exploration_template-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# How COVID has influenced Bike use in the Bay Area\n",
    "## by Grant Glass\n",
    "\n",
    "## Preliminary Wrangling\n",
    "\n",
    "> Bay Wheels is a regional public bicycle sharing system in the San Francisco Bay Area, California operated by Motivate in a partnership with the Metropolitan Transportation Commission and the Bay Area Air Quality Management District. Bay Wheels is the first regional and large-scale bicycle sharing system deployed in California and on the West Coast of the United States. It was established as Bay Area Bike Share in August 2013. As of January 2018, the Bay Wheels system had over 2,600 bicycles in 262 stations across San Francisco, East Bay and San Jose. On June 28, 2017, the system officially re-launched as Ford GoBike in a partnership with Ford Motor Company. After Motivate's acquisition by Lyft, the system was subsequently renamed to Bay Wheels in June 2019. The system is expected to expand to 7,000 bicycles around 540 stations in San Francisco, Oakland, Berkeley, Emeryville, and San Jose.\n",
    "\n",
    "\n",
    ">On Mar 17, 2020 San Francisco enacted a shelter in place, which drastically reduced car traffic in the bay area. I am going to investigate whether or not this caused an increase in ridership.\n",
    "\n",
    "\n",
    ">This data set includes information about individual rides made in a bike-sharing system covering the greater San Francisco Bay area. I will merge both January, Februrary, and March's data together. The dataset includes the following features : Trip Duration in seconds , Start Time and Date , End Time and Date , Start Station ID , Start Station Name , Start Station Latitude , Start Station Longitude , End Station ID , End Station Name, End Station Latitude , End Station Longitude , Bike ID , User Type , and  Rental Access Method. \n",
    "\n",
    "Pulling the data down and looking at it on Excel shows us that\n",
    "\n",
    "January's Data: 295855 trips\n",
    "February's Data: 432355 trips\n",
    "March's Data: 176800 trips\n",
    "\n",
    "We see that there is a clear downturn in March.\n",
    "But what can the data tell us about the behaviors of the users of Bay Wheels?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib as mat\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style \n",
    "import seaborn as sb\n",
    "import os\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Load in your dataset and describe its properties through the questions below.\n",
    "Try and motivate your exploration goals through this section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83118</td>\n",
       "      <td>2020-01-31 15:23:47.7330</td>\n",
       "      <td>2020-02-01 14:29:06.2630</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>13052</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68239</td>\n",
       "      <td>2020-01-31 15:40:31.6160</td>\n",
       "      <td>2020-02-01 10:37:51.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>12235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55407</td>\n",
       "      <td>2020-01-31 17:48:16.6350</td>\n",
       "      <td>2020-02-01 09:11:44.3170</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>12822</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54929</td>\n",
       "      <td>2020-01-31 17:53:03.4130</td>\n",
       "      <td>2020-02-01 09:08:32.6340</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>11705</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55700</td>\n",
       "      <td>2020-01-31 17:12:33.4600</td>\n",
       "      <td>2020-02-01 08:40:53.6460</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Lombard St at Columbus Ave</td>\n",
       "      <td>37.802746</td>\n",
       "      <td>-122.413579</td>\n",
       "      <td>3673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   duration_sec                start_time                  end_time  \\\n",
       "0         83118  2020-01-31 15:23:47.7330  2020-02-01 14:29:06.2630   \n",
       "1         68239  2020-01-31 15:40:31.6160  2020-02-01 10:37:51.0000   \n",
       "2         55407  2020-01-31 17:48:16.6350  2020-02-01 09:11:44.3170   \n",
       "3         54929  2020-01-31 17:53:03.4130  2020-02-01 09:08:32.6340   \n",
       "4         55700  2020-01-31 17:12:33.4600  2020-02-01 08:40:53.6460   \n",
       "\n",
       "   start_station_id             start_station_name  start_station_latitude  \\\n",
       "0             400.0  Buchanan St at North Point St               37.804272   \n",
       "1              99.0           Folsom St at 15th St               37.767037   \n",
       "2             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "3             197.0    El Embarcadero at Grand Ave               37.808848   \n",
       "4              12.0    Pier 1/2 at The Embarcadero               37.796389   \n",
       "\n",
       "   start_station_longitude  end_station_id               end_station_name  \\\n",
       "0              -122.433537           400.0  Buchanan St at North Point St   \n",
       "1              -122.415443            99.0           Folsom St at 15th St   \n",
