dataset-BerlinV2X-preprocess.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import correlation\n",
    "import arima\n",
    "import ewma\n",
    "\n",
    "plt.rcParams[\"figure.dpi\"] = 200\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"display.max_rows\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# More info at https://github.com/fraunhoferhhi/BerlinV2X\n",
    "# Please download the dataset from https://ieee-dataport.org/open-access/berlin-v2x\n",
    "sidelink='BerlinV2X/sidelink_dataframe.parquet'\n",
    "cellular='BerlinV2X/cellular_dataframe.parquet'\n",
    "df = pd.read_parquet(cellular)\n",
    "#df.head()\n",
    "#df.info(verbose=True, show_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_data = df.dropna(subset='datarate')\n",
    "filtered_data = filtered_data.query(\"measured_qos == 'datarate'\")\n",
    "filtered_data = filtered_data.drop(columns=[\n",
    "    'ping_ms', 'Traffic Jam Factor', 'Traffic Street Name', 'Traffic Distance', 'measurement', 'area', \n",
    "    'scenario', 'drive_mode', 'measured_qos', 'jitter', 'ts_gps', 'precipIntensity', 'precipProbability', \n",
    "    'temperature', 'apparentTemperature', 'dewPoint', 'humidity', 'pressure', 'windSpeed', 'cloudCover', 'uvIndex', \n",
    "    'visibility', 'target_datarate', 'PCell_MCC', 'PCell_MNC_Digit', 'PCell_MNC', 'PCell_Allowed_Access'])\n",
    "\n",
    "filtered_data['PCell_Downlink_bandwidth_MHz'] = pd.to_numeric(filtered_data['PCell_Downlink_bandwidth_MHz'])\n",
    "filtered_data['PCell_Uplink_bandwidth_MHz'] = pd.to_numeric(filtered_data['PCell_Uplink_bandwidth_MHz'])\n",
    "\n",
    "filtered_data_downlink = filtered_data.query(\"direction == 'downlink'\").drop(columns=['direction'])\n",
    "filtered_data_uplink = filtered_data.query(\"direction == 'uplink'\").drop(columns=['direction'])\n",
    "\n",
    "#filtered_data_downlink.info(verbose=True, show_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Operator1 pc1 19564 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:59:59+02:00\n",
    "#           pc4 9554 entries, 2021-06-22 09:49:54+02:00 to 2021-06-22 15:44:59+02:00\n",
    "# Operator2 pc2 8779 entries, 2021-06-22 09:49:54+02:00 to 2021-06-22 18:13:59+02:00\n",
    "#           pc3 14776 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:52:46+02:00\n",
    "#filtered_data = filtered_data.query(\"device == 'pc1'\").query(\"operator == 1\")\n",
    "#filtered_data = filtered_data.query(\"device == 'pc4'\").query(\"operator == 1\")\n",
    "#filtered_data = filtered_data.query(\"device == 'pc2'\").query(\"operator == 2\")\n",
    "#filtered_data = filtered_data.query(\"device == 'pc3'\").query(\"operator == 2\")\n",
    "\n",
    "filtered_data_downlink_pc1_op1 = filtered_data_downlink.query(\"device == 'pc1'\").query(\"operator == 1\")\n",
    "filtered_data_downlink_pc4_op1 = filtered_data_downlink.query(\"device == 'pc4'\").query(\"operator == 1\")\n",
    "filtered_data_downlink_pc2_op2 = filtered_data_downlink.query(\"device == 'pc2'\").query(\"operator == 2\")\n",
    "filtered_data_downlink_pc3_op2 = filtered_data_downlink.query(\"device == 'pc3'\").query(\"operator == 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "concat_filtered_data_downlink = pd.concat([filtered_data_downlink_pc1_op1, filtered_data_downlink_pc4_op1, filtered_data_downlink_pc2_op2, filtered_data_downlink_pc3_op2])\n",
    "#concat_filtered_data_downlink.info(verbose=True, show_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_plot = concat_filtered_data_downlink.copy()\n",
    "df_plot.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Calculate mean and standard deviation\n",
    "mean_value = df_plot['datarate'].div(1024*1024).mean()\n",
    "std_value = df_plot['datarate'].div(1024*1024).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 880x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure_size = (4.4, 4)\n",
    "fig, ax1 = plt.subplots(figsize=figure_size, dpi=200)\n",
    "ax1.set_xlabel('Time (Seconds)')\n",
    "ax1.set_ylabel('DL Bandwidth (Mbps)')\n",
    "ax1.set_ylim(bottom=0, top=400)\n",
    "\n",
    "left, bottom, width, height = [0.22, 0.67, 0.66, 0.19]\n",
    "ax2 = fig.add_axes([left, bottom, width, height])\n",
    "ax2.set_ylim(bottom=0, top=100)\n",
    "\n",
    "ax1.plot(df_plot['datarate'].div(1024*1024))\n",
    "\n",
    "alpha_val = 0.5\n",
    "# Plot mean and standard deviation lines on ax1\n",
    "ax1.axhline(mean_value, color='r', linestyle='--', alpha=alpha_val, label='Mean')\n",
    "ax1.axhline(mean_value + std_value, color='g', linestyle='--', alpha=alpha_val, label='+ Std Dev')\n",
    "ax1.axhline(mean_value - std_value, color='b', linestyle='--', alpha=alpha_val, label='- Std Dev')\n",
    "\n",
    "# Add legend\n",
    "ax1.legend(loc='lower left', bbox_to_anchor=(0.56, 0.4))\n",
    "\n",
    "ax2.plot(df_plot['datarate'].div(1024*1024)[:100])\n",
    "# Plot mean and standard deviation lines on ax2\n",
    "ax2.axhline(mean_value, color='r', linestyle='--', alpha=alpha_val, label='Mean')\n",
    "ax2.axhline(mean_value + std_value, color='g', linestyle='--', alpha=alpha_val, label='+ Std Dev')\n",
    "ax2.axhline(mean_value - std_value, color='b', linestyle='--', alpha=alpha_val, label='- Std Dev')\n",
    "fig.savefig(f'plots/BerlinV2X-data.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    52673.000000\n",
       "mean        47.458586\n",
       "std         37.380467\n",
       "min          0.000812\n",
       "25%         21.171570\n",
       "50%         37.193298\n",
       "75%         60.844421\n",
       "max        258.445740\n",
       "Name: datarate, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot['datarate'].div(1024*1024).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>device</th>\n",
       "      <th>datarate</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Altitude</th>\n",
       "      <th>speed_kmh</th>\n",
       "      <th>COG</th>\n",
       "      <th>Pos in Ref Round</th>\n",
       "      <th>PCell_RSRP_1</th>\n",
       "      <th>PCell_RSRP_2</th>\n",
       "      <th>PCell_RSRP_max</th>\n",
       "      <th>PCell_RSRQ_1</th>\n",
       "      <th>PCell_RSRQ_2</th>\n",
       "      <th>PCell_RSRQ_max</th>\n",
       "      <th>PCell_RSSI_1</th>\n",
       "      <th>PCell_RSSI_2</th>\n",
       "      <th>PCell_RSSI_max</th>\n",
       "      <th>PCell_SNR_1</th>\n",
       "      <th>PCell_SNR_2</th>\n",
       "      <th>PCell_E-ARFCN</th>\n",
       "      <th>PCell_Downlink_Num_RBs</th>\n",
       "      <th>PCell_Downlink_TB_Size</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_0</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_1</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_2</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_3</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_4</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_5</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_6</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_7</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_8</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_9</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_10</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_11</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_12</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_13</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_14</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_15</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_16</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_17</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_18</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_19</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_20</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_21</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_22</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_23</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_24</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_25</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_26</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_27</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_28</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_29</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_30</th>\n",
       "      <th>PCell_Downlink_RBs_MCS_31</th>\n",
       "      <th>PCell_Downlink_Average_MCS</th>\n",
       "      <th>PCell_Uplink_Num_RBs</th>\n",
       "      <th>PCell_Uplink_TB_Size</th>\n",
       "      <th>PCell_Uplink_Tx_Power_(dBm)</th>\n",
       "      <th>PCell_Cell_ID</th>\n",
       "      <th>PCell_Downlink_frequency</th>\n",
       "      <th>PCell_Uplink_frequency</th>\n",
       "      <th>PCell_Downlink_bandwidth_MHz</th>\n",
       "      <th>PCell_Uplink_bandwidth_MHz</th>\n",
       "      <th>PCell_Cell_Identity</th>\n",
       "      <th>PCell_TAC</th>\n",
       "      <th>PCell_Band_Indicator</th>\n",
       "      <th>PCell_freq_MHz</th>\n",
       "      <th>SCell_RSRP_1</th>\n",
       "      <th>SCell_RSRP_2</th>\n",
       "      <th>SCell_RSRP_max</th>\n",
       "      <th>SCell_RSRQ_1</th>\n",
       "      <th>SCell_RSRQ_2</th>\n",
       "      <th>SCell_RSRQ_max</th>\n",
       "      <th>SCell_RSSI_1</th>\n",
       "      <th>SCell_RSSI_2</th>\n",
       "      <th>SCell_RSSI_max</th>\n",
       "      <th>SCell_SNR_1</th>\n",
       "      <th>SCell_SNR_2</th>\n",
       "      <th>SCell_E-ARFCN</th>\n",
       "      <th>SCell_Downlink_Num_RBs</th>\n",
