight': 100,\n",
    "    'stayed': 0}\n",
    "dict_3 = {'station': '000000006b44cce7',\n",
    "       'depth': 2,\n",
    "       'weight': 27,\n",
    "       'stayed': 632930}\n",
    "dict_4 = {'station': '00000000fb7600be',\n",
    "          'depth': 3,\n",
    "          'weight': 4,\n",
    "          'stayed': 35950}\n",
    "dict_5 = {'station': '000000006b44cce7',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 95}\n",
    "dict_6 = {'station': '00000000fb7600be',\n",
    "                'depth': 5,\n",
    "                'weight': 1,\n",
    "                'stayed': 0}\n",
    "dict_7 = {'station': '000000006b087f40',\n",
    "             'depth': 4,\n",
    "             'weight': 2,\n",
    "             'stayed': 107}\n",
    "dict_8 = {'station': '00000000aa852af1',\n",
    "                'depth': 5,\n",
    "                'weight': 2,\n",
    "                'stayed': 61683}\n",
    "dict_9 = {'station': '00000000aa852af1',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 42}\n",
    "dict_10 = {'station': '00000000afef4555',\n",
    "          'depth': 3,\n",
    "          'weight': 2,\n",
    "          'stayed': 34}\n",
    "dict_11 = {'station': '0000000019fb59c4',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 19295}\n",
    "dict_12 = {'station': '0000000006dd41f6',\n",
    "                'depth': 5,\n",
    "                'weight': 1,\n",
    "                'stayed': 8}\n",
    "dict_13 = {'station': '00000000fdda10fe',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 310}\n",
    "dict_14 = {'station': '0000000038bf9618',\n",
    "                'depth': 5,\n",
    "                'weight': 1,\n",
    "                'stayed': 44}\n",
    "dict_15 = {'station': '0000000038d83d41',\n",
    "          'depth': 3,\n",
    "          'weight': 2,\n",
    "          'stayed': 41803}\n",
    "dict_16 = {'station': '00000000f1124bca',\n",
    "                'depth': 5,\n",
    "                'weight': 1,\n",
    "                'stayed': 0}\n",
    "dict_17 = {'station': '0000000038bf9618',\n",
    "                'depth': 5,\n",
    "                'weight': 1,\n",
    "                'stayed': 10}\n",
    "dict_18 = {'station': '0000000038d83d41',\n",
    "          'depth': 3,\n",
    "          'weight': 2,\n",
    "          'stayed': 41803}\n",
    "dict_19 = {'station': '00000000f1124bca',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 0}\n",
    "dict_20 = {'station': '0000000038bf9618',\n",
    "       'depth': 2,\n",
    "       'weight': 9,\n",
    "       'stayed': 239755}\n",
    "dict_21 = {'station': '00000000aa852af1',\n",
    "          'depth': 3,\n",
    "          'weight': 2,\n",
    "          'stayed': 67362}\n",
    "dict_22 = {'station': '000000006b44cce7',\n",
    "          'depth': 3,\n",
    "          'weight': 1,\n",
    "          'stayed': 10}\n",
    "dict_23 = {'station': '000000006b087f40',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 24}\n",
    "dict_24 = {'station': '00000000aa852af1',\n",
    "             'depth': 4,\n",
    "             'weight': 1,\n",
    "             'stayed': 8}\n",
    "dict_25 = {'station': '0000000038d83d41',\n",
    "                'depth': 5,\n",
    "                'weight': 3,\n",
    "                'stayed': 111}\n",
    "\n",
    "list_dict = [dict_1,dict_2,dict_3,dict_4,dict_5,dict_6,dict_7,dict_8,dict_9,dict_10,dict_11,dict_12,dict_13,dict_14,dict_15\n",
    "            ,dict_16,dict_17,dict_18,dict_19,dict_20,dict_21,dict_22,dict_23,dict_24,dict_25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(list_dict)\n",
    "\n",
    "new_df['serial'] = new_df['station']\n",
    "\n",
    "df5 = df1.merge(new_df,on='serial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
