DataProcessing.ipynb

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      "source": "import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport geopandas as gpd\nfrom shapely.geometry import Point, Polygon\nfrom geopandas import GeoDataFrame\nimport matplotlib.pyplot as plt\nimport sklearn\nimport sklearn.utils\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cluster import DBSCAN\nimport matplotlib.pyplot as plt\nimport scipy.stats as stats\nfrom datetime import datetime\nimport datetime\nfrom datetime import timedelta",
      "outputs": [],
      "execution_count": null
    },
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      "source": "\"\"\"\nImporting csv file with new data and filtering it further.\n\n    Returns:\n    _ _ _ _ _ _ _ _ _ _ _ _ _\n    Dataframe with dropped nan values and dropped index.\n    \n\"\"\"\nnewdata = pd.read_csv(\"/work/Data/mergeddata.csv\")\nnewdata = newdata.dropna().drop(columns=[\"Unnamed: 0\"])\nnewdata",
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          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00003-a99dad56-66cb-4fd5-ba11-7ba4c68fdd44",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "88a4878",
        "execution_millis": 1001,
        "execution_start": 1620255781245,
        "deepnote_cell_type": "code"
      },
      "source": "\"\"\"\n\nMaking sure there are no duplicated values in the dataframe.\n\n\"\"\"\nnewdata[newdata.duplicated()]",
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
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                  "_deepnote_index_column": 416774
                }
              ]
            },
            "text/plain": "        Unnamed: 0.1      timestamp                               vin  \\\n352151         138.0  1578942333714  00089af9f7d3588a888379cf0ffc53cc   \n352152        3548.0  1578917535963  005b420fd9275f080ad79e1c4330357e   \n352153        3549.0  1578917659974  005b420fd9275f080ad79e1c4330357e   \n352154        3550.0  1578917668955  005b420fd9275f080ad79e1c4330357e   \n352155        3556.0  1578918206960  005b420fd9275f080ad79e1c4330357e   \n...              ...            ...                               ...   \n416770           3.0  1580774399978  647353ec630718a358cdfbf99a990888   \n416771           4.0  1580774399971  75745529f421c99f0636efa2f5ba6a4e   \n416772           5.0  1580774399978  86752012e17bbc28fb9b8f938412626a   \n416773           6.0  1580774399995  92e64fbb0355adc2c4cd663d288b7932   \n416774           7.0  1580774399993  cc63c9825a40f6dc03a7ccfc6cdc7e8d   \n\n        sequence  navigation_location_coordinate_latitude  \\\n352151       733                                41.492962   \n352152      1142                                41.335046   \n352153      1142                                41.337986   \n352154      1142                                41.338722   \n352155      1142                                41.292217   \n...          ...                                      ...   \n416770      1407                                40.058158   \n416771      1466                                39.971760   \n416772       817                                39.850929   \n416773        26                                39.875714   \n416774       623                                40.089245   \n\n        navigation_location_coordinate_longitude  \\\n352151                                -81.995163   \n352152                                -81.398117   \n352153                                -81.402637   \n352154                                -81.403610   \n352155                                -81.409466   \n...                                          ...   \n416770                                -82.863375   \n416771                                -82.915914   \n416772                                -83.081206   \n416773                                -83.038050   \n416774                                -82.838651   \n\n                                                      key  \n352151  eyJ0aW1lc3RhbXAiOjE1Nzg5NDIzMzM3MTQsInZpbiI6Ij...  \n352152  eyJ0aW1lc3RhbXAiOjE1Nzg5MTc1MzU5NjMsInZpbiI6Ij...  \n352153  eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2NTk5NzQsInZpbiI6Ij...  \n352154  eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2Njg5NTUsInZpbiI6Ij...  \n352155  eyJ0aW1lc3RhbXAiOjE1Nzg5MTgyMDY5NjAsInZpbiI6Ij...  \n...                                                   ...  \n416770  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzgsInZpbiI6Ij...  \n416771  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzEsInZpbiI6Ij...  \n416772  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzgsInZpbiI6Ij...  \n416773  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTUsInZpbiI6Ij...  \n416774  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTMsInZpbiI6Im...  \n\n[64624 rows x 7 columns]",