       "      <th>SCell_Downlink_TB_Size</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_0</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_1</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_2</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_3</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_4</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_5</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_6</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_7</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_8</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_9</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_10</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_11</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_12</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_13</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_14</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_15</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_16</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_17</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_18</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_19</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_20</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_21</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_22</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_23</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_24</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_25</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_26</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_27</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_28</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_29</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_30</th>\n",
       "      <th>SCell_Downlink_RBs_MCS_31</th>\n",
       "      <th>SCell_Downlink_Average_MCS</th>\n",
       "      <th>SCell_Uplink_Num_RBs</th>\n",
       "      <th>SCell_Uplink_TB_Size</th>\n",
       "      <th>SCell_Uplink_Tx_Power_(dBm)</th>\n",
       "      <th>SCell_Cell_ID</th>\n",
       "      <th>SCell_Downlink_frequency</th>\n",
       "      <th>SCell_Uplink_frequency</th>\n",
       "      <th>SCell_Downlink_bandwidth_MHz</th>\n",
       "      <th>SCell_Uplink_bandwidth_MHz</th>\n",
       "      <th>SCell_Cell_Identity</th>\n",
       "      <th>SCell_TAC</th>\n",
       "      <th>SCell_Band_Indicator</th>\n",
       "      <th>SCell_MCC</th>\n",
       "      <th>SCell_MNC_Digit</th>\n",
       "      <th>SCell_MNC</th>\n",
       "      <th>SCell_Allowed_Access</th>\n",
       "      <th>SCell_freq_MHz</th>\n",
       "      <th>operator</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-22 09:49:54+02:00</th>\n",
       "      <td>pc1</td>\n",
       "      <td>68700000.0</td>\n",
       "      <td>52.514013</td>\n",
       "      <td>13.335172</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.638750</td>\n",
       "      <td>-79.811875</td>\n",
       "      <td>-76.638750</td>\n",
       "      <td>-14.459375</td>\n",
       "      <td>-14.248750</td>\n",
       "      <td>-14.119375</td>\n",
       "      <td>-42.186875</td>\n",
       "      <td>-45.562500</td>\n",
       "      <td>-45.562500</td>\n",
       "      <td>19.255</td>\n",
       "      <td>22.090</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>29914.0</td>\n",
       "      <td>4381967.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>1475.0</td>\n",
       "      <td>2287.0</td>\n",
       "      <td>3245.5</td>\n",
       "      <td>1461.0</td>\n",
       "      <td>3801.0</td>\n",
       "      <td>6476.0</td>\n",
       "      <td>4757.5</td>\n",
       "      <td>4120.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1803.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>2267.0</td>\n",
       "      <td>111.224691</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33802242.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-84.699375</td>\n",
       "      <td>-73.418750</td>\n",
       "      <td>-73.418750</td>\n",
       "      <td>-9.640625</td>\n",
       "      <td>-10.627500</td>\n",
       "      <td>-9.631250</td>\n",
       "      <td>-55.057500</td>\n",
       "      <td>-42.787500</td>\n",
       "      <td>-42.787500</td>\n",
       "      <td>21.968</td>\n",
       "      <td>26.189</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>46392.0</td>\n",
       "      <td>6111069.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>376.0</td>\n",
       "      <td>3703.0</td>\n",
       "      <td>21673.0</td>\n",
       "      <td>13544.0</td>\n",
       "      <td>1524.0</td>\n",
       "      <td>3174.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2398.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>21050.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>33802248.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 09:49:55+02:00</th>\n",
       "      <td>pc1</td>\n",
       "      <td>80300000.0</td>\n",
       "      <td>52.514012</td>\n",
       "      <td>13.335173</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-77.602500</td>\n",
       "      <td>-79.874375</td>\n",
       "      <td>-77.602500</td>\n",
       "      <td>-15.041875</td>\n",
       "      <td>-14.629375</td>\n",
       "      <td>-14.550000</td>\n",
       "      <td>-42.562500</td>\n",
       "      <td>-45.241250</td>\n",
       "      <td>-45.241250</td>\n",
       "      <td>17.914</td>\n",
       "      <td>22.183</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>36796.0</td>\n",
       "      <td>5085821.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>1197.0</td>\n",
       "      <td>2699.0</td>\n",
       "      <td>2735.5</td>\n",
       "      <td>2308.0</td>\n",
       "      <td>2648.5</td>\n",
       "      <td>2515.0</td>\n",
       "      <td>3534.5</td>\n",
       "      <td>5898.0</td>\n",
       "      <td>5727.5</td>\n",
       "      <td>2974.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3539.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>449.0</td>\n",
       "      <td>2267.0</td>\n",
       "      <td>109.516432</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33802242.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-84.580625</td>\n",
       "      <td>-73.717500</td>\n",
       "      <td>-73.717500</td>\n",
       "      <td>-9.693125</td>\n",
       "      <td>-10.678750</td>\n",
       "      <td>-9.676250</td>\n",
       "      <td>-54.890000</td>\n",
       "      <td>-43.045000</td>\n",
       "      <td>-43.045000</td>\n",
       "      <td>22.368</td>\n",
       "      <td>26.416</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>48460.0</td>\n",
       "      <td>7083430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1766.0</td>\n",
       "      <td>3440.0</td>\n",
       "      <td>5124.0</td>\n",
       "      <td>12942.0</td>\n",
       "      <td>6982.0</td>\n",
       "      <td>5600.0</td>\n",
       "      <td>4230.0</td>\n",
       "      <td>5506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>21050.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>33802248.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 09:49:56+02:00</th>\n",
       "      <td>pc1</td>\n",
       "      <td>82600000.0</td>\n",
       "      <td>52.514010</td>\n",
       "      <td>13.335173</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-77.634375</td>\n",
       "      <td>-80.081875</td>\n",
       "      <td>-77.634375</td>\n",
       "      <td>-15.056250</td>\n",
       "      <td>-14.765625</td>\n",
       "      <td>-14.661875</td>\n",
       "      <td>-42.579375</td>\n",
       "      <td>-45.315625</td>\n",
       "      <td>-45.315625</td>\n",
       "      <td>17.939</td>\n",
       "      <td>22.364</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>27210.0</td>\n",
       "      <td>3651540.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>106.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>426.0</td>\n",
       "      <td>711.5</td>\n",
       "      <td>1393.0</td>\n",
       "      <td>1527.5</td>\n",
       "      <td>1904.0</td>\n",
       "      <td>1675.0</td>\n",
       "      <td>1513.5</td>\n",
       "      <td>1880.0</td>\n",
       "      <td>3008.5</td>\n",
       "      <td>4622.5</td>\n",
       "      <td>6559.5</td>\n",
       "      <td>398.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1417.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>433.0</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>109.905340</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33802242.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-84.423125</td>\n",
       "      <td>-73.757500</td>\n",
       "      <td>-73.757500</td>\n",
       "      <td>-9.560625</td>\n",
       "      <td>-10.595000</td>\n",
       "      <td>-9.527500</td>\n",
       "      <td>-54.863750</td>\n",
       "      <td>-43.163750</td>\n",
       "      <td>-43.163750</td>\n",
       "      <td>22.495</td>\n",
       "      <td>26.046</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>43548.0</td>\n",
       "      <td>7045455.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>210.0</td>\n",
       "      <td>668.0</td>\n",
       "      <td>8148.0</td>\n",
       "      <td>24526.0</td>\n",
       "      <td>4880.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3796.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>21050.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>33802248.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 09:49:57+02:00</th>\n",
       "      <td>pc1</td>\n",
       "      <td>79600000.0</td>\n",
       "      <td>52.514007</td>\n",
