2021-04-26_data_try.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils import DataLoader, TimeKeeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "detail_num = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = DataLoader.SHLDataLoader('data/train/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Label 读取完成，共 980527 条数据，用时 0.15s\n",
      "Location 读取完成，共 911109 条数据，用时 0.56s\n",
      "Wifi 读取完成，共 1459351 条数据，用时 31.56s\n",
      "\t-- Wifi 详细信息提取完成，共 1000 条数据，提取了 Wifi 中前 1000 行，用时 0.59s\n",
      "GPS 读取完成，共 1322749 条数据，用时 23.71s\n",
      "\t-- GPS 详细信息提取完成，共 1000 条数据，提取了 GPS 中前 1000 行，用时 0.68s\n",
      "Cells 读取完成，共 1324881 条数据，用时 22.38s\n",
      "\t-- Cells 详细信息提取完成，共 1000 条数据，提取了 Cells 中前 1000 行，用时 1.52s\n"
     ]
    }
   ],
   "source": [
    "data.load()"
   ]
  },
  {
   "source": [
    "## Location"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "loc_names = ['time', 'ign1', 'ign2', 'accuracy', 'latitude', 'longitude', 'altitude']\n",
    "loc = pd.read_table('data/train/Location.txt', header = None, names = loc_names, sep = \" \").drop(['ign1', 'ign2'], axis = 1)\n",
    "loc.head()"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": []
  },
  {
   "source": [
    "## Label"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            time  label\n",
       "0  1490431583000      4\n",
       "1  1490431584000      4\n",
       "2  1490431585000      4\n",
       "3  1490431586000      4\n",
       "4  1490431587000      4"
      ],
      "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>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1490431583000</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1490431584000</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1490431585000</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1490431586000</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1490431587000</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "label_names = ['time', 'label']\n",
    "label = pd.read_table('data/train/Label.txt', header = None, names = label_names, sep = \"\\t\")\n",
    "# label.groupby('label').count().apply(lambda x: x/label.shape[0], axis = 1)\n",
    "label.head()"
   ]
  },
  {
   "source": [
    "## Wifi"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wifi_detail_transformer(wifi_list, this_time):\n",
    "    res = np.array(wifi_list).reshape(-1, 5)\n",
    "    res = pd.DataFrame(res, columns = ('bssid', 'ssid', 'rssi', 'freq', 'cap'))\n",
    "    res.insert(loc = 0, column = 'time', value = [this_time] * res.shape[0])\n",
    "    return res"
   ]
  },
  {
   "source": [
    "time_recorder = []\n",
    "time_recorder.append(time.time())\n",
    "wifi = pd.read_table('data/train/Wifi.txt', header = None)\n",
    "wifi['time'] = wifi.apply(lambda x: x[0].split(\";\")[0], axis = 1)\n",
    "wifi['number'] = wifi.apply(lambda x: x[0].split(\";\")[3], axis = 1)\n",
    "time_recorder.append(time.time())\n",
    "# detail info\n",
    "wifi_detail = wifi.iloc[:detail_num,:].apply(lambda x: wifi_detail_transformer(x[0].split(\";\")[4:], x[0].split(\";\")[0]), axis = 1)\n",
    "wifi_detail = pd.concat(list(wifi_detail)).reset_index(drop = True)\n",
    "time_recorder.append(time.time())\n",
    "# delete raw data\n",
    "wifi.drop([0], axis = 1, inplace = True)\n",
    "print(pd.Series(time_recorder).diff(1))"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 56,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0          NaN\n1    33.852244\n2     0.663604\ndtype: float64\n"
     ]
    }
   ]
  },
  {
   "source": [
    "## GPS"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gps_detail_transformer(gps_list, this_time):\n",
    "    res = np.array(gps_list).reshape(-1, 4)\n",
    "    res = pd.DataFrame(res, columns = ('id', 'snr', 'azimuth', 'elevation'))\n",
    "    res.insert(loc = 0, column = 'time', value = [this_time] * res.shape[0])\n",
    "    # print(res)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_recorder = []\n",
    "time_recorder.append(time.time())\n",
    "gps = pd.read_table('data/train/GPS.txt', header = None)\n",
    "gps['time'] = gps.apply(lambda x: x[0].split(\" \")[0], axis = 1)\n",
    "gps['number'] = gps.apply(lambda x: x[0].split(\" \")[-1], axis = 1)\n",
    "time_recorder.append(time.time())\n",
    "# detail info\n",
    "gps_detail = gps.iloc[:detail_num,:].apply(lambda x: gps_detail_transformer(x[0].split(\" \")[3:-1], x[0].split(\";\")[0]), axis = 1)\n",
    "gps_detail = pd.concat(list(gps_detail)).reset_index(drop = True)\n",
    "time_recorder.append(time.time())\n",
    "time_recorder.append(time.time())\n",
    "# delete raw data\n",
    "gps.drop([0], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0          NaN\n1    26.050008\n2     0.588471\n3     0.000024\ndtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(pd.Series(time_recorder).diff(1))"
   ]
  },
  {
   "source": [
    "## Cells"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cells_detail_transformer(cells_list, this_time):\n",
    "    # get cell type\n",
    "    cells_type = re.findall(\" LTE | GSM | WCDMA \", cells_list)\n",
    "    # get detail info\n",
    "    cells_info = re.split(\" LTE | GSM | WCDMA \", cells_list)[1:]\n",