       "2              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "3              -122.249680           197.0    El Embarcadero at Grand Ave   \n",
       "4              -122.394586           371.0     Lombard St at Columbus Ave   \n",
       "\n",
       "   end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "0             37.804272            -122.433537    13052    Customer   \n",
       "1             37.767037            -122.415443    12235  Subscriber   \n",
       "2             37.808848            -122.249680    12822    Customer   \n",
       "3             37.808848            -122.249680    11705    Customer   \n",
       "4             37.802746            -122.413579     3673    Customer   \n",
       "\n",
       "  rental_access_method  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge all the files together\n",
    "currentDir = os.getcwd()\n",
    "path = currentDir+'/data/'\n",
    "text_files = os.listdir(path)\n",
    "\n",
    "# loop to get all files together\n",
    "file_list = []\n",
    "for text_file in text_files:\n",
    "    filename = path+text_file\n",
    "    df = pd.read_csv(filename)\n",
    "    file_list.append(df)\n",
    "df_merge = pd.concat(file_list) \n",
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error seems to be caused by the blank in the 13th column. We will address that in cleanup. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
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       "      <th>end_time</th>\n",
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       "      <th>start_station_name</th>\n",
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       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
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       "      <th>176794</th>\n",
       "      <td>61</td>\n",
       "      <td>2020-04-01 10:42:35</td>\n",
       "      <td>2020-04-01 10:43:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848493</td>\n",
       "      <td>-122.265797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848476</td>\n",
       "      <td>-122.265779</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176795</th>\n",
       "      <td>78</td>\n",
       "      <td>2020-03-18 17:10:42</td>\n",
       "      <td>2020-03-18 17:12:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848499</td>\n",
       "      <td>-122.265794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848477</td>\n",
       "      <td>-122.265815</td>\n",
       "      <td>999960</td>\n",
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       "      <td>app</td>\n",
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       "    <tr>\n",
       "      <th>176796</th>\n",
       "      <td>122</td>\n",
       "      <td>2020-04-01 13:32:28</td>\n",
       "      <td>2020-04-01 13:34:31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848507</td>\n",
       "      <td>-122.266010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848485</td>\n",
       "      <td>-122.266063</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
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       "    <tr>\n",
       "      <th>176797</th>\n",
       "      <td>340</td>\n",
       "      <td>2020-04-01 13:07:34</td>\n",
       "      <td>2020-04-01 13:13:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848530</td>\n",
       "      <td>-122.266013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266029</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176798</th>\n",
       "      <td>333</td>\n",
       "      <td>2020-03-23 16:39:57</td>\n",
       "      <td>2020-03-23 16:45:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848625</td>\n",
       "      <td>-122.266660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848446</td>\n",
       "      <td>-122.266022</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec           start_time             end_time  \\\n",
       "176794            61  2020-04-01 10:42:35  2020-04-01 10:43:37   \n",
       "176795            78  2020-03-18 17:10:42  2020-03-18 17:12:01   \n",
       "176796           122  2020-04-01 13:32:28  2020-04-01 13:34:31   \n",
       "176797           340  2020-04-01 13:07:34  2020-04-01 13:13:14   \n",
       "176798           333  2020-03-23 16:39:57  2020-03-23 16:45:30   \n",
       "\n",
       "        start_station_id start_station_name  start_station_latitude  \\\n",
       "176794               NaN                NaN               37.848493   \n",
       "176795               NaN                NaN               37.848499   \n",
       "176796               NaN                NaN               37.848507   \n",
       "176797               NaN                NaN               37.848530   \n",
       "176798               NaN                NaN               37.848625   \n",
       "\n",
       "        start_station_longitude  end_station_id end_station_name  \\\n",
       "176794              -122.265797             NaN              NaN   \n",
       "176795              -122.265794             NaN              NaN   \n",
       "176796              -122.266010             NaN              NaN   \n",
       "176797              -122.266013             NaN              NaN   \n",