       "      <td>13.335173</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.256875</td>\n",
       "      <td>-79.810000</td>\n",
       "      <td>-76.256875</td>\n",
       "      <td>-14.679375</td>\n",
       "      <td>-14.410000</td>\n",
       "      <td>-14.321250</td>\n",
       "      <td>-41.573125</td>\n",
       "      <td>-45.401875</td>\n",
       "      <td>-45.401875</td>\n",
       "      <td>19.029</td>\n",
       "      <td>22.223</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>41625.0</td>\n",
       "      <td>6077291.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.0</td>\n",
       "      <td>302.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>2656.0</td>\n",
       "      <td>6469.5</td>\n",
       "      <td>5059.0</td>\n",
       "      <td>2796.0</td>\n",
       "      <td>3578.0</td>\n",
       "      <td>11656.0</td>\n",
       "      <td>4210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3727.5</td>\n",
       "      <td>26.0</td>\n",
       "      <td>434.0</td>\n",
       "      <td>1863.0</td>\n",
       "      <td>110.144231</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33802242.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-84.025625</td>\n",
       "      <td>-73.589375</td>\n",
       "      <td>-73.589375</td>\n",
       "      <td>-9.721875</td>\n",
       "      <td>-10.649375</td>\n",
       "      <td>-9.709375</td>\n",
       "      <td>-54.304375</td>\n",
       "      <td>-42.946875</td>\n",
       "      <td>-42.946875</td>\n",
       "      <td>22.936</td>\n",
       "      <td>26.435</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>48608.0</td>\n",
       "      <td>7226349.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>3946.0</td>\n",
       "      <td>10166.0</td>\n",
       "      <td>7936.0</td>\n",
       "      <td>9188.0</td>\n",
       "      <td>13124.0</td>\n",
       "      <td>842.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2678.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>21050.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>33802248.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 09:49:58+02:00</th>\n",
       "      <td>pc1</td>\n",
       "      <td>95700000.0</td>\n",
       "      <td>52.514005</td>\n",
       "      <td>13.335173</td>\n",
       "      <td>41.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-76.021875</td>\n",
       "      <td>-79.693750</td>\n",
       "      <td>-76.021875</td>\n",
       "      <td>-14.573125</td>\n",
       "      <td>-14.085625</td>\n",
       "      <td>-14.018750</td>\n",
       "      <td>-41.448750</td>\n",
       "      <td>-45.606250</td>\n",
       "      <td>-45.606250</td>\n",
       "      <td>19.359</td>\n",
       "      <td>22.872</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>40278.0</td>\n",
       "      <td>5670015.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>204.0</td>\n",
       "      <td>410.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>2954.5</td>\n",
       "      <td>2726.5</td>\n",
       "      <td>2527.5</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>2612.0</td>\n",
       "      <td>4968.5</td>\n",
       "      <td>9966.5</td>\n",
       "      <td>3487.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4093.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>436.0</td>\n",
       "      <td>2329.0</td>\n",
       "      <td>110.278450</td>\n",
       "      <td>246.0</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>33802242.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>-83.791250</td>\n",
       "      <td>-73.498125</td>\n",
       "      <td>-73.498125</td>\n",
       "      <td>-9.620000</td>\n",
       "      <td>-10.664375</td>\n",
       "      <td>-9.616875</td>\n",
       "      <td>-54.168750</td>\n",
       "      <td>-42.833125</td>\n",
       "      <td>-42.833125</td>\n",
       "      <td>23.189</td>\n",
       "      <td>26.118</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>46538.0</td>\n",
       "      <td>6526884.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244.0</td>\n",
       "      <td>4058.0</td>\n",
       "      <td>10054.0</td>\n",
       "      <td>10498.0</td>\n",
       "      <td>5618.0</td>\n",
       "      <td>6091.0</td>\n",
       "      <td>7151.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2824.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431.0</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>21050.0</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>33802248.0</td>\n",
       "      <td>1494.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          device    datarate   Latitude  Longitude  Altitude  \\\n",
       "timestamp                                                                      \n",
       "2021-06-22 09:49:54+02:00    pc1  68700000.0  52.514013  13.335172      41.9   \n",
       "2021-06-22 09:49:55+02:00    pc1  80300000.0  52.514012  13.335173      41.9   \n",
       "2021-06-22 09:49:56+02:00    pc1  82600000.0  52.514010  13.335173      41.9   \n",
       "2021-06-22 09:49:57+02:00    pc1  79600000.0  52.514007  13.335173      41.9   \n",
       "2021-06-22 09:49:58+02:00    pc1  95700000.0  52.514005  13.335173      41.8   \n",
       "\n",
       "                           speed_kmh  COG  Pos in Ref Round  PCell_RSRP_1  \\\n",
       "timestamp                                                                   \n",
       "2021-06-22 09:49:54+02:00        0.0  0.0               NaN    -76.638750   \n",
       "2021-06-22 09:49:55+02:00        0.0  0.0               NaN    -77.602500   \n",
       "2021-06-22 09:49:56+02:00        0.0  0.0               NaN    -77.634375   \n",
       "2021-06-22 09:49:57+02:00        0.0  0.0               NaN    -76.256875   \n",
       "2021-06-22 09:49:58+02:00        0.0  0.0               NaN    -76.021875   \n",
       "\n",
       "                           PCell_RSRP_2  PCell_RSRP_max  PCell_RSRQ_1  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00    -79.811875      -76.638750    -14.459375   \n",
       "2021-06-22 09:49:55+02:00    -79.874375      -77.602500    -15.041875   \n",
       "2021-06-22 09:49:56+02:00    -80.081875      -77.634375    -15.056250   \n",
       "2021-06-22 09:49:57+02:00    -79.810000      -76.256875    -14.679375   \n",
       "2021-06-22 09:49:58+02:00    -79.693750      -76.021875    -14.573125   \n",
       "\n",
       "                           PCell_RSRQ_2  PCell_RSRQ_max  PCell_RSSI_1  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00    -14.248750      -14.119375    -42.186875   \n",
       "2021-06-22 09:49:55+02:00    -14.629375      -14.550000    -42.562500   \n",
       "2021-06-22 09:49:56+02:00    -14.765625      -14.661875    -42.579375   \n",
       "2021-06-22 09:49:57+02:00    -14.410000      -14.321250    -41.573125   \n",
       "2021-06-22 09:49:58+02:00    -14.085625      -14.018750    -41.448750   \n",
       "\n",
       "                           PCell_RSSI_2  PCell_RSSI_max  PCell_SNR_1  \\\n",
       "timestamp                                                              \n",
       "2021-06-22 09:49:54+02:00    -45.562500      -45.562500       19.255   \n",
       "2021-06-22 09:49:55+02:00    -45.241250      -45.241250       17.914   \n",
       "2021-06-22 09:49:56+02:00    -45.315625      -45.315625       17.939   \n",
       "2021-06-22 09:49:57+02:00    -45.401875      -45.401875       19.029   \n",
       "2021-06-22 09:49:58+02:00    -45.606250      -45.606250       19.359   \n",
       "\n",
       "                           PCell_SNR_2  PCell_E-ARFCN  PCell_Downlink_Num_RBs  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00       22.090         1300.0                 29914.0   \n",
       "2021-06-22 09:49:55+02:00       22.183         1300.0                 36796.0   \n",
       "2021-06-22 09:49:56+02:00       22.364         1300.0                 27210.0   \n",
       "2021-06-22 09:49:57+02:00       22.223         1300.0                 41625.0   \n",
       "2021-06-22 09:49:58+02:00       22.872         1300.0                 40278.0   \n",
       "\n",
       "                           PCell_Downlink_TB_Size  PCell_Downlink_RBs_MCS_0  \\\n",
       "timestamp                                                                     \n",
       "2021-06-22 09:49:54+02:00               4381967.0                       NaN   \n",
       "2021-06-22 09:49:55+02:00               5085821.0                       NaN   \n",
       "2021-06-22 09:49:56+02:00               3651540.0                       NaN   \n",
       "2021-06-22 09:49:57+02:00               6077291.0                       NaN   \n",
       "2021-06-22 09:49:58+02:00               5670015.0                       3.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_1  PCell_Downlink_RBs_MCS_2  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                      28.0                       NaN   \n",
       "2021-06-22 09:49:55+02:00                      44.0                       NaN   \n",
       "2021-06-22 09:49:56+02:00                      68.0                       NaN   \n",
       "2021-06-22 09:49:57+02:00                      56.0                       NaN   \n",
       "2021-06-22 09:49:58+02:00                      16.0                       NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_3  PCell_Downlink_RBs_MCS_4  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       5.0                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_5  PCell_Downlink_RBs_MCS_6  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_7  PCell_Downlink_RBs_MCS_8  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_9  \\\n",