            "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>Unnamed: 0.1</th>\n      <th>timestamp</th>\n      <th>vin</th>\n      <th>sequence</th>\n      <th>navigation_location_coordinate_latitude</th>\n      <th>navigation_location_coordinate_longitude</th>\n      <th>key</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>352151</th>\n      <td>138.0</td>\n      <td>1578942333714</td>\n      <td>00089af9f7d3588a888379cf0ffc53cc</td>\n      <td>733</td>\n      <td>41.492962</td>\n      <td>-81.995163</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5NDIzMzM3MTQsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>352152</th>\n      <td>3548.0</td>\n      <td>1578917535963</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.335046</td>\n      <td>-81.398117</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc1MzU5NjMsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>352153</th>\n      <td>3549.0</td>\n      <td>1578917659974</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.337986</td>\n      <td>-81.402637</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2NTk5NzQsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>352154</th>\n      <td>3550.0</td>\n      <td>1578917668955</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.338722</td>\n      <td>-81.403610</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2Njg5NTUsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>352155</th>\n      <td>3556.0</td>\n      <td>1578918206960</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.292217</td>\n      <td>-81.409466</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTgyMDY5NjAsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>416770</th>\n      <td>3.0</td>\n      <td>1580774399978</td>\n      <td>647353ec630718a358cdfbf99a990888</td>\n      <td>1407</td>\n      <td>40.058158</td>\n      <td>-82.863375</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzgsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>416771</th>\n      <td>4.0</td>\n      <td>1580774399971</td>\n      <td>75745529f421c99f0636efa2f5ba6a4e</td>\n      <td>1466</td>\n      <td>39.971760</td>\n      <td>-82.915914</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzEsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>416772</th>\n      <td>5.0</td>\n      <td>1580774399978</td>\n      <td>86752012e17bbc28fb9b8f938412626a</td>\n      <td>817</td>\n      <td>39.850929</td>\n      <td>-83.081206</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5NzgsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>416773</th>\n      <td>6.0</td>\n      <td>1580774399995</td>\n      <td>92e64fbb0355adc2c4cd663d288b7932</td>\n      <td>26</td>\n      <td>39.875714</td>\n      <td>-83.038050</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTUsInZpbiI6Ij...</td>\n    </tr>\n    <tr>\n      <th>416774</th>\n      <td>7.0</td>\n      <td>1580774399993</td>\n      <td>cc63c9825a40f6dc03a7ccfc6cdc7e8d</td>\n      <td>623</td>\n      <td>40.089245</td>\n      <td>-82.838651</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTMsInZpbiI6Im...</td>\n    </tr>\n  </tbody>\n</table>\n<p>64624 rows × 7 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00004-28ef2097-b102-4809-9fc4-c6a17fe58c80",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "a8786dd6",
        "execution_millis": 1,
        "execution_start": 1620255794642,
        "deepnote_cell_type": "code"
      },