    "    cells_detail = []\n",
    "    for i, ctype in enumerate(cells_type):\n",
    "        if re.match('.*LTE.*', ctype):\n",
    "            info = ['LTE'] + np.array(cells_info[i].split(\" \"))[[0, 1, 5, 2, 3, 6, 7, 8]].tolist()\n",
    "        elif re.match('.*GSM.*', ctype):\n",
    "            info = ['GSM'] + np.array(cells_info[i].split(\" \")).tolist()\n",
    "        elif re.match(\".*WCDMA.*\", ctype):\n",
    "            info = ['WCDMA'] + np.array(cells_info[i].split(\" \"))[[0, 1, 2, 3, 4, 6, 7, 8]].tolist()\n",
    "        else:\n",
    "            raise ValueError(\"Unrecognized cell type {}\".format(ctype))\n",
    "        cells_detail.append(info)\n",
    "\n",
    "    cells_detail = pd.DataFrame(cells_detail, columns = (\"ctype\", \"isRegistered\", \"cid\", \"lac\", \"mmc\", \"mnc\", \"asuLevel\", \"dbm\", \"level\"))\n",
    "    cells_detail.insert(loc = 0, column = 'time', value = [this_time] * len(cells_type))\n",
    "\n",
    "    return cells_detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells = pd.read_table('data/train/Cells.txt', header = None)\n",
    "cells['time'] = cells.apply(lambda x: x[0].split(\" \")[0], axis = 1)\n",
    "cells['number'] = cells.apply(lambda x: x[0].split(\" \")[3], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cells_detail = cells.iloc[:detail_num,:].apply(lambda x: cells_detail_transformer(x[0], x[0].split(\" \")[0]), axis = 1)\n",
    "cells_detail = pd.concat(cells_detail.to_list()).reset_index(drop = True)\n",
    "cells.drop([0], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            time number\n",
       "0  1490430923343      3\n",
       "1  1490430923349      3\n",
       "2  1490430947385     13\n",
       "3  1490430953392      3\n",
       "4  1490430958903      3"
      ],
      "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>number</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1490430923343</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1490430923349</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1490430947385</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1490430953392</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1490430958903</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 35
    }
   ],
   "source": [
    "cells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             time  ctype isRegistered         cid         lac  mmc mnc  \\\n",
       "0   1490430923343    LTE            1   128000386         144  234  10   \n",
       "1   1490430923343    LTE            0  2147483647  2147483647  234  10   \n",
       "2   1490430923343    LTE            0  2147483647  2147483647  234  10   \n",
       "3   1490430923349    LTE            1   128000386         144  234  10   \n",
       "4   1490430923349    LTE            0  2147483647  2147483647  234  10   \n",
       "5   1490430923349    LTE            0  2147483647  2147483647  234  10   \n",
       "6   1490430947385    LTE            1   128000386         144  234  10   \n",
       "7   1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "8   1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "9   1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "10  1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "11  1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "12  1490430947385    GSM            0  2147483647  2147483647  234  10   \n",
       "13  1490430947385  WCDMA            0  2147483647  2147483647  234  10   \n",
       "14  1490430947385  WCDMA            0  2147483647  2147483647  234  10   \n",
       "15  1490430947385  WCDMA            0  2147483647  2147483647  234  10   \n",
       "16  1490430947385    LTE            0  2147483647  2147483647  234  10   \n",
       "17  1490430947385    LTE            0  2147483647  2147483647  234  10   \n",
       "18  1490430947385    LTE            0  2147483647  2147483647  234  10   \n",
       "19  1490430953392    LTE            1   128000386         144  234  10   \n",
       "\n",
       "   asuLevel   dbm level  \n",
       "0        25  -115     2  \n",
       "1        22  -118     1  \n",
       "2        16  -124     1  \n",
       "3        25  -115     2  \n",
       "4        22  -118     1  \n",
       "5        16  -124     1  \n",
       "6        20  -120     1  \n",
       "7        10   -93     3  \n",
       "8         6  -101     2  \n",
       "9         5  -103     2  \n",
       "10        2  -109     0  \n",
       "11        2  -109     0  \n",
       "12        2  -109     0  \n",
       "13        3  -107     1  \n",
       "14        3  -107     1  \n",
       "15        0  -113     0  \n",
       "16       20  -120     1  \n",
       "17       19  -121     1  \n",
       "18       19  -121     1  \n",
       "19       25  -115     2  "
      ],