       "timestamp                                             \n",
       "2021-06-22 09:49:54+02:00                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_10  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_11  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_12  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_13  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_14  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                       32.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_15  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                      106.0   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                      110.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_16  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                       82.0   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_17  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                      196.0   \n",
       "2021-06-22 09:49:56+02:00                      426.0   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                      204.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_18  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                      642.0   \n",
       "2021-06-22 09:49:56+02:00                      711.5   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                      410.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_19  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                       68.0   \n",
       "2021-06-22 09:49:55+02:00                     1197.0   \n",
       "2021-06-22 09:49:56+02:00                     1393.0   \n",
       "2021-06-22 09:49:57+02:00                       61.0   \n",
       "2021-06-22 09:49:58+02:00                      999.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_20  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                      392.0   \n",
       "2021-06-22 09:49:55+02:00                     2699.0   \n",
       "2021-06-22 09:49:56+02:00                     1527.5   \n",
       "2021-06-22 09:49:57+02:00                      302.0   \n",
       "2021-06-22 09:49:58+02:00                     1670.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_21  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     1475.0   \n",
       "2021-06-22 09:49:55+02:00                     2735.5   \n",
       "2021-06-22 09:49:56+02:00                     1904.0   \n",
       "2021-06-22 09:49:57+02:00                     1054.0   \n",
       "2021-06-22 09:49:58+02:00                     2954.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_22  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     2287.0   \n",
       "2021-06-22 09:49:55+02:00                     2308.0   \n",
       "2021-06-22 09:49:56+02:00                     1675.0   \n",
       "2021-06-22 09:49:57+02:00                     2656.0   \n",
       "2021-06-22 09:49:58+02:00                     2726.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_23  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     3245.5   \n",
       "2021-06-22 09:49:55+02:00                     2648.5   \n",
       "2021-06-22 09:49:56+02:00                     1513.5   \n",
       "2021-06-22 09:49:57+02:00                     6469.5   \n",
       "2021-06-22 09:49:58+02:00                     2527.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_24  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     1461.0   \n",
       "2021-06-22 09:49:55+02:00                     2515.0   \n",
       "2021-06-22 09:49:56+02:00                     1880.0   \n",
       "2021-06-22 09:49:57+02:00                     5059.0   \n",
       "2021-06-22 09:49:58+02:00                     3498.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_25  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     3801.0   \n",
       "2021-06-22 09:49:55+02:00                     3534.5   \n",
       "2021-06-22 09:49:56+02:00                     3008.5   \n",
       "2021-06-22 09:49:57+02:00                     2796.0   \n",
       "2021-06-22 09:49:58+02:00                     2612.0   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_26  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     6476.0   \n",
       "2021-06-22 09:49:55+02:00                     5898.0   \n",
       "2021-06-22 09:49:56+02:00                     4622.5   \n",
       "2021-06-22 09:49:57+02:00                     3578.0   \n",
       "2021-06-22 09:49:58+02:00                     4968.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_27  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     4757.5   \n",
       "2021-06-22 09:49:55+02:00                     5727.5   \n",
       "2021-06-22 09:49:56+02:00                     6559.5   \n",
       "2021-06-22 09:49:57+02:00                    11656.0   \n",
       "2021-06-22 09:49:58+02:00                     9966.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_28  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     4120.0   \n",
       "2021-06-22 09:49:55+02:00                     2974.5   \n",
       "2021-06-22 09:49:56+02:00                      398.0   \n",
       "2021-06-22 09:49:57+02:00                     4210.0   \n",
       "2021-06-22 09:49:58+02:00                     3487.5   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_29  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_30  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                       50.0   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           PCell_Downlink_RBs_MCS_31  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     1803.0   \n",
       "2021-06-22 09:49:55+02:00                     3539.5   \n",
       "2021-06-22 09:49:56+02:00                     1417.0   \n",
       "2021-06-22 09:49:57+02:00                     3727.5   \n",
       "2021-06-22 09:49:58+02:00                     4093.0   \n",
       "\n",
       "                           PCell_Downlink_Average_MCS  PCell_Uplink_Num_RBs  \\\n",
       "timestamp                                                                     \n",
       "2021-06-22 09:49:54+02:00                        26.0                 429.0   \n",
       "2021-06-22 09:49:55+02:00                        25.0                 449.0   \n",
       "2021-06-22 09:49:56+02:00                        24.0                 433.0   \n",
       "2021-06-22 09:49:57+02:00                        26.0                 434.0   \n",
       "2021-06-22 09:49:58+02:00                        25.0                 436.0   \n",
       "\n",
       "                           PCell_Uplink_TB_Size  PCell_Uplink_Tx_Power_(dBm)  \\\n",
       "timestamp                                                                      \n",
       "2021-06-22 09:49:54+02:00                2267.0                   111.224691   \n",
       "2021-06-22 09:49:55+02:00                2267.0                   109.516432   \n",
       "2021-06-22 09:49:56+02:00                2154.0                   109.905340   \n",
       "2021-06-22 09:49:57+02:00                1863.0                   110.144231   \n",
       "2021-06-22 09:49:58+02:00                2329.0                   110.278450   \n",
       "\n",
       "                           PCell_Cell_ID  PCell_Downlink_frequency  \\\n",
       "timestamp                                                            \n",
       "2021-06-22 09:49:54+02:00          246.0                    1300.0   \n",
       "2021-06-22 09:49:55+02:00          246.0                    1300.0   \n",
       "2021-06-22 09:49:56+02:00          246.0                    1300.0   \n",
       "2021-06-22 09:49:57+02:00          246.0                    1300.0   \n",
       "2021-06-22 09:49:58+02:00          246.0                    1300.0   \n",
       "\n",
       "                           PCell_Uplink_frequency  \\\n",
       "timestamp                                           \n",
       "2021-06-22 09:49:54+02:00                 19300.0   \n",
       "2021-06-22 09:49:55+02:00                 19300.0   \n",
       "2021-06-22 09:49:56+02:00                 19300.0   \n",
       "2021-06-22 09:49:57+02:00                 19300.0   \n",
       "2021-06-22 09:49:58+02:00                 19300.0   \n",
       "\n",
       "                           PCell_Downlink_bandwidth_MHz  \\\n",
       "timestamp                                                 \n",
       "2021-06-22 09:49:54+02:00                          20.0   \n",
       "2021-06-22 09:49:55+02:00                          20.0   \n",
       "2021-06-22 09:49:56+02:00                          20.0   \n",
       "2021-06-22 09:49:57+02:00                          20.0   \n",
       "2021-06-22 09:49:58+02:00                          20.0   \n",
       "\n",
       "                           PCell_Uplink_bandwidth_MHz  PCell_Cell_Identity  \\\n",
       "timestamp                                                                    \n",
       "2021-06-22 09:49:54+02:00                        20.0           33802242.0   \n",
       "2021-06-22 09:49:55+02:00                        20.0           33802242.0   \n",
       "2021-06-22 09:49:56+02:00                        20.0           33802242.0   \n",
       "2021-06-22 09:49:57+02:00                        20.0           33802242.0   \n",
       "2021-06-22 09:49:58+02:00                        20.0           33802242.0   \n",
       "\n",
       "                           PCell_TAC  PCell_Band_Indicator  PCell_freq_MHz  \\\n",