      "source": "\"\"\"\n\nConverting the timestamp column in the dataframe from milliseconds into seconds.\n\nMaking a new column in the dataframe with the converted timestamp column as a datetime object.\n\n\"\"\"\n\nnewdata['timestamp'] = newdata['timestamp']/1000\nnewdata['datetime'] = pd.to_datetime(newdata['timestamp'], unit = 's')",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00005-8863458d-3eff-477f-97a8-6943865ebd64",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "b1e75b1e",
        "execution_millis": 88,
        "execution_start": 1620255796164,
        "deepnote_cell_type": "code"
      },
      "source": "## Getting first and last locations based on trip_sequence(engine cycle)\ndatetimeFormat = '%Y-%m-%d %H:%M:%S.%f'\ntimeline_df = newdata.groupby(\"sequence\")[\"datetime\"].agg([\"first\",\"last\"])\ntimeline_df\n\n## Getting the last latitude and longitude for each trip or sequence\ntimeline_df[\"latitude\"] = newdata.groupby(\"sequence\")[\"navigation_location_coordinate_latitude\"].agg([\"last\"])\ntimeline_df[\"longitude\"] = newdata.groupby(\"sequence\")[\"navigation_location_coordinate_longitude\"].agg([\"last\"])\n\nx = timeline_df[\"last\"]-timeline_df[\"first\"]\nduration = pd.to_timedelta(x.values, unit = 's').total_seconds()\ntimeline_df[\"trip_duration_s\"] = duration",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00006-4103d6b1-f750-46a3-8765-d019ceeaf3a2",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "56df70a2",
        "execution_millis": 4,
        "execution_start": 1620255797383,
        "deepnote_cell_type": "code"
      },
      "source": "\"\"\"\nProcess filtered dataframe into a dataframe grouped by vin and sequence and aggregate by first \nand last to find the start latitude and longitudes and end latitudes and longitudes.\n\n    Returns:\n    _ _ _ _ _ _ _ _ _ _ _ _ _\n    Dataframe grouped by vin and sequence with four new columns for start/end latitudes and longitudes.\n\n\"\"\"\ndef gen_timeline_df(df):\n    timeline = df.groupby([\"vin\",\"sequence\"])[\"timestamp\"].agg([\"first\",\"last\"])\n    timeline[\"start_lat\"] = df.groupby([\"vin\",\"sequence\"])[\"navigation_location_coordinate_latitude\"].agg([\"first\"])\n    timeline[\"start_long\"] = df.groupby([\"vin\",\"sequence\"])[\"navigation_location_coordinate_longitude\"].agg([\"first\"])\n    timeline[\"latitude\"] = df.groupby([\"vin\",\"sequence\"])[\"navigation_location_coordinate_latitude\"].agg([\"last\"])\n    timeline[\"longitude\"] = df.groupby([\"vin\",\"sequence\"])[\"navigation_location_coordinate_longitude\"].agg([\"last\"])\n    return timeline\n\"\"\"\nProcess filtered dataframe into a dataframe with the dwell time duration for each completed trip\n(time of previous trip location with engine off to time of current trip with engine on)\n    \n    Returns:\n    _ _ _ _ _ _ _ _ _ _ _ _ _\n    Dataframe with new column for dwell time duration in seconds.\n\n\"\"\"\ndef calc_dwell_duration(df):\n    df['dwell time duration (seconds)'] = ''\n    vins = df.index.levels[0]\n    for i in vins:\n        num_trips = len(df.loc[i])\n        for j in np.arange(num_trips):\n            if j == 0:\n                ## first trip has no associated dwell time\n                df.loc[i].iloc[j,6] = ''\n            else:\n                ## find differences in timestamps between consecutive trips (dwell time)\n                if df.loc[i].iloc[j, 0] - df.loc[i].iloc[j - 1, 1] < 0:\n                    df.loc[i].iloc[j,6] = ''\n                else:\n                    df.loc[i].iloc[j,6] = df.loc[i].iloc[j, 0] - df.loc[i].iloc[j - 1, 1]\n    \n",
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "code",
      "metadata": {
        "tags": [],
        "cell_id": "00007-883c12e7-070d-42f1-9055-64d86cc0d6ae",
        "deepnote_to_be_reexecuted": false,
        "source_hash": "36d110de",
        "execution_millis": 1728,
        "execution_start": 1620255797994,
        "deepnote_cell_type": "code"
      },
      "source": "\"\"\"\n\nCreate a new dataframe with the original newdata dataframe using the two functions above to find \nthe dwell time duration in seconds.\n\n\"\"\"\ndwell_df = gen_timeline_df(newdata)\ncalc_dwell_duration(dwell_df)\ndwell_df.loc['123b9a7bbd7775662d3a55656dc2379b']",
      "outputs": [
        {
          "name": "stderr",