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\"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"updatemenus\": [{\"buttons\": [{\"args\": [null, {\"frame\": {\"duration\": 500, \"redraw\": true}, \"fromcurrent\": true, \"mode\": \"immediate\", \"transition\": {\"duration\": 500, \"easing\": \"linear\"}}], \"label\": \"&#9654;\", \"method\": \"animate\"}, {\"args\": [[null], {\"frame\": {\"duration\": 0, \"redraw\": true}, \"fromcurrent\": true, \"mode\": \"immediate\", \"transition\": {\"duration\": 0, \"easing\": \"linear\"}}], \"label\": \"&#9724;\", \"method\": \"animate\"}], \"direction\": \"left\", \"pad\": {\"r\": 10, \"t\": 70}, \"showactive\": false, \"type\": \"buttons\", \"x\": 0.1, \"xanchor\": \"right\", \"y\": 0, \"yanchor\": \"top\"}]},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            Plotly.addFrames('a490721e-88a7-48bd-ae7e-5cd025dc3b7e', [{\"data\": [{\"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"<b>%{hovertext}</b><br><br>depth=5<br>stayed=%{marker.size}<br>latitude=%{lat}<br>longitude=%{lon}\", \"hovertext\": [\"0000000006dd41f6\", \"0000000038bf9618\", \"0000000038bf9618\", \"0000000038d83d41\", \"00000000aa852af1\", \"00000000f1124bca\", \"00000000fb7600be\"], \"lat\": [60.14951, 60.1538, 60.1538, 60.16544, 60.16897, 60.16075, 60.16629], \"legendgroup\": \"\", \"lon\": [24.92138, 24.95595, 24.95595, 24.95398, 24.95222, 24.95883, 24.95957], \"marker\": {\"color\": \"#636efa\", \"size\": [8, 44, 10, 111, 61683, 0, 0], \"sizemode\": \"area\", \"sizeref\": 1582.325}, \"mode\": \"markers\", \"name\": \"\", \"showlegend\": false, \"subplot\": \"mapbox\", \"type\": \"scattermapbox\"}], \"name\": \"5\"}, {\"data\": [{\"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"<b>%{hovertext}</b><br><br>depth=4<br>stayed=%{marker.size}<br>latitude=%{lat}<br>longitude=%{lon}\", \"hovertext\": [\"0000000019fb59c4\", \"000000006b087f40\", \"000000006b087f40\", \"000000006b44cce7\", \"00000000aa852af1\", \"00000000aa852af1\", \"00000000f1124bca\", \"00000000fdda10fe\"], \"lat\": [60.15432, 60.17002, 60.17002, 60.16762, 60.16897, 60.16897, 60.16075, 60.16531], \"legendgroup\": \"\", \"lon\": [24.9211, 24.94407, 24.94407, 24.95415, 24.95222, 24.95222, 24.95883, 24.9442], \"marker\": {\"color\": \"#636efa\", \"size\": [19295, 107, 24, 95, 42, 8, 0, 310], \"sizemode\": \"area\", \"sizeref\": 1582.325}, \"mode\": \"markers\", \"name\": \"\", \"showlegend\": false, \"subplot\": \"mapbox\", \"type\": \"scattermapbox\"}], \"name\": \"4\"}, {\"data\": [{\"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"<b>%{hovertext}</b><br><br>depth=2<br>stayed=%{marker.size}<br>latitude=%{lat}<br>longitude=%{lon}\", \"hovertext\": [\"0000000038bf9618\", \"000000006b44cce7\"], \"lat\": [60.1538, 60.16762], \"legendgroup\": \"\", \"lon\": [24.95595, 24.95415], \"marker\": {\"color\": \"#636efa\", \"size\": [239755, 632930], \"sizemode\": \"area\", \"sizeref\": 1582.325}, \"mode\": \"markers\", \"name\": \"\", \"showlegend\": false, \"subplot\": \"mapbox\", \"type\": \"scattermapbox\"}], \"name\": \"2\"}, {\"data\": [{\"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"<b>%{hovertext}</b><br><br>depth=3<br>stayed=%{marker.size}<br>latitude=%{lat}<br>longitude=%{lon}\", \"hovertext\": [\"0000000038d83d41\", \"0000000038d83d41\", \"000000006b44cce7\", \"00000000aa852af1\", \"00000000afef4555\", \"00000000fb7600be\"], \"lat\": [60.16544, 60.16544, 60.16762, 60.16897, 60.16829, 60.16629], \"legendgroup\": \"\", \"lon\": [24.95398, 24.95398, 24.95415, 24.95222, 24.94214, 24.95957], \"marker\": {\"color\": \"#636efa\", \"size\": [41803, 41803, 10, 67362, 34, 35950], \"sizemode\": \"area\", \"sizeref\": 1582.325}, \"mode\": \"markers\", \"name\": \"\", \"showlegend\": false, \"subplot\": \"mapbox\", \"type\": \"scattermapbox\"}], \"name\": \"3\"}]);\n",
       "                        }).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('a490721e-88a7-48bd-ae7e-5cd025dc3b7e');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "px.set_mapbox_access_token('pk.eyJ1IjoiYmxlc3NlZDY2IiwiYSI6ImNrMWhudTkzbDBoYzMzaHBjZm90OGZkcWMifQ.opsS4eksuFXKYgh4x_T4Sg')\n",
    "px.scatter_mapbox(df5,\n",
    "                  size = 'stayed',\n",
    "                 lat='latitude',\n",
    "                 lon='longitude',\n",
    "                 hover_name='station',\n",
    "                 animation_frame='depth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>serial</th>\n",
       "      <th>address</th>\n",
       "      <th>postalcode</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>description</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>group</th>\n",