       "timestamp                                                                    \n",
       "2021-06-22 09:49:54+02:00     1494.0                   3.0          1800.0   \n",
       "2021-06-22 09:49:55+02:00     1494.0                   3.0          1800.0   \n",
       "2021-06-22 09:49:56+02:00     1494.0                   3.0          1800.0   \n",
       "2021-06-22 09:49:57+02:00     1494.0                   3.0          1800.0   \n",
       "2021-06-22 09:49:58+02:00     1494.0                   3.0          1800.0   \n",
       "\n",
       "                           SCell_RSRP_1  SCell_RSRP_2  SCell_RSRP_max  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00    -84.699375    -73.418750      -73.418750   \n",
       "2021-06-22 09:49:55+02:00    -84.580625    -73.717500      -73.717500   \n",
       "2021-06-22 09:49:56+02:00    -84.423125    -73.757500      -73.757500   \n",
       "2021-06-22 09:49:57+02:00    -84.025625    -73.589375      -73.589375   \n",
       "2021-06-22 09:49:58+02:00    -83.791250    -73.498125      -73.498125   \n",
       "\n",
       "                           SCell_RSRQ_1  SCell_RSRQ_2  SCell_RSRQ_max  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00     -9.640625    -10.627500       -9.631250   \n",
       "2021-06-22 09:49:55+02:00     -9.693125    -10.678750       -9.676250   \n",
       "2021-06-22 09:49:56+02:00     -9.560625    -10.595000       -9.527500   \n",
       "2021-06-22 09:49:57+02:00     -9.721875    -10.649375       -9.709375   \n",
       "2021-06-22 09:49:58+02:00     -9.620000    -10.664375       -9.616875   \n",
       "\n",
       "                           SCell_RSSI_1  SCell_RSSI_2  SCell_RSSI_max  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00    -55.057500    -42.787500      -42.787500   \n",
       "2021-06-22 09:49:55+02:00    -54.890000    -43.045000      -43.045000   \n",
       "2021-06-22 09:49:56+02:00    -54.863750    -43.163750      -43.163750   \n",
       "2021-06-22 09:49:57+02:00    -54.304375    -42.946875      -42.946875   \n",
       "2021-06-22 09:49:58+02:00    -54.168750    -42.833125      -42.833125   \n",
       "\n",
       "                           SCell_SNR_1  SCell_SNR_2  SCell_E-ARFCN  \\\n",
       "timestamp                                                            \n",
       "2021-06-22 09:49:54+02:00       21.968       26.189         3050.0   \n",
       "2021-06-22 09:49:55+02:00       22.368       26.416         3050.0   \n",
       "2021-06-22 09:49:56+02:00       22.495       26.046         3050.0   \n",
       "2021-06-22 09:49:57+02:00       22.936       26.435         3050.0   \n",
       "2021-06-22 09:49:58+02:00       23.189       26.118         3050.0   \n",
       "\n",
       "                           SCell_Downlink_Num_RBs  SCell_Downlink_TB_Size  \\\n",
       "timestamp                                                                   \n",
       "2021-06-22 09:49:54+02:00                 46392.0               6111069.0   \n",
       "2021-06-22 09:49:55+02:00                 48460.0               7083430.0   \n",
       "2021-06-22 09:49:56+02:00                 43548.0               7045455.0   \n",
       "2021-06-22 09:49:57+02:00                 48608.0               7226349.0   \n",
       "2021-06-22 09:49:58+02:00                 46538.0               6526884.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_0  SCell_Downlink_RBs_MCS_1  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_2  SCell_Downlink_RBs_MCS_3  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_4  SCell_Downlink_RBs_MCS_5  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_6  SCell_Downlink_RBs_MCS_7  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_8  SCell_Downlink_RBs_MCS_9  \\\n",
       "timestamp                                                                       \n",
       "2021-06-22 09:49:54+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:55+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:56+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:57+02:00                       NaN                       NaN   \n",
       "2021-06-22 09:49:58+02:00                       NaN                       NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_10  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_11  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_12  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_13  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_14  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_15  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_16  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_17  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_18  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_19  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_20  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_21  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                      376.0   \n",
       "2021-06-22 09:49:55+02:00                     1766.0   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                       98.0   \n",
       "2021-06-22 09:49:58+02:00                      244.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_22  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     3703.0   \n",
       "2021-06-22 09:49:55+02:00                     3440.0   \n",
       "2021-06-22 09:49:56+02:00                     1320.0   \n",
       "2021-06-22 09:49:57+02:00                      630.0   \n",
       "2021-06-22 09:49:58+02:00                     4058.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_23  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                    21673.0   \n",
       "2021-06-22 09:49:55+02:00                     5124.0   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                     3946.0   \n",
       "2021-06-22 09:49:58+02:00                    10054.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_24  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                    13544.0   \n",
       "2021-06-22 09:49:55+02:00                    12942.0   \n",
       "2021-06-22 09:49:56+02:00                      210.0   \n",
       "2021-06-22 09:49:57+02:00                    10166.0   \n",
       "2021-06-22 09:49:58+02:00                    10498.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_25  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     1524.0   \n",
       "2021-06-22 09:49:55+02:00                     6982.0   \n",
       "2021-06-22 09:49:56+02:00                      668.0   \n",
       "2021-06-22 09:49:57+02:00                     7936.0   \n",
       "2021-06-22 09:49:58+02:00                     5618.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_26  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     3174.0   \n",
       "2021-06-22 09:49:55+02:00                     5600.0   \n",
       "2021-06-22 09:49:56+02:00                     8148.0   \n",
       "2021-06-22 09:49:57+02:00                     9188.0   \n",
       "2021-06-22 09:49:58+02:00                     6091.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_27  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                     4230.0   \n",
       "2021-06-22 09:49:56+02:00                    24526.0   \n",
       "2021-06-22 09:49:57+02:00                    13124.0   \n",
       "2021-06-22 09:49:58+02:00                     7151.0   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_28  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                     5506.0   \n",
       "2021-06-22 09:49:56+02:00                     4880.0   \n",
       "2021-06-22 09:49:57+02:00                      842.0   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_29  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_30  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                        NaN   \n",
       "2021-06-22 09:49:55+02:00                        NaN   \n",
       "2021-06-22 09:49:56+02:00                        NaN   \n",
       "2021-06-22 09:49:57+02:00                        NaN   \n",
       "2021-06-22 09:49:58+02:00                        NaN   \n",
       "\n",
       "                           SCell_Downlink_RBs_MCS_31  \\\n",
       "timestamp                                              \n",
       "2021-06-22 09:49:54+02:00                     2398.0   \n",
       "2021-06-22 09:49:55+02:00                     2870.0   \n",
       "2021-06-22 09:49:56+02:00                     3796.0   \n",
       "2021-06-22 09:49:57+02:00                     2678.0   \n",
       "2021-06-22 09:49:58+02:00                     2824.0   \n",
       "\n",
       "                           SCell_Downlink_Average_MCS  SCell_Uplink_Num_RBs  \\\n",
       "timestamp                                                                     \n",
       "2021-06-22 09:49:54+02:00                        24.0                   NaN   \n",
       "2021-06-22 09:49:55+02:00                        25.0                   NaN   \n",
       "2021-06-22 09:49:56+02:00                        27.0                   NaN   \n",
       "2021-06-22 09:49:57+02:00                        26.0                   NaN   \n",
       "2021-06-22 09:49:58+02:00                        25.0                   NaN   \n",
       "\n",
       "                           SCell_Uplink_TB_Size  SCell_Uplink_Tx_Power_(dBm)  \\\n",
       "timestamp                                                                      \n",
       "2021-06-22 09:49:54+02:00                   NaN                          NaN   \n",