       "176798              -122.266660             NaN              NaN   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "176794             37.848476            -122.265779   999960  Subscriber   \n",
       "176795             37.848477            -122.265815   999960  Subscriber   \n",
       "176796             37.848485            -122.266063   633095  Subscriber   \n",
       "176797             37.848497            -122.266029   633095  Subscriber   \n",
       "176798             37.848446            -122.266022   633095  Subscriber   \n",
       "\n",
       "       rental_access_method  \n",
       "176794                  app  \n",
       "176795                  app  \n",
       "176796                  app  \n",
       "176797                  app  \n",
       "176798                  app  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 905007 entries, 0 to 176798\n",
      "Data columns (total 14 columns):\n",
      "duration_sec               905007 non-null int64\n",
      "start_time                 905007 non-null object\n",
      "end_time                   905007 non-null object\n",
      "start_station_id           418705 non-null float64\n",
      "start_station_name         419938 non-null object\n",
      "start_station_latitude     905007 non-null float64\n",
      "start_station_longitude    905007 non-null float64\n",
      "end_station_id             419225 non-null float64\n",
      "end_station_name           420499 non-null object\n",
      "end_station_latitude       905007 non-null float64\n",
      "end_station_longitude      905007 non-null float64\n",
      "bike_id                    905007 non-null int64\n",
      "user_type                  905007 non-null object\n",
      "rental_access_method       617858 non-null object\n",
      "dtypes: float64(6), int64(2), object(6)\n",
      "memory usage: 103.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_merge.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                 int64\n",
       "start_time                  object\n",
       "end_time                    object\n",
       "start_station_id           float64\n",
       "start_station_name          object\n",
       "start_station_latitude     float64\n",
       "start_station_longitude    float64\n",
       "end_station_id             float64\n",
       "end_station_name            object\n",
       "end_station_latitude       float64\n",
       "end_station_longitude      float64\n",
       "bike_id                      int64\n",
       "user_type                   object\n",
       "rental_access_method        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the structure of your dataset?\n",
    "\n",
    "> The dataset is the result of merging 3 files(January, February, March (2020)). It has 14 features and 905,007 rows. \n",
    "\n",
    "### What is/are the main feature(s) of interest in your dataset?\n",
    "\n",
    "> Did Covid19 influence ridership? Are people biking for longer?\n",
    "\n",
    "### What features in the dataset do you think will help support your investigation into your feature(s) of interest?\n",
    "\n",
    "> Trip Duration, Start Time and Date , End Time and Date."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "> In this section, investigate distributions of individual variables. If\n",
    "you see unusual points or outliers, take a deeper look to clean things up\n",
    "and prepare yourself to look at relationships between variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Duration in seconds')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cuFA5OTnKzMyUJJ08eVJ5eXnuICNJCQkJKi8v14YNG3T9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\nhGiU/g9c1B6hRHiscQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23f9a0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at the average trip length\n",
    "style.use('seaborn-poster') #sets the size of the charts\n",
    "style.use('ggplot')\n",
    "bin_edges = np.arange(0, df_merge['duration_sec'].max() + 200 , 200)\n",
    "plt.hist(data = df_merge , x = 'duration_sec' , normed=True, bins = bin_edges)\n",
    "plt.xlim(0,3000)\n",
    "plt.title('Trip Durations Distribution')\n",
    "plt.xlabel('Duration in seconds')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a37067b90>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tTfv379cnn3yiGTNmaMWKFaqsrIxY8+LFi3rrrbfk8/mU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\nW5Ao3wYmPAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abb87e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(df_merge.duration_sec.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "806.7834304044057"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.duration_sec.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph is a little hard to look at, maybe minutes could create a better looking graph?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.016667      1152\n",
       "7.866667      1138\n",
       "6.783333      1137\n",
       "6.166667      1129\n",
       "6.566667      1128\n",
       "5.916667      1123\n",
       "5.600000      1121\n",
       "6.416667      1121\n",
       "6.733333      1118\n",
       "5.933333      1115\n",
       "6.266667      1113\n",
       "6.350000      1112\n",
       "6.183333      1112\n",
       "5.733333      1112\n",