          "text": "/shared-libs/python3.9/py/lib/python3.9/site-packages/pandas/core/indexing.py:1720: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n  self._setitem_single_column(loc, value, pi)\n",
          "output_type": "stream"
        },
        {
          "output_type": "execute_result",
          "execution_count": 25,
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            "text/plain": "                 first          last  start_lat  start_long   latitude  \\\nsequence                                                                 \n182       1.580768e+09  1.580770e+09  40.092665  -82.776403  40.161695   \n1185      1.580767e+09  1.580768e+09  41.526163  -83.633261  41.570769   \n2065      1.580774e+09  1.580774e+09  39.911455  -82.784533  39.911642   \n\n          longitude dwell time duration (seconds)  \nsequence                                           \n182      -83.146587                                \n1185     -83.657931                                \n2065     -82.810996                      6190.533  ",
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            "text/plain": "        Unnamed: 0.1     timestamp                               vin  \\\n0              138.0  1.578942e+09  00089af9f7d3588a888379cf0ffc53cc   \n1             3548.0  1.578918e+09  005b420fd9275f080ad79e1c4330357e   \n2             3549.0  1.578918e+09  005b420fd9275f080ad79e1c4330357e   \n3             3550.0  1.578918e+09  005b420fd9275f080ad79e1c4330357e   \n4             3556.0  1.578918e+09  005b420fd9275f080ad79e1c4330357e   \n...              ...           ...                               ...   \n736059         235.0  1.580774e+09  cc63c9825a40f6dc03a7ccfc6cdc7e8d   \n736060         238.0  1.580774e+09  cc63c9825a40f6dc03a7ccfc6cdc7e8d   \n736061         241.0  1.580774e+09  d8538c269b6cb7eec9a1f3b2bd8fc2d7   \n736062         242.0  1.580774e+09  d8538c269b6cb7eec9a1f3b2bd8fc2d7   \n736063         263.0  1.580774e+09  dc1133b0088aacfd4583783ff9ee06cb   \n\n        sequence  navigation_location_coordinate_latitude  \\\n0            733                                41.492962   \n1           1142                                41.335046   \n2           1142                                41.337986   \n3           1142                                41.338722   \n4           1142                                41.292217   \n...          ...                                      ...   \n736059       623                                40.089127   \n736060       623                                40.089245   \n736061       764                                40.304051   \n736062       764                                40.304008   \n736063       765                                39.967060   \n\n        navigation_location_coordinate_longitude  \\\n0                                     -81.995163   \n1                                     -81.398117   \n2                                     -81.402637   \n3                                     -81.403610   \n4                                     -81.409466   \n...                                          ...   \n736059                                -82.838978   \n736060                                -82.838651   \n736061                                -83.524914   \n736062                                -83.524871   \n736063                                -82.924559   \n\n                                                      key  \\\n0       eyJ0aW1lc3RhbXAiOjE1Nzg5NDIzMzM3MTQsInZpbiI6Ij...   \n1       eyJ0aW1lc3RhbXAiOjE1Nzg5MTc1MzU5NjMsInZpbiI6Ij...   \n2       eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2NTk5NzQsInZpbiI6Ij...   \n3       eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2Njg5NTUsInZpbiI6Ij...   \n4       eyJ0aW1lc3RhbXAiOjE1Nzg5MTgyMDY5NjAsInZpbiI6Ij...   \n...                                                   ...   \n736059  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg5ODcsInZpbiI6Im...   \n736060  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTMsInZpbiI6Im...   \n736061  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg4NzgsInZpbiI6Im...   \n736062  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk4NzksInZpbiI6Im...   \n736063  eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg5MDUsInZpbiI6Im...   \n\n                            datetime  \n0      2020-01-13 19:05:33.713999872  \n1      2020-01-13 12:12:15.963000064  \n2      2020-01-13 12:14:19.973999872  \n3      2020-01-13 12:14:28.954999808  \n4      2020-01-13 12:23:26.960000000  \n...                              ...  \n736059 2020-02-03 23:59:58.987000064  \n736060 2020-02-03 23:59:59.992999936  \n736061 2020-02-03 23:59:58.878000128  \n736062 2020-02-03 23:59:59.879000064  \n736063 2020-02-03 23:59:58.904999936  \n\n[671439 rows x 8 columns]",