       "2021-06-22 09:49:55+02:00                   NaN                          NaN   \n",
       "2021-06-22 09:49:56+02:00                   NaN                          NaN   \n",
       "2021-06-22 09:49:57+02:00                   NaN                          NaN   \n",
       "2021-06-22 09:49:58+02:00                   NaN                          NaN   \n",
       "\n",
       "                           SCell_Cell_ID  SCell_Downlink_frequency  \\\n",
       "timestamp                                                            \n",
       "2021-06-22 09:49:54+02:00          431.0                    3050.0   \n",
       "2021-06-22 09:49:55+02:00          431.0                    3050.0   \n",
       "2021-06-22 09:49:56+02:00          431.0                    3050.0   \n",
       "2021-06-22 09:49:57+02:00          431.0                    3050.0   \n",
       "2021-06-22 09:49:58+02:00          431.0                    3050.0   \n",
       "\n",
       "                           SCell_Uplink_frequency  \\\n",
       "timestamp                                           \n",
       "2021-06-22 09:49:54+02:00                 21050.0   \n",
       "2021-06-22 09:49:55+02:00                 21050.0   \n",
       "2021-06-22 09:49:56+02:00                 21050.0   \n",
       "2021-06-22 09:49:57+02:00                 21050.0   \n",
       "2021-06-22 09:49:58+02:00                 21050.0   \n",
       "\n",
       "                          SCell_Downlink_bandwidth_MHz  \\\n",
       "timestamp                                                \n",
       "2021-06-22 09:49:54+02:00                          20    \n",
       "2021-06-22 09:49:55+02:00                          20    \n",
       "2021-06-22 09:49:56+02:00                          20    \n",
       "2021-06-22 09:49:57+02:00                          20    \n",
       "2021-06-22 09:49:58+02:00                          20    \n",
       "\n",
       "                          SCell_Uplink_bandwidth_MHz  SCell_Cell_Identity  \\\n",
       "timestamp                                                                   \n",
       "2021-06-22 09:49:54+02:00                        20            33802248.0   \n",
       "2021-06-22 09:49:55+02:00                        20            33802248.0   \n",
       "2021-06-22 09:49:56+02:00                        20            33802248.0   \n",
       "2021-06-22 09:49:57+02:00                        20            33802248.0   \n",
       "2021-06-22 09:49:58+02:00                        20            33802248.0   \n",
       "\n",
       "                           SCell_TAC  SCell_Band_Indicator  SCell_MCC  \\\n",
       "timestamp                                                               \n",
       "2021-06-22 09:49:54+02:00     1494.0                   7.0      262.0   \n",
       "2021-06-22 09:49:55+02:00     1494.0                   7.0      262.0   \n",
       "2021-06-22 09:49:56+02:00     1494.0                   7.0      262.0   \n",
       "2021-06-22 09:49:57+02:00     1494.0                   7.0      262.0   \n",
       "2021-06-22 09:49:58+02:00     1494.0                   7.0      262.0   \n",
       "\n",
       "                           SCell_MNC_Digit  SCell_MNC  SCell_Allowed_Access  \\\n",
       "timestamp                                                                     \n",
       "2021-06-22 09:49:54+02:00              2.0        1.0                   0.0   \n",
       "2021-06-22 09:49:55+02:00              2.0        1.0                   0.0   \n",
       "2021-06-22 09:49:56+02:00              2.0        1.0                   0.0   \n",
       "2021-06-22 09:49:57+02:00              2.0        1.0                   0.0   \n",
       "2021-06-22 09:49:58+02:00              2.0        1.0                   0.0   \n",
       "\n",
       "                           SCell_freq_MHz  operator  \n",
       "timestamp                                            \n",
       "2021-06-22 09:49:54+02:00          2600.0         1  \n",
       "2021-06-22 09:49:55+02:00          2600.0         1  \n",
       "2021-06-22 09:49:56+02:00          2600.0         1  \n",
       "2021-06-22 09:49:57+02:00          2600.0         1  \n",
       "2021-06-22 09:49:58+02:00          2600.0         1  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_filtered_data_downlink.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 52673 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:52:46+02:00\n",
      "Columns: 131 entries, device to operator\n",
      "dtypes: float64(127), int64(1), object(3)\n",
      "memory usage: 53.0+ MB\n"
     ]
    }
   ],
   "source": [
    "concat_filtered_data_downlink.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 52673 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:52:46+02:00\n",
      "Data columns (total 17 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   datarate                    52673 non-null  float64\n",
      " 1   PCell_Downlink_TB_Size      52647 non-null  float64\n",
      " 2   SCell_Downlink_TB_Size      27231 non-null  float64\n",
      " 3   PCell_SNR_1                 52637 non-null  float64\n",
      " 4   PCell_SNR_2                 52637 non-null  float64\n",
      " 5   SCell_SNR_1                 28657 non-null  float64\n",
      " 6   SCell_SNR_2                 28657 non-null  float64\n",
      " 7   PCell_RSSI_1                52637 non-null  float64\n",
      " 8   PCell_RSSI_2                52637 non-null  float64\n",
      " 9   PCell_RSSI_max              52637 non-null  float64\n",
      " 10  PCell_RSRP_1                52637 non-null  float64\n",
      " 11  PCell_RSRP_2                52637 non-null  float64\n",
      " 12  PCell_RSRP_max              52637 non-null  float64\n",
      " 13  PCell_Downlink_Average_MCS  52647 non-null  float64\n",
      " 14  SCell_Downlink_Average_MCS  27231 non-null  float64\n",
      " 15  PCell_Downlink_Num_RBs      52647 non-null  float64\n",
      " 16  SCell_Downlink_Num_RBs      27231 non-null  float64\n",
      "dtypes: float64(17)\n",
      "memory usage: 7.2 MB\n"
     ]
    }
   ],
   "source": [
    "# Select features\n",
    "filtered_data_downlink_selected = concat_filtered_data_downlink[[\n",
    "    'datarate',\n",
    "    'PCell_Downlink_TB_Size', 'SCell_Downlink_TB_Size', \n",
    "    'PCell_SNR_1', 'PCell_SNR_2', 'SCell_SNR_1', 'SCell_SNR_2', \n",
    "    'PCell_RSSI_1', 'PCell_RSSI_2', 'PCell_RSSI_max',  \n",
    "    'PCell_RSRP_1', 'PCell_RSRP_2', 'PCell_RSRP_max',\n",
    "    'PCell_Downlink_Average_MCS', 'SCell_Downlink_Average_MCS', \n",
    "    'PCell_Downlink_Num_RBs', 'SCell_Downlink_Num_RBs']]\n",
    "\n",
    "filtered_data_downlink_selected.info(verbose=True, show_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_970320/3750080312.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data_downlink_selected['date'] = filtered_data_downlink_selected.index\n"
     ]
    }
   ],
   "source": [
    "filtered_data_downlink_selected['date'] = filtered_data_downlink_selected.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 52673 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:52:46+02:00\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype                        \n",
      "---  ------                      --------------  -----                        \n",
      " 0   datarate                    52673 non-null  float64                      \n",
      " 1   PCell_Downlink_TB_Size      52673 non-null  float64                      \n",
      " 2   SCell_Downlink_TB_Size      52673 non-null  float64                      \n",
      " 3   PCell_SNR_1                 52673 non-null  float64                      \n",
      " 4   PCell_SNR_2                 52673 non-null  float64                      \n",
      " 5   SCell_SNR_1                 52673 non-null  float64                      \n",
      " 6   SCell_SNR_2                 52673 non-null  float64                      \n",
      " 7   PCell_RSSI_1                52673 non-null  float64                      \n",
      " 8   PCell_RSSI_2                52673 non-null  float64                      \n",
      " 9   PCell_RSSI_max              52673 non-null  float64                      \n",
      " 10  PCell_RSRP_1                52673 non-null  float64                      \n",
      " 11  PCell_RSRP_2                52673 non-null  float64                      \n",
      " 12  PCell_RSRP_max              52673 non-null  float64                      \n",
      " 13  PCell_Downlink_Average_MCS  52673 non-null  float64                      \n",
      " 14  SCell_Downlink_Average_MCS  52673 non-null  float64                      \n",
      " 15  PCell_Downlink_Num_RBs      52673 non-null  float64                      \n",
      " 16  SCell_Downlink_Num_RBs      52673 non-null  float64                      \n",
      " 17  date                        52673 non-null  datetime64[us, Europe/Berlin]\n",
      "dtypes: datetime64[us, Europe/Berlin](1), float64(17)\n",
      "memory usage: 7.6 MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_970320/2017817406.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data_downlink_selected.ffill(limit=5, inplace=True)\n",
      "/tmp/ipykernel_970320/2017817406.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  filtered_data_downlink_selected.fillna(0, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "filtered_data_downlink_selected.ffill(limit=5, inplace=True)\n",
    "filtered_data_downlink_selected.fillna(0, inplace=True)\n",
    "filtered_data_downlink_selected.info(verbose=True, show_counts=True)"
   ]
  },
  {
   "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>datarate</th>\n",