       "6.050000      1108\n",
       "5.583333      1108\n",
       "6.150000      1106\n",
       "5.883333      1105\n",
       "6.366667      1104\n",
       "7.266667      1103\n",
       "6.083333      1103\n",
       "5.900000      1103\n",
       "6.766667      1103\n",
       "5.950000      1102\n",
       "6.433333      1101\n",
       "5.800000      1101\n",
       "6.300000      1100\n",
       "6.833333      1096\n",
       "6.516667      1096\n",
       "6.900000      1094\n",
       "              ... \n",
       "125.200000       1\n",
       "99.550000        1\n",
       "99.950000        1\n",
       "238.950000       1\n",
       "80.883333        1\n",
       "126.850000       1\n",
       "285.400000       1\n",
       "75.300000        1\n",
       "147.083333       1\n",
       "207.733333       1\n",
       "95.650000        1\n",
       "223.816667       1\n",
       "130.850000       1\n",
       "184.450000       1\n",
       "185.550000       1\n",
       "143.816667       1\n",
       "142.183333       1\n",
       "95.183333        1\n",
       "130.150000       1\n",
       "136.616667       1\n",
       "212.216667       1\n",
       "148.833333       1\n",
       "331.816667       1\n",
       "160.016667       1\n",
       "894.483333       1\n",
       "84.250000        1\n",
       "177.966667       1\n",
       "111.266667       1\n",
       "928.333333       1\n",
       "93.083333        1\n",
       "Name: duration_min, Length: 8742, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge['duration_min'] = df_merge['duration_sec'] / 60\n",
    "df_merge['duration_min'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t8GhkJjg4WP/4xz+MEYoPP/xQwcHBX/rkDfNYrg4rDofjcufatZPL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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ad29d64d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_edges = 10**np.arange(0 , 3+0.1 , 0.1)\n",
    "ticks =  [ 0.1 , 0.3 , 1 , 3, 10, 30, 100, 300]\n",
    "labels = ['{}'.format(v) for v in ticks]\n",
    "plt.hist(data = df_merge , x = 'duration_min' , bins = bin_edges);\n",
    "plt.xticks(ticks,labels);\n",
    "plt.xlabel('Duration in min');\n",
    "plt.xscale('log');\n",
    "plt.title('Trip Duration Distribution')\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.44639050674056"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.duration_min.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks much better. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?\n",
    "\n",
    "> I was interested in time, to see if any changes have occured since COVID19. So it makes sense to see what the distributions of the trips were. The seconds visulaization was hard to read and the mean didn't look quite right. As in the distribution was too diverse. I did some transformations by creating a minute duration instead of the second. \n",
    "\n",
    "### Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?\n",
    "\n",
    "> After look at the second, it looks like 1-2 super riders have gone for very long trips, which is influencing the distrobution. I then tried to look at minutes instead of seconds to try to level the scale a little more. This ended up producing a much better looking distribution and mean. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lots of short and very long trips. Not many inbetween. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Exploration\n",
    "\n",
    "> In this section, investigate relationships between pairs of variables in your\n",
    "data. Make sure the variables that you cover here have been introduced in some\n",
    "fashion in the previous section (univariate exploration)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at time. First we need to extract the date data from the start_time column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merge['start_time'] = pd.to_datetime(df_merge['start_time'])\n",
    "df_merge['start_year'] = df_merge['start_time'].dt.year\n",
    "df_merge['start_month'] = df_merge['start_time'].dt.month\n",
    "df_merge['start_day'] = df_merge['start_time'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                           object\n",
       "start_station_id                  float64\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                    float64\n",
       "end_station_name                   object\n",
       "end_station_latitude              float64\n",
       "end_station_longitude             float64\n",
       "bike_id                             int64\n",
       "user_type                          object\n",
       "rental_access_method               object\n",
       "duration_min                      float64\n",
       "start_year                          int64\n",
       "start_month                         int64\n",
       "start_day                           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print out csv to move to third party GIS software\n",
    "df_merge.to_csv('baywheels_covid_gis.csv', encoding='utf-8', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#create month/day column\n",
    "df_merge['timing'] = df_merge['start_time'].dt.strftime('%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>210892</th>\n",