      "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>ctype</th>\n      <th>isRegistered</th>\n      <th>cid</th>\n      <th>lac</th>\n      <th>mmc</th>\n      <th>mnc</th>\n      <th>asuLevel</th>\n      <th>dbm</th>\n      <th>level</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1490430923343</td>\n      <td>LTE</td>\n      <td>1</td>\n      <td>128000386</td>\n      <td>144</td>\n      <td>234</td>\n      <td>10</td>\n      <td>25</td>\n      <td>-115</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1490430923343</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>22</td>\n      <td>-118</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1490430923343</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>16</td>\n      <td>-124</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1490430923349</td>\n      <td>LTE</td>\n      <td>1</td>\n      <td>128000386</td>\n      <td>144</td>\n      <td>234</td>\n      <td>10</td>\n      <td>25</td>\n      <td>-115</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1490430923349</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>22</td>\n      <td>-118</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>1490430923349</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>16</td>\n      <td>-124</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1490430947385</td>\n      <td>LTE</td>\n      <td>1</td>\n      <td>128000386</td>\n      <td>144</td>\n      <td>234</td>\n      <td>10</td>\n      <td>20</td>\n      <td>-120</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>10</td>\n      <td>-93</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>6</td>\n      <td>-101</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>5</td>\n      <td>-103</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>2</td>\n      <td>-109</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>2</td>\n      <td>-109</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>1490430947385</td>\n      <td>GSM</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>2</td>\n      <td>-109</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>1490430947385</td>\n      <td>WCDMA</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>3</td>\n      <td>-107</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>1490430947385</td>\n      <td>WCDMA</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>3</td>\n      <td>-107</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>1490430947385</td>\n      <td>WCDMA</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>0</td>\n      <td>-113</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>1490430947385</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>20</td>\n      <td>-120</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>1490430947385</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>19</td>\n      <td>-121</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>1490430947385</td>\n      <td>LTE</td>\n      <td>0</td>\n      <td>2147483647</td>\n      <td>2147483647</td>\n      <td>234</td>\n      <td>10</td>\n      <td>19</td>\n      <td>-121</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>1490430953392</td>\n      <td>LTE</td>\n      <td>1</td>\n      <td>128000386</td>\n      <td>144</td>\n      <td>234</td>\n      <td>10</td>\n      <td>25</td>\n      <td>-115</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "cells_detail.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'1490430923343 53750757282 242658854 3 LTE 1 128000386 234 10 20 144 25 -115 2 LTE 0 2147483647 234 10 398 2147483647 22 -118 1 LTE 0 2147483647 234 10 4 2147483647 16 -124 1'"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'1490430947385 77792426028 242658854 13 LTE 1 128000386 234 10 20 144 20 -120 1 GSM 0 2147483647 2147483647 234 10 10 -93 3 GSM 0 2147483647 2147483647 234 10 6 -101 2 GSM 0 2147483647 2147483647 234 10 5 -103 2 GSM 0 2147483647 2147483647 234 10 2 -109 0 GSM 0 2147483647 2147483647 234 10 2 -109 0 GSM 0 2147483647 2147483647 234 10 2 -109 0 WCDMA 0 2147483647 2147483647 234 10 250 3 -107 1 WCDMA 0 2147483647 2147483647 234 10 14 3 -107 1 WCDMA 0 2147483647 2147483647 234 10 158 0 -113 0 LTE 0 2147483647 234 10 398 2147483647 20 -120 1 LTE 0 2147483647 234 10 4 2147483647 19 -121 1 LTE 0 2147483647 234 10 20 2147483647 19 -121 1'"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['1 128000386 234 10 20 144 25 -115 2',\n",
       " '0 2147483647 234 10 398 2147483647 22 -118 1',\n",
       " '0 2147483647 234 10 4 2147483647 16 -124 1']"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "re.split(\" LTE | GSM | WCDMA \", t)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['1 128000386 234 10 20 144 20 -120 1',\n",
       " '0 2147483647 2147483647 234 10 10 -93 3',\n",
       " '0 2147483647 2147483647 234 10 6 -101 2',\n",
       " '0 2147483647 2147483647 234 10 5 -103 2',\n",
       " '0 2147483647 2147483647 234 10 2 -109 0',\n",
       " '0 2147483647 2147483647 234 10 2 -109 0',\n",
       " '0 2147483647 2147483647 234 10 2 -109 0',\n",
       " '0 2147483647 2147483647 234 10 250 3 -107 1',\n",
       " '0 2147483647 2147483647 234 10 14 3 -107 1',\n",
       " '0 2147483647 2147483647 234 10 158 0 -113 0',\n",
       " '0 2147483647 234 10 398 2147483647 20 -120 1',\n",
       " '0 2147483647 234 10 4 2147483647 19 -121 1',\n",
       " '0 2147483647 234 10 20 2147483647 19 -121 1']"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "re.split(\" LTE | GSM | WCDMA \", t2)[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}