       "      <th>PCell_Downlink_TB_Size</th>\n",
       "      <th>SCell_Downlink_TB_Size</th>\n",
       "      <th>PCell_SNR_1</th>\n",
       "      <th>PCell_SNR_2</th>\n",
       "      <th>SCell_SNR_1</th>\n",
       "      <th>SCell_SNR_2</th>\n",
       "      <th>PCell_RSSI_1</th>\n",
       "      <th>PCell_RSSI_2</th>\n",
       "      <th>PCell_RSSI_max</th>\n",
       "      <th>PCell_RSRP_1</th>\n",
       "      <th>PCell_RSRP_2</th>\n",
       "      <th>PCell_RSRP_max</th>\n",
       "      <th>PCell_Downlink_Average_MCS</th>\n",
       "      <th>SCell_Downlink_Average_MCS</th>\n",
       "      <th>PCell_Downlink_Num_RBs</th>\n",
       "      <th>SCell_Downlink_Num_RBs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.267300e+04</td>\n",
       "      <td>5.267300e+04</td>\n",
       "      <td>5.267300e+04</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "      <td>52673.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.976393e+07</td>\n",
       "      <td>4.524627e+06</td>\n",
       "      <td>2.617516e+06</td>\n",
       "      <td>10.744335</td>\n",
       "      <td>9.072081</td>\n",
       "      <td>5.986271</td>\n",
       "      <td>5.508088</td>\n",
       "      <td>-56.041132</td>\n",
       "      <td>-60.308330</td>\n",
       "      <td>-60.308330</td>\n",
       "      <td>-89.687946</td>\n",
       "      <td>-94.608625</td>\n",
       "      <td>-88.819027</td>\n",
       "      <td>19.865225</td>\n",
       "      <td>9.900537</td>\n",
       "      <td>61590.498567</td>\n",
       "      <td>35126.498358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.919626e+07</td>\n",
       "      <td>3.011763e+06</td>\n",
       "      <td>3.502870e+06</td>\n",
       "      <td>7.529651</td>\n",
       "      <td>8.938396</td>\n",
       "      <td>8.083984</td>\n",
       "      <td>8.363758</td>\n",
       "      <td>10.959077</td>\n",
       "      <td>15.602209</td>\n",
       "      <td>15.602209</td>\n",
       "      <td>11.737896</td>\n",
       "      <td>17.766489</td>\n",
       "      <td>11.905223</td>\n",
       "      <td>5.321538</td>\n",
       "      <td>9.807939</td>\n",
       "      <td>23607.251834</td>\n",
       "      <td>37313.230915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.510000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-15.603000</td>\n",
       "      <td>-16.289899</td>\n",
       "      <td>-17.950000</td>\n",
       "      <td>-18.250000</td>\n",
       "      <td>-92.683333</td>\n",
       "      <td>-96.388462</td>\n",
       "      <td>-96.388462</td>\n",
       "      <td>-174.689375</td>\n",
       "      <td>-175.605625</td>\n",
       "      <td>-175.605625</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.220000e+07</td>\n",
       "      <td>2.405036e+06</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.446000</td>\n",
       "      <td>3.602000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-63.305000</td>\n",
       "      <td>-67.619375</td>\n",
       "      <td>-67.619375</td>\n",
       "      <td>-97.376250</td>\n",
       "      <td>-101.821875</td>\n",
       "      <td>-96.683750</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42202.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.900000e+07</td>\n",
       "      <td>3.824019e+06</td>\n",
       "      <td>1.022330e+06</td>\n",
       "      <td>10.949000</td>\n",
       "      <td>9.715842</td>\n",
       "      <td>1.845000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-55.931875</td>\n",
       "      <td>-57.961250</td>\n",
       "      <td>-57.961250</td>\n",
       "      <td>-89.764375</td>\n",
       "      <td>-92.017500</td>\n",
       "      <td>-88.893125</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>54024.000000</td>\n",
       "      <td>32668.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.380000e+07</td>\n",
       "      <td>5.869808e+06</td>\n",
       "      <td>4.113265e+06</td>\n",
       "      <td>16.390000</td>\n",
       "      <td>15.810000</td>\n",
       "      <td>12.867000</td>\n",
       "      <td>12.918000</td>\n",
       "      <td>-48.853125</td>\n",
       "      <td>-49.593125</td>\n",
       "      <td>-49.593125</td>\n",
       "      <td>-82.306250</td>\n",
       "      <td>-83.096250</td>\n",
       "      <td>-81.147500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>86657.000000</td>\n",
       "      <td>71072.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.710000e+08</td>\n",
       "      <td>1.867910e+07</td>\n",
       "      <td>1.843587e+07</td>\n",
       "      <td>28.958000</td>\n",
       "      <td>28.564000</td>\n",
       "      <td>28.478000</td>\n",
       "      <td>28.404000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>100580.000000</td>\n",
       "      <td>99516.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           datarate  PCell_Downlink_TB_Size  SCell_Downlink_TB_Size  \\\n",
       "count  5.267300e+04            5.267300e+04            5.267300e+04   \n",
       "mean   4.976393e+07            4.524627e+06            2.617516e+06   \n",
       "std    3.919626e+07            3.011763e+06            3.502870e+06   \n",
       "min    8.510000e+02            0.000000e+00            0.000000e+00   \n",
       "25%    2.220000e+07            2.405036e+06            0.000000e+00   \n",
       "50%    3.900000e+07            3.824019e+06            1.022330e+06   \n",
       "75%    6.380000e+07            5.869808e+06            4.113265e+06   \n",
       "max    2.710000e+08            1.867910e+07            1.843587e+07   \n",
       "\n",
       "        PCell_SNR_1   PCell_SNR_2   SCell_SNR_1   SCell_SNR_2  PCell_RSSI_1  \\\n",
       "count  52673.000000  52673.000000  52673.000000  52673.000000  52673.000000   \n",
       "mean      10.744335      9.072081      5.986271      5.508088    -56.041132   \n",
       "std        7.529651      8.938396      8.083984      8.363758     10.959077   \n",
       "min      -15.603000    -16.289899    -17.950000    -18.250000    -92.683333   \n",
       "25%        5.446000      3.602000      0.000000      0.000000    -63.305000   \n",
       "50%       10.949000      9.715842      1.845000      0.000000    -55.931875   \n",
       "75%       16.390000     15.810000     12.867000     12.918000    -48.853125   \n",
       "max       28.958000     28.564000     28.478000     28.404000      0.000000   \n",
       "\n",
       "       PCell_RSSI_2  PCell_RSSI_max  PCell_RSRP_1  PCell_RSRP_2  \\\n",
       "count  52673.000000    52673.000000  52673.000000  52673.000000   \n",
       "mean     -60.308330      -60.308330    -89.687946    -94.608625   \n",
       "std       15.602209       15.602209     11.737896     17.766489   \n",
       "min      -96.388462      -96.388462   -174.689375   -175.605625   \n",
       "25%      -67.619375      -67.619375    -97.376250   -101.821875   \n",
       "50%      -57.961250      -57.961250    -89.764375    -92.017500   \n",
       "75%      -49.593125      -49.593125    -82.306250    -83.096250   \n",
       "max        0.000000        0.000000      0.000000      0.000000   \n",
       "\n",
       "       PCell_RSRP_max  PCell_Downlink_Average_MCS  SCell_Downlink_Average_MCS  \\\n",
       "count    52673.000000                52673.000000                52673.000000   \n",
       "mean       -88.819027                   19.865225                    9.900537   \n",
       "std         11.905223                    5.321538                    9.807939   \n",
       "min       -175.605625                    0.000000                    0.000000   \n",
       "25%        -96.683750                   16.000000                    0.000000   \n",
       "50%        -88.893125                   20.000000                   11.000000   \n",
       "75%        -81.147500                   24.000000                   19.000000   \n",
       "max          0.000000                   28.000000                   31.000000   \n",
       "\n",
       "       PCell_Downlink_Num_RBs  SCell_Downlink_Num_RBs  \n",
       "count            52673.000000            52673.000000  \n",
       "mean             61590.498567            35126.498358  \n",
       "std              23607.251834            37313.230915  \n",
       "min                  0.000000                0.000000  \n",
       "25%              42202.000000                0.000000  \n",
       "50%              54024.000000            32668.000000  \n",
       "75%              86657.000000            71072.000000  \n",
       "max             100580.000000            99516.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_data_downlink_selected.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 52673 entries, 2021-06-22 09:49:54+02:00 to 2021-06-24 18:52:46+02:00\n",
      "Data columns (total 18 columns):\n",
      " #   Column                      Non-Null Count  Dtype                        \n",
      "---  ------                      --------------  -----                        \n",
      " 0   datarate                    52673 non-null  float64                      \n",
      " 1   PCell_Downlink_TB_Size      52673 non-null  float64                      \n",
      " 2   SCell_Downlink_TB_Size      52673 non-null  float64                      \n",
      " 3   PCell_SNR_1                 52673 non-null  float64                      \n",
      " 4   PCell_SNR_2                 52673 non-null  float64                      \n",
      " 5   SCell_SNR_1                 52673 non-null  float64                      \n",
      " 6   SCell_SNR_2                 52673 non-null  float64                      \n",