       "      <td>3251</td>\n",
       "      <td>2020-02-03 08:00:27</td>\n",
       "      <td>2020-02-03 08:54:39</td>\n",
       "      <td>101.0</td>\n",
       "      <td>15th St at Potrero Ave</td>\n",
       "      <td>37.767079</td>\n",
       "      <td>-122.407359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.795254</td>\n",
       "      <td>-122.394187</td>\n",
       "      <td>338400</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>54.183333</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267827</th>\n",
       "      <td>287</td>\n",
       "      <td>2020-02-06 09:36:10</td>\n",
       "      <td>2020-02-06 09:40:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.776197</td>\n",
       "      <td>-122.393010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.782529</td>\n",
       "      <td>-122.391315</td>\n",
       "      <td>395252</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>4.783333</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0206</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec          start_time             end_time  \\\n",
       "210892          3251 2020-02-03 08:00:27  2020-02-03 08:54:39   \n",
       "267827           287 2020-02-06 09:36:10  2020-02-06 09:40:57   \n",
       "\n",
       "        start_station_id      start_station_name  start_station_latitude  \\\n",
       "210892             101.0  15th St at Potrero Ave               37.767079   \n",
       "267827               NaN                     NaN               37.776197   \n",
       "\n",
       "        start_station_longitude  end_station_id end_station_name  \\\n",
       "210892              -122.407359             NaN              NaN   \n",
       "267827              -122.393010             NaN              NaN   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "210892             37.795254            -122.394187   338400  Subscriber   \n",
       "267827             37.782529            -122.391315   395252  Subscriber   \n",
       "\n",
       "       rental_access_method  duration_min  start_year  start_month  start_day  \\\n",
       "210892                  app     54.183333        2020            2          3   \n",
       "267827                  app      4.783333        2020            2          6   \n",
       "\n",
       "       timing  \n",
       "210892   0203  \n",
       "267827   0206  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a36a01c90>]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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JAAAAAFMRSgAAAACYilACAAAAwFSEEgAAAACmIpQAAAAA\nMBWhBAAAAICpCCUAAAAATEUoAQAAAGAqQgkAAAAAUxFKAAAAAJiKUAIAAADAVIQSAAAAAKYilAAA\nAAAwFaEEAAAAgKkIJQAAAABMRSgBAAAAYCpCCQAAAABTEUoAAAAAmIpQAgAAAMBUhBIAAAAApiKU\nAAAAADAVoQQAAACAqQglAAAAAEw1bqw31NXVaf/+/frRj35kXHO73Tp16pTOnj2rrq4uxcfHa9Om\nTZo8ebJR09fXp4qKCl26dEm9vb2aN2+esrOzNWHCBKOmu7tb5eXlunLlitxut1JSUrRhwwbZbDaj\nprOzU0ePHlVDQ4MCAwO1ePFiZWRkaNy4P7Ry69YtlZWVqampSSEhIUpNTdWqVatksVjG/AEBAAAA\neLXGtFPy/vvv65//+Z/ldrs9rp84cUJVVVVauXKltm/frp6eHu3atUs9PT1GTWlpqS5cuKCsrCxt\n2bJFLS0tKi4ulsvlMmr27t2ra9euafPmzdq48f9j783jpKjO/f9PdfX0NlvPDjMMOzPsy7AKKChE\nlAjJjV8UMYomcsM3aNxegt6YfL1XjSHXJCZ6E5dfEn0ZjSFxuZJgiEpiAmpEWQREkJ1h9n3r7uml\nfn9UnzNV3bV390wPc96vly+HOl2nzqmqc+p5nvM8z1lPFSBCMBjEo48+iqamJtxxxx249tprsXPn\nTrzwwgv0N+3t7Xj44YfBcRzuvvtuLF++HK+88gq2b99u+uYwGAwGg8FgMBiM1GNopSQYDGLHjh34\n/e9/D6fTKVMkfD4ftm/fjjVr1mDlypUAgIkTJ2LTpk3YtWsXrrnmGtTV1eG9997DnXfeiYULFwIA\nRo0ahbvuugt79+7F/PnzcfjwYRw5cgSPPvooJkyYAAAoKCjAww8/jFOnTmHs2LHYvXs36urq8NRT\nT6GgoAAA4HA48Nxzz+Haa6+F1+vFzp07EYlEsHnzZjidTlRVVSEYDOKNN97AypUrZSsqDAaDwWAw\nGAwGY+AxtFKyf/9+vPHGG/j617+Oq6++Wlb2xRdfwO/3Y86cOfRYVlYWJk2ahAMHDgAADh8+DACo\nqqqivxk+fDhGjBhBf3Po0CHk5uZShQQApkyZArfbLfvNmDFjqEICAHPnzkU4HKbXOHToEKZOnQqn\n00l/M2/ePHR1deHkyZNGustgMBgMBoPBYDD6EUPLBuPHj8dTTz2FzMxMbNu2TVZWU1MDABg2bJjs\neElJCT7++GMAQG1tLbxeL1wuV9xvamtr6W9i67DZbCguLpb9Zvjw4bLfZGdnw+1203bU1NRg8uTJ\nst8UFxfT8ysrK410mbJlyxbF41u3bkVhYaGpupIFWe1Rur7VslTVm05l6daeod6PodDHdGvPUO/H\nUOhjurVnqPdjKPQx3drD+qjdj3TGkFIiDUaPxefzISMjI84tyu1205gSn88Ht9sdd67L5UJzczP9\nTazSQn7j8/kAAD09PYq/cbvd9DdK1yL/lsa4MBgMBoPBYDAYjPQg4QCL2KB36XGbzUb/Vst8RY5L\nf6/2m9i/pdeS1qOGWv1abN26VbWsqanJdH3JgGi/Ste3WpaqetOpLN3aM9T7MRT6mG7tGer9GAp9\nTLf2DPV+DIU+plt7WB+1+9FflJaWmj4nYaXE4/EgFAohFArJVkv8fj9N5evxeOhKhpTY37S2tur+\nxkg9fr9fVk7OkaYWZgxNwg2iKyBsGf1SZyquNxCwfqQX/f0eXywMhT4yGAzGYCVhpWT48OEQBAEN\nDQ0yrai+vp7+e/jw4Whra0Nvby8cDofsN5MmTQIgxqQcO3ZMVnckEkFDQwMWL15Mf9PQ0CD7TWdn\nJ3w+n+xa9fX1st+Qc6xobYyLi97D+8Q/ps/vlzpTcb2BIN36YVW41OrHQAisqeiHVfTqtNzWzw6K\nfxSXaf/QBKm6b1brvViUnYulH4zBA3vnGFIS3tG9srISGRkZ2Lt3Lz3W1dWFo0ePYurUqQCAqVOn\nIhKJ0MB3QAw6r66upr+ZNm0aWltbceLECfqbI0eOwOfzYdq0afQ3J0+epHEoALB3717wPE+D26dO\nnYpDhw7JVks++ugjZGdnY/To0Yl2l6FAuKG2b2JJc3oP70fv4f2mz9Pqo1adWmVadabbPbV631JF\n7+F9fQKmqfO0npW1OhMhFf2w3hbtOq221fe3P8P3tz8n0rSktSVVfRyId8cq2nNZevUj3eZBNQZL\nO4H0a2t/v3Pp1n+GnIRXSlwuF66++mq88sor4DgOpaWleO211+B2u7Fs2TIA4grHggUL8Mwzz6Cn\npwdZWVl4+eWXMXLkSMybNw+AqExMmDABjz/+OL7+9a8jHA7jxRdfRFVVFcaOHQsAWLRoEV599VX8\n4Ac/wPXXX4+Wlha89NJLWL58ObxeLwBgxYoV+Mtf/oLHHnsMq1evxtmzZ/HGG29g3bp1bI+SFJFu\nVnQtAgc/AgDY1m00dZ6R1RClqCkjZYNhhUWrH6lyX9M6lwqWJu+P9vOwVmciWO7Hp6IRiDP5Hmu3\nRf3eAIB/97viH6bvuUPxTAMAACAASURBVNhH7nqrLVOvM5nPP5F6Ax/vsXSeVRIZV9rzTv+PAS3S\nbR5UY7C0E0i/tvb3O5du/WfISYqUfsMNN4DjOGzfvh