       "      <th>alias</th>\n",
       "      <th>size</th>\n",
       "      <th>station</th>\n",
       "      <th>depth</th>\n",
       "      <th>weight</th>\n",
       "      <th>stayed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0000000006dd41f6</td>\n",
       "      <td>Munkkisaarenlaituri</td>\n",
       "      <td>00150</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Hernesaari LHC</td>\n",
       "      <td>60.14951</td>\n",
       "      <td>24.92138</td>\n",
       "      <td>hernesaari</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>0000000006dd41f6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0000000019fb59c4</td>\n",
       "      <td>Tyynenmerenkatu 8</td>\n",
       "      <td>00220</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Lansiterminaali 1</td>\n",
       "      <td>60.15432</td>\n",
       "      <td>24.92110</td>\n",
       "      <td>lansiterminaali</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>0000000019fb59c4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>19295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>Mattolaituri</td>\n",
       "      <td>00140</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kaivopuiston ranta</td>\n",
       "      <td>60.15380</td>\n",
       "      <td>24.95595</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>Mattolaituri</td>\n",
       "      <td>00140</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kaivopuiston ranta</td>\n",
       "      <td>60.15380</td>\n",
       "      <td>24.95595</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>Mattolaituri</td>\n",
       "      <td>00140</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Finland</td>\n",
       "      <td>Kaivopuiston ranta</td>\n",
       "      <td>60.15380</td>\n",
       "      <td>24.95595</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>0000000038bf9618</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>239755</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             serial              address postalcode      city  country  \\\n",
       "0  0000000006dd41f6  Munkkisaarenlaituri      00150  Helsinki  Finland   \n",
       "1  0000000019fb59c4    Tyynenmerenkatu 8      00220  Helsinki  Finland   \n",
       "2  0000000038bf9618         Mattolaituri      00140  Helsinki  Finland   \n",
       "3  0000000038bf9618         Mattolaituri      00140  Helsinki  Finland   \n",
       "4  0000000038bf9618         Mattolaituri      00140  Helsinki  Finland   \n",
       "\n",
       "          description  latitude  longitude            group alias  size  \\\n",
       "0      Hernesaari LHC  60.14951   24.92138       hernesaari  None    30   \n",
       "1   Lansiterminaali 1  60.15432   24.92110  lansiterminaali  None    30   \n",
       "2  Kaivopuiston ranta  60.15380   24.95595          default  None    30   \n",
       "3  Kaivopuiston ranta  60.15380   24.95595          default  None    30   \n",
       "4  Kaivopuiston ranta  60.15380   24.95595          default  None    30   \n",
       "\n",
       "            station  depth  weight  stayed  \n",
       "0  0000000006dd41f6      5       1       8  \n",
       "1  0000000019fb59c4      4       1   19295  \n",
       "2  0000000038bf9618      5       1      44  \n",
       "3  0000000038bf9618      5       1      10  \n",
       "4  0000000038bf9618      2       9  239755  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>station</th>\n",
       "      <th>depth</th>\n",
       "      <th>weight</th>\n",
       "      <th>stayed</th>\n",
       "      <th>children</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>root</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>[{'depth': 1, 'weight': 100, 'stayed': 0, 'chi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  station  depth  weight  stayed  \\\n",
       "0    root      0       1       0   \n",
       "\n",
       "                                            children  \n",
       "0  [{'depth': 1, 'weight': 100, 'stayed': 0, 'chi...  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "move_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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