            "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>Unnamed: 0.1</th>\n      <th>timestamp</th>\n      <th>vin</th>\n      <th>sequence</th>\n      <th>navigation_location_coordinate_latitude</th>\n      <th>navigation_location_coordinate_longitude</th>\n      <th>key</th>\n      <th>datetime</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>138.0</td>\n      <td>1.578942e+09</td>\n      <td>00089af9f7d3588a888379cf0ffc53cc</td>\n      <td>733</td>\n      <td>41.492962</td>\n      <td>-81.995163</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5NDIzMzM3MTQsInZpbiI6Ij...</td>\n      <td>2020-01-13 19:05:33.713999872</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>3548.0</td>\n      <td>1.578918e+09</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.335046</td>\n      <td>-81.398117</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc1MzU5NjMsInZpbiI6Ij...</td>\n      <td>2020-01-13 12:12:15.963000064</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3549.0</td>\n      <td>1.578918e+09</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.337986</td>\n      <td>-81.402637</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2NTk5NzQsInZpbiI6Ij...</td>\n      <td>2020-01-13 12:14:19.973999872</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3550.0</td>\n      <td>1.578918e+09</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.338722</td>\n      <td>-81.403610</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTc2Njg5NTUsInZpbiI6Ij...</td>\n      <td>2020-01-13 12:14:28.954999808</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3556.0</td>\n      <td>1.578918e+09</td>\n      <td>005b420fd9275f080ad79e1c4330357e</td>\n      <td>1142</td>\n      <td>41.292217</td>\n      <td>-81.409466</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1Nzg5MTgyMDY5NjAsInZpbiI6Ij...</td>\n      <td>2020-01-13 12:23:26.960000000</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>736059</th>\n      <td>235.0</td>\n      <td>1.580774e+09</td>\n      <td>cc63c9825a40f6dc03a7ccfc6cdc7e8d</td>\n      <td>623</td>\n      <td>40.089127</td>\n      <td>-82.838978</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg5ODcsInZpbiI6Im...</td>\n      <td>2020-02-03 23:59:58.987000064</td>\n    </tr>\n    <tr>\n      <th>736060</th>\n      <td>238.0</td>\n      <td>1.580774e+09</td>\n      <td>cc63c9825a40f6dc03a7ccfc6cdc7e8d</td>\n      <td>623</td>\n      <td>40.089245</td>\n      <td>-82.838651</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk5OTMsInZpbiI6Im...</td>\n      <td>2020-02-03 23:59:59.992999936</td>\n    </tr>\n    <tr>\n      <th>736061</th>\n      <td>241.0</td>\n      <td>1.580774e+09</td>\n      <td>d8538c269b6cb7eec9a1f3b2bd8fc2d7</td>\n      <td>764</td>\n      <td>40.304051</td>\n      <td>-83.524914</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg4NzgsInZpbiI6Im...</td>\n      <td>2020-02-03 23:59:58.878000128</td>\n    </tr>\n    <tr>\n      <th>736062</th>\n      <td>242.0</td>\n      <td>1.580774e+09</td>\n      <td>d8538c269b6cb7eec9a1f3b2bd8fc2d7</td>\n      <td>764</td>\n      <td>40.304008</td>\n      <td>-83.524871</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTk4NzksInZpbiI6Im...</td>\n      <td>2020-02-03 23:59:59.879000064</td>\n    </tr>\n    <tr>\n      <th>736063</th>\n      <td>263.0</td>\n      <td>1.580774e+09</td>\n      <td>dc1133b0088aacfd4583783ff9ee06cb</td>\n      <td>765</td>\n      <td>39.967060</td>\n      <td>-82.924559</td>\n      <td>eyJ0aW1lc3RhbXAiOjE1ODA3NzQzOTg5MDUsInZpbiI6Im...</td>\n      <td>2020-02-03 23:59:58.904999936</td>\n    </tr>\n  </tbody>\n</table>\n<p>671439 rows × 8 columns</p>\n</div>"
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