      " 7   PCell_RSSI_1                52673 non-null  float64                      \n",
      " 8   PCell_RSSI_2                52673 non-null  float64                      \n",
      " 9   PCell_RSSI_max              52673 non-null  float64                      \n",
      " 10  PCell_RSRP_1                52673 non-null  float64                      \n",
      " 11  PCell_RSRP_2                52673 non-null  float64                      \n",
      " 12  PCell_RSRP_max              52673 non-null  float64                      \n",
      " 13  PCell_Downlink_Average_MCS  52673 non-null  float64                      \n",
      " 14  SCell_Downlink_Average_MCS  52673 non-null  float64                      \n",
      " 15  PCell_Downlink_Num_RBs      52673 non-null  float64                      \n",
      " 16  SCell_Downlink_Num_RBs      52673 non-null  float64                      \n",
      " 17  date                        52673 non-null  datetime64[us, Europe/Berlin]\n",
      "dtypes: datetime64[us, Europe/Berlin](1), float64(17)\n",
      "memory usage: 7.6 MB\n"
     ]
    }
   ],
   "source": [
    "directory = 'preprocessed-data/BerlinV2X'\n",
    "if not os.path.exists(directory):\n",
    "    os.makedirs(directory)\n",
    "\n",
    "filtered_data_downlink_selected.info(verbose=True, show_counts=True)\n",
    "filtered_data_downlink_selected.to_csv('preprocessed-data/BerlinV2X/filtered_data_downlink.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filtered_data_downlink_selected.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 4000x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pearson, spearman, kendall = correlation.multi(filtered_data_downlink_selected, 'datarate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>pearson</th>\n",
       "      <th>spearman</th>\n",
       "      <th>kendall</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>datarate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCell_Downlink_TB_Size</th>\n",
       "      <td>0.847742</td>\n",
       "      <td>0.713194</td>\n",
       "      <td>0.573318</td>\n",
       "      <td>0.711418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_Downlink_TB_Size</th>\n",
       "      <td>0.797109</td>\n",
       "      <td>0.791373</td>\n",
       "      <td>0.610053</td>\n",
       "      <td>0.732845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCell_SNR_1</th>\n",
       "      <td>0.685647</td>\n",
       "      <td>0.678052</td>\n",
       "      <td>0.515107</td>\n",
       "      <td>0.626269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCell_SNR_2</th>\n",
       "      <td>0.674484</td>\n",
       "      <td>0.646673</td>\n",
       "      <td>0.482764</td>\n",
       "      <td>0.601307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCell_Downlink_Average_MCS</th>\n",
       "      <td>0.634042</td>\n",
       "      <td>0.651000</td>\n",
       "      <td>0.500547</td>\n",
       "      <td>0.595196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SCell_Downlink_Num_RBs</th>\n",
       "      <td>0.629578</td>\n",
       "      <td>0.623857</td>\n",
       "      <td>0.464920</td>\n",
       "      <td>0.572785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_SNR_2</th>\n",
       "      <td>0.581212</td>\n",
       "      <td>0.655686</td>\n",
       "      <td>0.465526</td>\n",
       "      <td>0.567475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_SNR_1</th>\n",
       "      <td>0.528634</td>\n",
       "      <td>0.590892</td>\n",
       "      <td>0.413328</td>\n",
       "      <td>0.510951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSRP_max</th>\n",
       "      <td>0.508006</td>\n",
       "      <td>0.596185</td>\n",
       "      <td>0.422238</td>\n",
       "      <td>0.508809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSSI_2</th>\n",
       "      <td>0.496905</td>\n",
       "      <td>0.564189</td>\n",
       "      <td>0.393367</td>\n",
       "      <td>0.484820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSSI_max</th>\n",
       "      <td>0.496905</td>\n",
       "      <td>0.564189</td>\n",
       "      <td>0.393367</td>\n",
       "      <td>0.484820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSSI_1</th>\n",
       "      <td>0.496791</td>\n",
       "      <td>0.565631</td>\n",
       "      <td>0.395668</td>\n",
       "      <td>0.486030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSRP_1</th>\n",
       "      <td>0.489428</td>\n",
       "      <td>0.582648</td>\n",
       "      <td>0.410207</td>\n",
       "      <td>0.494094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_RSRP_2</th>\n",
       "      <td>0.484452</td>\n",
       "      <td>0.577175</td>\n",
       "      <td>0.403341</td>\n",
       "      <td>0.488323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_Downlink_Average_MCS</th>\n",
       "      <td>0.427827</td>\n",
       "      <td>0.476029</td>\n",
       "      <td>0.338171</td>\n",
       "      <td>0.414009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCell_Downlink_Num_RBs</th>\n",
       "      <td>0.376499</td>\n",
       "      <td>0.328906</td>\n",
       "      <td>0.220545</td>\n",
       "      <td>0.308650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>0.245500</td>\n",
       "      <td>0.047498</td>\n",
       "      <td>0.027626</td>\n",
       "      <td>0.106874</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             pearson  spearman   kendall   average\n",
       "datarate                    1.000000  1.000000  1.000000  1.000000\n",
       "SCell_Downlink_TB_Size      0.847742  0.713194  0.573318  0.711418\n",
       "PCell_Downlink_TB_Size      0.797109  0.791373  0.610053  0.732845\n",
       "SCell_SNR_1                 0.685647  0.678052  0.515107  0.626269\n",
       "SCell_SNR_2                 0.674484  0.646673  0.482764  0.601307\n",
       "SCell_Downlink_Average_MCS  0.634042  0.651000  0.500547  0.595196\n",
       "SCell_Downlink_Num_RBs      0.629578  0.623857  0.464920  0.572785\n",
       "PCell_SNR_2                 0.581212  0.655686  0.465526  0.567475\n",
       "PCell_SNR_1                 0.528634  0.590892  0.413328  0.510951\n",
       "PCell_RSRP_max              0.508006  0.596185  0.422238  0.508809\n",
       "PCell_RSSI_2                0.496905  0.564189  0.393367  0.484820\n",
       "PCell_RSSI_max              0.496905  0.564189  0.393367  0.484820\n",
       "PCell_RSSI_1                0.496791  0.565631  0.395668  0.486030\n",
       "PCell_RSRP_1                0.489428  0.582648  0.410207  0.494094\n",
       "PCell_RSRP_2                0.484452  0.577175  0.403341  0.488323\n",
       "PCell_Downlink_Average_MCS  0.427827  0.476029  0.338171  0.414009\n",
       "PCell_Downlink_Num_RBs      0.376499  0.328906  0.220545  0.308650\n",
       "date                        0.245500  0.047498  0.027626  0.106874"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr = pearson\n",
    "df_corr['pearson'] = pearson\n",
    "df_corr['spearman'] = spearman\n",
    "df_corr['kendall'] = kendall\n",
    "df_corr.drop(columns=['datarate'], inplace=True)\n",
    "\n",
    "df_corr['average'] = df_corr.mean(axis=1)\n",
    "df_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EWMA8 rmse: 8887482.695764722, mae: 6056226.595529158\n",
      "Shifted rmse: 7556218.80730079, mae: 5022861.567695962\n"
     ]
    }
   ],
   "source": [
    "# EWMA\n",
    "\n",
    "# scope is duration of test dataset (%20 of whole data)\n",
    "scope = int(len(filtered_data_downlink_selected) * 0.2) #10534\n",
    "tail = filtered_data_downlink_selected.tail(scope)\n",
    "tail = tail[['datarate']]\n",
    "\n",
    "span = 8\n",
    "pred_len = 1\n",
    "\n",
    "ewma.predict_and_evaluate(tail, 'datarate', span, pred_len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
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       "\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>datarate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-06-22 16:41:48+02:00</th>\n",
       "      <td>59600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 16:41:49+02:00</th>\n",
       "      <td>51900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 16:41:50+02:00</th>\n",
       "      <td>39400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 16:41:51+02:00</th>\n",
       "      <td>40600000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22 16:41:52+02:00</th>\n",
       "      <td>39400000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             datarate\n",
       "timestamp                            \n",
       "2021-06-22 16:41:48+02:00  59600000.0\n",
       "2021-06-22 16:41:49+02:00  51900000.0\n",
       "2021-06-22 16:41:50+02:00  39400000.0\n",
       "2021-06-22 16:41:51+02:00  40600000.0\n",
       "2021-06-22 16:41:52+02:00  39400000.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ARIMA\n",
    "\n",
    "# lenght of last %20 + window size\n",
    "scope = 10534 + 32\n",
    "df_arima = filtered_data_downlink_selected.tail(scope)\n",
    "df_arima = df_arima[['datarate']]\n",
    "df_arima.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the rolling window size and prediction horizon\n",
    "window_size = 32\n",
    "prediction_horizon = 1\n",
    "\n",
    "# Predict the next value using ARIMA model with a rolling window\n",
    "predictions = arima.predict_next_value(df_arima['datarate'], window_size=window_size, horizon=prediction_horizon)\n",
    "\n",
    "arima.evaluate(predictions, df_arima, window_size, prediction_horizon)"
   ]
  }
 ],
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