1+vx+VlZXYtGmTLIbj29/+Nl544QW89NJL\nEAQB06ZNw6233kqDzzmOw+bNm/HrX/8azz77LDIyMjBnzhysX7+e1uF0OvG9730Pv/rVr/Dzn/8c\nHo8HV155JW644Qb6m7y8PHzve9/D888/j5/85CfIzc3F2rVrsXr16mR09aLGuktEen3IpETefBnC\n9lfijoc39L0P3Kq1sK1ep1mPtI9qdWKDxjumcD2t+zbQ99RIH/v6oT7J+9/fJf6xeIXpNmidqydc\nEqz0Q6/OZJKKfhjFbJ29R0U3LN7wFaLXic4rZs/TIlX3zeo7QMZrwq4Hhq9nXbDSnnf6fwxoodXW\ndHL7GSztBAb+2xJLf79z6dZ/LVLh+prumFZKrrvuOlx33XWyYzzP48Ybb8SNN96oep7L5cK3vvUt\nfOtb31L9TW5uLu6++27N6w8bNgzf/e53NX8zbtw4PPzww5q/YcRj9UNndfWhP7CtXocIoCyYwLgg\nJ5049epExRTg+BHd61ldYUkEox9IM/dNa5L3/zNqXbeilPzrn/Rcq0K57rOKloWl5QkI+nqkqh9G\n2yl9/kbe47h7A32FPvzfDyi+/9LzUDEF/H2P6baXkLLnr9JHvXqTZeywSiKClXRuMXNfByLexupK\ncyrQXrm13s7+TpQy0IpnKgwsZkjFKnOqoG6v1//7wDakH2H+TAyK0Q9dsj7I/WVBUhNMtNpoaDVE\nQfkgdSqeb0AA0rqeFcEzFjMfcqP3TesjFzp3EoA1K3no9HF6biJCuVY/YLFOq6SqH0bbGfv89epU\nHQca1+Xve0xVMQFgWiHRaqdSW3x7oitsldM1z02kj8lSEq1iRrC0tLIbRcmIYtVlzsh55lY2B87t\nJ5ntTLUb70ArAbH099hJt/6bIXBAVKBsTClhDEWMfuiSvfqQyo+KlsBBFAQlS6CRPnILlyHywAb5\n8YXL6P9jz7XdeheE999NeIVFj2T6jCv1Q+jpklu9CRpCjhGF1Yiyq6UIaqHUD27hMnCFJQkL+mZJ\nqpKM+PdYC6Xnr3ZvNBULct1jhxQVDFXFxIJCQjB633p2/EH8I6qUAOp9NFOvFK3ngWhdkWjdySAR\nwcrIXAaF8njjgzUXJTNzjrkV2oFz+0lmO1PtxjvQCrSZNqWbIag/0Bo7QnND3LGLHaaUDGFS8aEz\nJzyn3tJlZkKKjWHQtbDuEV2TMq//BgCg+/e/hnDsMLjCEgjHDiuWWbHamp00k+kzrtQPrnwcsGqt\nplCWMsrHxislUeFSC7XnwRWWaAqshGSv6hm5phQz77FZNxO1e5Poigf31a9D+NEDcccSwch9C536\nAoB8dU7r+RutV0oq3Om0SPR6evOO0FSv2/9Ek3YYnXNU+2rR1S5ZxPYjWe3sDzfe/lQCjGJ2zCVC\nOvafIB07qXB9HWwwpWQIY/VDlzyrbf9Yumyr1yG8/0Og+oy8YMRoWRulMQwEzYnz2CFwq9Yia+1t\nAICenh7g2CFg0TLNMq06kzFRW/UZV0SlH7Zv3KX6QdayrmthyKJbUAIBAF82CgAQvnBWJlyqovE8\niMDqvmYNAMD3pz/E1ZnsVT09IVkJo++xWTcTjBiteG/C77+j/SyPH0H4vx9Q/UAKUTcqvrQcABCu\nOS8emzBFvU4dyH3jPFniv3u64u+bvyf+RK2xigSex/lTwIGP5AUz5yVlVVPpeokIVlpzC+k/P3o8\nACB85gQiv38uvm+ArotSMtxlrKw0J1sRNDR2lq2KKzayIt4fbryx9KcSYATyzsGTKf6/p9vYXG6S\nZMkrqUI6dlLh+jrYYErJICSZGRmsfOiSrsz0h6Vr/uVA9W/ij0kInRY371SysHLeAvHfbc19E2fl\nNNgW9U3q0tUTrTItAciKcJQsn3FFNPqRCsHBtnodwj3dwLvb5QXLVonX/vUT4FatReTvO2h9UuHS\nSj+IwJoxehwAwK9QZ9Kz6+gIyWpwC5ZC+OPzccekmHUzQUGJ4r3hF92V2Afy+CGgYgoyiAKZlSMe\ns0DsOy70dPX9/fzPEH7+Z3HnyKyLmdngn3iJ/lP2/AFDz0PxOdsUoqWUjqmg9V7FxsYAiQmWmnNL\ntP+RPe+Ida5aCzQ1aK6Iqln7E1nV0RIgqausRRdOglFF0IyRJNagkUgcU7LceAlG7ml/CuVx7enp\npn+SsZzM9iTTfSsVyR5ijYh6hqDImy8P+OpOKmFKySAk2RkZrHzojFptzcZpJDrY1AY/l50DAfIP\nMk4eQXhDjKKCGGEmakUW3nqVtpEIKzYFAZIc0yqzusKiRrJ8xhXr1uiHlota+MRRIJpCljJphrHn\n6y2MP1ZzTjmOJdqn8Ae7tBXpaJtl7yPpW1Rh6X35abFo3Ua5wIoUZAHSUpK0yMoFEPMeR4+ptdWS\n4SHaNv6+xxC+Y2386oPLo2+xq5gG/pY70Psf4jzF/+BZhJ9/UvscFfTecbg8yiskAJBfBH7rr+Lr\nlL7b0edB3g+l56H4nN3iio3seUSPGUHrvVKKjbFitKAozC3CO/+rqNBBep+VhGQj1n4DykPsfJ1o\nLJ8RkhHjQpWLqJFEyaCRSBxTMtx49fqQaL1WGYj2GJVX9EhGsoeUGhEvAphSMghJ9mZkfe4rYqpn\n35+2aX7oNK081WcQ3rBasqxvLttPoqhOGkof5P0f6ldYfQaCZCITDArBBEUlyeIKixbJ8Bk3jZYC\n1d0V/3ulY0ocEJ+LTNDr7RWVwxQkVyD3WyYgNtfrBvMnkgVIU2nVwoxgqeNmYvj5O53xAr/Tqfpz\n6fwQ3vN2XHvCe962NN713vHwlm8CLY3yk1QUkri6Y94BTJ8f9zwUkwREwkDsGGhSD041s1ocOhm/\ncmt1hQ1An/IdXWm3rV6HSEEJ0Bw/N0jboqQEGFkRNaI8KO1FZFu9DuFzp4GD/5JXOmO+bD50zLsU\nAND70T9Nuf2YdRvWNNppGDQSiWNKtquV3j3tbwYixsPIKrMe1jdtNbd6LRz4CDh/Sl5QPvaiV0gA\nppQMSpK+GVn0Q8fniy5Kei4xZiwdZrL9JAP/XpWdlZWEfZ0PcjLQEoKlGFph0cGIz7glC6saWgqU\nryfueuSYEeIE793vJCXbUcKbYEraZyRQNelYeI+lbiaWnr/TDaAVGVNmAgCCRw5EjymTSkuo5tyx\n5lbgmR/JT1hzq6n6TSeJMGlEMHVvAv74H1hdYUPfPCJdaSfHjCT0iFUCdK39pF2OqALbG4h759T2\nMbKtvQ2RGAHaFp0PyPfK1tVBr6f1vUrUbVhr7lQyaBhaEdepNxXzteY9HQD6PcbFwCqzHtY3VzW3\neh2OulHK6O4wedXBCVNKBgEpz8gQ/dD5f/wgAMB27yNxHzqlZXYzQbdGsv0kI8AteES8XuzOyoqC\nfYoVEqB/d4814jNuycKqgqYCleMFt2CJXLH47GDc75XgLr3SkuBtZqXEqO87lMr7KQuQ0iqb1j03\n4mZi6fkLEaBiCvIf+QUAoH7LBqC1WbOtybCEKvVf8x1/VxS2bUXiPBJprBePzVmsWacUK0kiYol9\nRoZdlFwexfdKNs/nF8U9M6V3wkiKXu56Y0HAxIVVSQlQThneLW9zb6CvLCZmIHT2BIB4Axt5zoqr\nIWR1IuoWaPvBs8lTBJWwujo5Yz54Sb2m4phSMF+n8rubSHusrniZxsI9tbq5qNUkCVQpyy8BWprk\nClT+wDyn/oYpJYOAVGdkIB812WZ1ca4LCm5RSsHj2bn6e1ioZPsxOuEaGfxG9sXQ9VNPQsBhv1rR\ntSbdBCysVlBVLAygJXibdUFIdBNMJbe3ZGUB0kPLf1lJ6DTiZkIw9fyjsSEE/r7H4mJDlNqaqCVU\nsf9a7/j5k0B+EYqefR0AUP/Nr4jHJMS6C/WHf7eSi5Jikoj/9zNE/vs/4l3QCAZd0QDtd0e60m4m\nEYKSEqCUtYsrH6ubMjxW+Yqdr9HUEKcISRUBmUJA3AI1AqQTUpItrk4qtkMhjklWb4JuvJqkQNFJ\nRnuMrngljIV7as4rROL6aSJJgmKa+qISYNI0w26hFxNMKRkkpGIzMjMCvaIbVjR43B79IIXOnAA3\nfykwfpKlbD9GMTv49QIrw7vfjrP8Iq9AvOc/+T5w9IC8bNLMhITglPnNaky6RtzCtKyrZrOOJOKG\npl1x7BqYyjFSlGDgrNX9ZqRYzdhixJVIWmbEzUSK0echVUjUjim11Yhl1vSme1qCRfk48Fv65kJ+\n668Q3irfJyU27Xcqk0TQa+5+R3ZNQNlKHPn9c+oKCQC0NBrOvBP4uM+N1dBKu8MpW9EAAGTn9sUG\nqSgBNBHIh38H0Kc8xK2gm0VpNSTB1VKrSrLV1clE6k3J/NnPhimj7TG64pUoVu+p0bk+dr4ymiQh\nHYyI6QRTSgYRqdiMzCiKFn+SRlLyQdLaw8JIth9jbTEQFxAlNtZA0YKY441XSnK84v+7O+MrVToW\nZSAznWhmmDKAkkWXoLk5WhJTVOviUch25NHOdmTU950Iy0JTvSG3N6NCTqIZW5QzfikI7ANoCdWa\nH7TaY3bTPS3BQqqQEGKPSVeD6fkpThIROnsq7pqKVmITKXj1CHz6Cb2mkZV2TJsHvBqz6n3l18D1\n+rRdXKOJQATy7yS4w2opQrS9FlICp8J9Kd32/iCYdf3sT2SJMHRWvJK9aa1VjDxnpfnK6up1ot/y\nwQxTSgYRZDMyLjdP/Hd7a0Kbkem6LyF+mV3JrSH2gxT+YJehbD/WLcjWc+IbVmjOnhQtiVGBV2rR\nVE09GsWoZSVVE65VIVhp88i+OjX2U0hyimotzGY7oucZ2ARTePtN8bjUjcDAfjOaG/nBeFyRmVU2\nraBrrbS2ycKQ25OeTz3kFv1UrDIaXQ02kiTCecVKsc27dljKThhWuDexVmLbomUI73lXMYuYVr8N\nXTO/SLFeHD+irLC8+htxbldRAgCdDEJKG0uWlAL1Nar9gMr1pPVaTglsdS8aDfo9LsIgyd7sNZlY\ndYkaSJQUWiObi2LG/LjzzKxep0v/+xOmlAwmopuRcVGrvlAxxfJmZATbavXN6jhPpuV4C+IvqZV5\nxeiAMyQAlZQB9Rfk5QqxBr2f7hXbuG6jsdR8nx0EN3mG3LfZQMC2GctKsiccq8H1Slbkvjr1rfbJ\nSlGticVlbU3Bkgjz0eelZ7GKTRestJGflexciRoJiKColdY2EZKx51DcR/egOB5tGNhVRiNJItDS\nQNuR8ArUwX/JFQapS5QSLY2yVOuxGHl3FF3DtNzFoK0EcIUl2hmEfqngVlw2Cty8y3SfsdY+HWRs\nGk1hTzEwd5iek/s7LsIg/ZlgxQpWXaKMknSDn5JCa2Blk/zG6up1uj/HVMCUkkGAbIJurEOEFDTW\nAYDmx8oQSpvVeQthu+rfLG/wRPwlSbnSZG10wBlSHqrPxislNlvCCk1EKchTI2DbzO65qZpwtIRg\ns0kC4lCwWic9RbUGlpe1ox8VzicqENyqtRBefloxcDYsFRCjwcVm4pgSyc6l9bGGxjX10nAng2Ts\nOWTonUtwx24rypMR/+7ee9eL1f74hdT5dxtMrhErdOm5sWrBrVoLwcC+IIoBuVrGF5WNJW2r1yH8\n3k6go1V+vZw82j8jK5tKGxZqYcwybXLsJCEuIhUr5v2aYMUAphNzREk0BW/S5kAVhda2aJmua3BC\n8bMSI+pQgSklg4CUWxAPfAgAyJgxFwAQPLhXPHbVv5ne4Ek4/Em8MCZpe/iDXTQ438yEoycAkWxA\nsbEGhiyIsQoJILNkmvF7tbI0neiHw4rbTzIma60MOolmhNPDbGYqKjx87/+KRQ//EpGCEghvvmwo\n25HpIEYNIdFqQK6eZZqQKoHE6p5DZsdH4jt2xytPaqvB9L5puL4RoULoaKOnaiWJSDR4Pvyf39FN\nta4V/6WKPQMIBeXHPJk0bkZvXxD3FV8GIFfYtFaYNF0t7Qqih+QYqVf6TYpLCaywYWGiGB07ZuIi\njF4zkTl5QBOsGMBoYg4jLlFG+pFsw4yWQqs1B5qN40n359gfMKVkkJBQSkMDcKvWQvjis76/oy5K\nRjZ4AhedwgUBiMa7aHL8iKLrArm2Wn80FSSND6BV67NRq6WiEKRkeSwptWxB18KcImRuZ1kolHOr\n1kI4dihlKaqNYDYzFc2WI02HumgZsGiZoZ3AzQYxJjJetQQy5X0hugzvPp8IhvbwgNKKYN/zMJKY\nQjh3GoD1gGTFd0NlNZigtKO78P47iu5USsq34hxw4ihwNMbVc9IM3eB5IztPa8V/qRKrkABA7J4i\nEqhiSOJfPhXnM5m112oa8qgCIovVkyoqJInKETHzoXSlndQZOCgKr7Z1Gy27KVoVApNpKEyGAD2Q\nro9G0ErMIVN2k5TsoT9XipKZQCHdn2N/wJSSQUSqMn2QPSVCt4s+uvy9j/S5KBnZOEoQ+v5WsnJI\nseieAOgMfpUPoN7mYADETFsSKygAYMRo8Pc9pumiRlCacBV3z73rPw3vb2E2o5VxH934nWW1rMhq\nwpNt9bqkp6g2g9HMVIbSoQ4fEV/JmAn6++1oBDECCYxXEsMgMRLEWqbl+0KMS1rWJinJ2/G+73kI\nxwzEwJ36PKEsYorvRnQ1WBpTRFaD5ef2tZWrnAZBTfGOwlVOizuPIok1ij1mykoMQPjj8wj/8Xl6\nX0OnjgHoH5dJnBezh8kMGuRZWE1Dnu0FN3+JeqweWbl6648AAP6BHyH8P48ojkkje1KpkYgQmCxD\nYbIE6GS0J9XJV5QSc8Qqu3ouUUoM6ApDkrMeptoAne4wpWQQQVcmXB7x//6ehDJ9qC1By9yWFDLo\nhM+f0s9DP2IMUH1afmzhMvC33mlQ0De3cZrZXPLS68LpUrVMWvV7pc8qwyH+P9hran+Lzt89J/5x\n50OK7Vbsi0UrOibNjC+PWpE13TP6MUW11cxUhtKhRgCgWr4TeHu7LCYqFiNBjJYtaNGPdfAOMXMA\nf+8jcZZp6b4QVtNwx2J453FJ3VqxCASzz4NbuNyUH7Yh5SmvMD6mSCFhhcz6nqgrpk/M0idzKY0e\nsxI4C+i7TKreV6vIXBjNpWjWghjCYmP19L5JmnVaFNoSEQKtGB5SKUCnZMNSCxjto3jNeGXXbD8G\ndIUhiXuK6BlRw9tfueiVE6aUDALiXlRJSlqlbD/JhBs5Jv5Y+VgIeisi4VD8sYC4OZcxQd/kxmka\n2FavQ3j/h6p+2qQOvmyU2PQLZ4GsXAAJpAIkBHvpn+RZGUmXHCIWcd3e6QS5vvsnzXOlwplMeCJW\nZA1FkKao9kZTVLcllqJaCyOxQVqZqRTToUZXdcK3X6e4E7hty2OJBTFq+OIrYchIQPpK/pDGaiWY\nhlvR7U1HYFOKRUBhkb4inOGQjQ0AQFaObJVNulqoJegackMkCStiYooMBd6PGK04d+i5YhJFSLbK\nTPqkETir5vqpl0qXq5wGoa5GMYCcW7LCWiB8NOuXDAXh2aiFXS8WwxAJJkJQwqowT74P7mvWAAB8\nf/qDruEhlQJ0oq5EyYrFSFSht9KPgVphSOb+L8x9iyklg4JUvagpHQDN9QBiBN3jnwLQCWKMkkyr\nHKDjp038l/++QzwuzeZixaJpMF2y1AUnbsKNFdg0SDiw9rODusITERBlgncKUlRrYTU2iFu1Fpg0\nQ31VR2Mn8ISCGFXcE1KB9L3S2jNBe7NCZYHE1AocoKsIA1B+v7s6ZJkEzex/Y1QgiYspgv5KqlB7\nPl4pGVYGbtYCQ4qQrI1Rt1jN3bwTcP0MK71fvQHLmbmsxtWpoWtcMLLao1Aea0W2ut+IaWHeaiaw\nVAnQCboSGc3caATVZ20gOyFGjLbUj3TdzNIMekbUix2mlAwSUjWJGcpMY7At0jYJ/3oP3PwlckH3\nX++JPyAuKNLA+tg4FUKyfEKjKx+yj86JzxCOUVQAyK3PUfe12Kw8an6veh9VYfsrdMKVuuAIr71g\nOKhWCdXg+qgrjWZgbdSVQlpXrPAkExCl+3SkIkW1BlYzU5HsbBlTRFe14JEDdFVHayfwPkuoyT0R\nIA2eVvDFj5LMrE0krS15DpyCgOTf27dZYSxqAomSEcFIHItaW5WOx/ZFbI+5/W/MZAOMHVdKrnr0\n3bnnJgByaziOHYbtW1uMxXEZXPGhbVbYkE8r3gTRNkQAGiwu3dATObngvrRae77e+TrQG5AXOJwJ\nxdWpofct01z5NagkaWUnUxSurQrzCWQCS4kAbdKbINWZG5X6aLv1Ll0FW1GhN7EfVTKCzgcSI8ku\nzMadDhaYUjIIMONnaClQTSczjRK21esQfu8v8QHiOV4q1KoJumTijAusb44XnAl6QfC6WNz8SMkS\nqOX3ivKx+pa+6jMQqs/EueDoorNzuaKFNdpfrcnayN4fUgGxv5aYlSZdzRgXrY98dFUn9zvfAwA0\n/fj7xlZ1DFhC9d5H7aB8pfS1XfGrDcuu0VUuaSzKPeIKkO0nv437kAePiNezwYTfd1NDnBGBxLGY\nVYQFpb5Jy6PCtW21+f1vaBIAiRsmt2QlhIx31MdkdEdzpYB2as31ZMW9A8KHooHFiGBpZsUHgPKG\nfAZWZ6Vl0g090VArlikl9CgfK75zO/4QX2fUBTfR/STM7lFhW70O4ROfA0cPyCuaNBP8Pf9lKGVy\nQCM7maJwbdU1OCZrGyc5RlCbH5SSViQqQJv1JjDnZmXetUttvrZiZDWzH1Wygs4HDAUjKjlGMD2v\nDBKYUjIIsOKfaconNJqZRjGmQIsMBSEsekxxApFY2FWDGA34C1vK0a/w0Qn/zyPA9l2qp0gFJKUJ\nWWnC5crHWnbtgssjixeSEZOi1nR2MgOTtda7Eysg9of/ruKkqxXjotX/imngb7mDulLx9z1GV080\nMWAJjb1v5qyPCnt/5BVCiDmVy5MnHuDcYrILwdcjs6LrxaLElmkRGzsjNSKoZUICEL9buRQDrl16\nwdz8fY9pWrsju/5M249jh9SD66N16e0pQ+NRpO9A1MBiJKbA7IqP2oZ8tkXLEN79NhB1l6TkFej2\nA0C8QgIA50+J99eTBfR0gfMWiP1qawZi+ihN/W5mPwmjKaGlqaTR3Rn/A3Js/uVA9W/kZfMvl/0z\neOpzAMoKrWK2xBTuJ6E6r6okrehvAdroXG4pS5jGfJ0OK0Vpi6Hvtcl5ZZDAlJJBglE/Q6uBaqox\nBVrw4usjU2b4BF8phZWG2IlKywqmhtJqAL/pQcOb3ClOyCqBzLZv3IXw3t1AXbW8wmEjdN0hwvfc\nBHS2y8/LzpUpJNL2GM1OZmSyNp1Kd0xFXHky/XcDB8TdbG1SpURjkzut/vO33AFA3kdyjKC0MkPu\nmT8qOPAKeyLEukSZMSJIsz1pZaUiKWHpv30S5TXBNNxQaCs5rmtEsHBNy0jcF6WGCdUxLHXDXLgs\nfl6JxhQZnVv9H/0DQMw7EH3nek/Ep28mROrEzVn1VnyMbMinuA9UTh7th+WYq6YGoLAYwluv0mPC\n/g/lz9pE6nfpO9712m/FYwb3RrKtXodwdCUKDqf4/94AzVzGZedAQIw73ckjCG/4TVx9SumC/fs+\nACA+x1h80eQdqJweVyZzw0tghSH2fVVKWpGoccesN0Ey9j9SdCdSMQbq7Y1j1fMjmUHnSqQqZXIc\nBr7XZleSBwtMKRkEaKbQrD4jDnALu6QTSEyBTNArMLCEHM01LxXKtZQZo1lyBAD26JJ2SGFJO3jm\nCwDWBmMyNzrU3FSsbHS8UlI2WuynloVo+EigM8ataPhIhX6oZyfT2pVaSryLlvnUrcLp46Yyz0jR\nm+CFpnrVNiu50xlTvNTHR/drL4p/bLzfUFYm8uGUukTJrg3jQZ6GBH2r6MU4qWTtEt5PwMJ4/AhQ\nUgbUX5AfnzEf3Mgx2vENShtzxsRT+f75jvjH4hXGYtxaxNUFW8lwAECkvhbCnl2IHD2ovrpA5tYY\nwgpzQHjDVwAA/D2PIPzgRv3zFFZ8DPdjz9uAOzN6I7rF9OvkNyrCpZ7LnJFjiihktYtLLnDhLAD5\nKmv4/Kl4xWbmPAAx90ka59JYK5ZFY/Jk7nT7P9RtqtYKHGlzD3FjU1BKYtO0W11hMKPQWBWCzXpM\nKK/668eNSfuptLKt9N2xYgxM1s7sidJf7VG6b2Y2cx3MMKVkEKArIAKWd0kHdAQ9DYgy49+9U6zH\ngDKjF1hPgnUj0iBwlSB4pY+Kvn+/gjBvs8X/sHQUuHmXmbaE0fvWJgpBslWk6DHqSzwi6vtefbZP\nmI+6Hcj2zIgek/dDPTuZ0edoaH8HjVS65FlZjbeIneANrc5Er60UsK3nMqjWR+pm8Znow87DQJYg\n6As5VoI8E1nVSDQbnBQtv28j9QKIV0gA4OC/IMQaAKTX3f6K5qoGIXxO3NCPh05KbFKnJ1N8d2yi\naBzJzQeOHxJdo2D93vXNq313kH/kaV2FnggOSkYSzSDwXz8hCop/+j09Lo01o65WZMU6HNIVLi1T\nMQW2W+/q2/mdtEFll3jZMQVF2Hb9BtWEFQSpO53/Vz8Rz/vmPboxiUafY+iUusFLKU17IisMWskV\nCFaFYNMeE9EVP758LG2H2f2PzLgT6SVmsdyPFDOQ7TFiKBzsCgnAlJJBA3/fY6quPdzSq5MSdGx2\nwFHXlt1Ri+riFcaWSpWC6GvOyyZy00HgUfTiTaTCvKYws/8DCPs/MBTjomZ9V3WJoymI36K/o8J8\nJALkFyH/kV8AAJoe2Ai0tSQcp6CEoZUrrVS60ZUJmXCgE28hL5O308ykq7Q6oYSpoHyfPJ4nEXcY\n2SoNHxVjwmFDQZ7h3/wMiF2liO6LoQQ9rrIywd/+XYSfejReAMjMUq+T7KlTMUUUIBWMCPzaDcr1\nzpgP+Lqsu2+d+3QovAAAIABJREFUj266Kr1vv3sW4fOn435qeIWpoAT8fY/1bUj55O9pTJGW+5Zq\nrIaRFS8NhZ6gGFNkQtCl7l4fxMTGSfaJEohBR+n9MLD/iSrHjyDyyv8n/k0MO5GIblydliLMFZYY\nXoGQzgHC+xrJDKJtNbKqoxrTByimsVaK8TK6wqCZtCKKVSHYtMdEdC7v+amYCMR298N0/jIa+2HW\nnUgrMQshWbvdJ4uBbo9efNzFACcIQuzcwDBITY3Fydwi4ddeAKI+v5SrrwX/tfWG0jYWForKQFNT\nk2L9QjSwkvvBs3FlWueG/++1AAD+l6/GlSmdF/7hFuDkUfkqwrhJ4CbPSNjSxf34QbEv9z4CQN+K\nqgWxdsdaArHsGv2A3ZnzwG96UNZ/RQEulrwi8D/6FTKjLmHd0+cjvPUB8Fv0A3LJPTD7HDVjXJ5/\nEtjztiyVLhZ9CdJ4jPBd4nX5J16Ou15GNDg4qOC/HdvORJ6VtM1G+6goNDKGHNyqtRAcbuDVmJgE\nux0IKWwCmwyiQoRw/zfFNvzwVwhv+SZ7Hy8mdAxakT3vQnj+Z3BcshQA0PvB38HdcqfMsKc1l+dF\nggCAVluGobmTXNv9kRiv45u3JO434TtEgwv/ZF9dpJ0y165b7tRXBAH6nivOyQr1YuY8w/FKWjJJ\nssrM3Nf+aA8ARL44Em8o3PwYbJJNi6XvxkBSWlpq+hy2UjKY6OhQPWYlk4UZ67uR85TcqdRQWkXQ\ntSC/8qxqqlRCKCbriiE/bY1lZKXMHWZ9bQn87d814UsrCciO7plhNk7BqAufkVS6ZOWmfssGYP/7\nCO95O64epedvaGUqCfEUen7Yiumbc72pEQITCfLWysBmFd4us5wz5KgKHalSSABNd1vLpCK5wGAi\njYwMagYt2Tc5umIePnqQnmNmvy6Zm66JFeHu/xWNRzYFpURx7lFJIJKwO1GCafr7g/5Kf28GIZqM\nQWnPLUK6xeKYgSklgwBNC9qet0UBMTMbgPZuzrHCWn8E3CnWqxFYryUgK6VKjXX7IsT59ytNdDG5\n/WVopDWl7hBG3AEkGPF970tBrOISZnEzKq2Jk/qhe6LBsz3dcal0CSSVLpdfYCrewojQpRsboYHS\nvdNLQYrTX2hXqiHkaCmzesqnJgkpJBwQO0IcTvD/8wdl108bD0TCCVxPB4czfkO+HC+4JVdpu/bA\nQtyMRbhVayHs+GP/KW32DHBXX2stTsfAanH4lefiXe2cLiDgt9beVAj6FuKmDNHSqPxO22yiW6wW\nSsaA/KK+epXILwK3SOF7FeO+Sb7BQlO9/JtMEpO89RoAgN+y1dR+XbGuXUbd3oSGOvq3mmwh+/Zl\nZoN/4iX6T2nfEnInUklMorYxcWw/tGSSZMor/ZH+Xg3FfigZCmP23Eq3WBwz6LlkM9IAfuuv+iZI\nJfKLwE2fKw6SzGzYMrP74hQk9B7e1yewRbGtXkc/BFKU4iZizzVC72cH+1IFkmtKArJJneRYX058\nm/if5JiwP7rHxITJ4CdMFn9XpxBMq4DSqpHt1rsU+07RuucVU2C77weGrkOvp3KvpQjbX0F4w2pE\nGusQaaxDeMNq+l/kzZdlWVLIsjeJUzDyHMMNtXSii7z5MsIbVvcpWT3d4n+IxhRsWA0uvyC+Th2F\nRAuiIKq107Z6HeByx5/ocmveu9iPNZmUjdxzVVoaoeg9XFwqXkspQYLNlrAbmmVyvPHHLonu37Do\nS/FlV/6bKCCqEd3pXKtcDW7VWiDqZy/D79NVZtWyQQnbX+nLOiWlsFi9jYQxE+KPLbtG/H+qFJKi\n4fHHJs5QfyejGzmqcvwI4FQYG+MmQdjzrihIxiokgHWFBEjNysPxI30GEClTZwOwrnhyq9YCw8vj\nC4aP1J07sP72+II1t4rf3uzc+DKHA2hpVH1XwxtWQ3jnf0Wlt7MdQmd73DeZCuShXvG/6LFEvslK\n35/4YwKI8cKIbCFVSOLajvgkFGrH1OqQfpOkGxPH1RlzzP/+rr4Y0hisyitqGLuvyUexjxXTZAof\nf99jQMU02U+S3f/+hK2UDBL4rSo+x9FN9YiFwX/XjeLvn3hJcx8KKcZ27JVkmDKxwkJTLN5yV9zv\naFpPJYu20GfZokG3eQXicvfb/0uvI3x2ENzi5YaDjqX+q6obgBG0PsiSIE8uP7qxXUuTbkpcrXSY\nXPlY/X5EM15Z3YzK6pK/acuThj+1boBnyQjgbMwqhtNteZVJPe2zQuBvLBkZ8QGuZBM3TxYAgI8K\nvOHTX9BjA0JHa/yx9/6C8Ht/Uf79X/6oURlHP3zhD98DGmvlxZ5M/VTDSsSunJjF1x1/TJJ9ShVX\nvBDM5RXCtuJrulnWLBN7zwDg8Mfqwfl6CkDFFLGvAZ/8eGuTosU+relReI6HP4Fw+BPdlRQtd1vy\nbZDRXC+WfbwHqD0vLxteLpZtfSD+vD/8BuFnfqTc/t74oPfYdpIsYWTFmHyHDK1O5ObHlatlNYtN\nUw/EfOe+t4kqParXUyJmw141iDsR5xXbLLS1xLkTmcnCCPQZIm3RdNeR6tNx31bfP/sS7MTXqb5S\nYOZbZnSzT70VE6srN0p7shHPBWmdXH6BqT1l0hmmlAwm1twKxE6So8drbnAWfv5nfT6o0VzuXEzQ\nMV2diDkmnQCkm7yZckM6FLVYK/wueEQsM7zfyMjx4gdEMsmHP/qHvhvRsUPgCkrArVoLx/S5AIz5\nr+py6nPxI/lnUfGSZdFSQPO+HfgIwoGP9LN9KexFYmYzKqtL/l2vixug4dpbDSkzWv7U9H3LFl0O\n0dkJ4dhhRH7zhLqg296ifFyCaZcxHYVE5vInSbFKlBIuEgZWrUXkg7/R36OpIaFUuvR9inNB4YHx\nE9Xvj5abjd71lNyXeMmobFdQdkJhZZfIdOVoNN1zuSjkhM+fFldeV3xNHEM7X49XmBxOcCv+LX36\nePyI8ua0PV3i3i4XEw5HvODPcZbcbeH3qZfVRt1/ebt8DOUXKb/3BqFZ0cxuOkpQmu+UVsEk0PTQ\nq+Qb+gpKmeVimb0Q+OR9+bE1txpra9SdSKiLJv6pmBLnTqSVFZNm71TYCDjy9psAlL+t4VPHACjL\nD1pZsrTaErsBZLJiSsxcU0pQo4/SOtMx9sUqLPtWAvRX9q0BcwexiMzCHp2I+efeTFp2JbN1Sq1W\n3M/+HwBAuPM/EX5wo/V0mAbbmnnsUwBAd3QzLr1AdyVh3vbYc3GrL+7o3jC+6CSXSGYuoale95qR\nu6O7X//0t4m/jyNGg5u1gNZBhflv3KUdOGlR8E5ZAHA0w5rsfdzzrvZ+CYQcL9DRJj9WPhb8959A\n+P4NQHPMxpHuTOVVAikqfvH81l8h/F939qXa1WrDxQrHgbvmegg7Rf99bsXXIHx2EPz9WwGoC4z8\nc2+qlg0qpSxt4ICKybrB0eH7bwOaY1bACoqBAg0XN62EDqlIIKEFxwFqopVKLArF4l5F0pgMWfbG\n/3lEN6MV8oqA1kYgO0f8d2cHMH4KTbICADkNohGnI0Z4Dj//JPhb7pDNg+QY5bHN4v8fiF95Cm8S\nN9/l/+cP9Bjph6xOI9krNSD3iPuh2Bbh/vi2ZPxe/C4GJRtAAlC+h9H5n6CV8coWzQoaiWYFlcI/\n/4R4jVvuMpXtKzbTKKCdSXMgYNm3hjIqFnZAw2Kbomwluvn71dARHmPrNWN5Ir6qoaOiksADQG5e\nSpQSWaaTP0TTjD74U7EdansiZOeq+tMrLRN3vSauXPBRpUTVLczgzvSxxK6UCV0Kmd+sUn0GgqT/\nqnstxNLSKCZ0IO5ThGFlmvtJpCwj0YGPZO+g7h4VUpSUgfOnxDryFPbxEXQCdQFloaulUX2cDBWF\nBKBxQDJXGulGry434I9xi+I4zTkmrRQSEt+j9q73t1CuiiC2USndcmFJn6/8xBnizvVSPFnaY1kr\nNsjfo6wo5BWKx5P9HdSy9ZJrKa1OzF4IfuP9qvv/6Lm/ku+c1DXayA7qwl/fEOcs0u78IuD8Sdnv\n2p58VLzGw2KAtdSAJM3GSL009rzdp2SePg5Ae7PT2LFmeZVJAem3M3TmBG1LLIFPRG8SW1Qp0fNu\nCG9YrZgNLZbg5xK5I4be6DX5W4z0pI/QF0fi6rSShTXdYErJIEDXHQRQnKxlricxEza3ai0EhXS3\ncSh8zLhVa0V3gWQLeykQHuOsBOEUZhuKImx/BeG33wT/5Cuynac1s6jFZkbSQ/J7zdUFI0qXgvWJ\nxvHEkOjHIWFyvPFKSZnokmNpB/VUQD7Em28FWpvlZUrCbyytCvsI6Z0zUPB8/JjSshIPFPUXFBVI\nskEkN+sSoLBYtnpnyO0lXTh+RGyz2jyQFgqJBKV0y0314nPJLwJXOU3mWsutWgshutmsZQqKgaaY\nFUiejz+Walwe9fnok/fFe+CNjykhGwsacbcORo1T0tT44bffjH8PXB6x7Ogh8Fsek69MxMTZCHXV\nsn/rpgQG4lJfp/z7oWSEMrDRKY13VJp7DdK5TTRA2jSyPipumyB5JkZkPbPGWTOxL+kAc99KgIvC\nfYvtYZASrLqaqZKIxb9iCrjKaf27isBgMBgMZbS+u/lF4Fav68uKSCgpTZm7MVwe0Ygm+V7FEls2\naDb7NLLZsRY6qeFT4aqebAZKIbHivsWUkgS4KJQSxtBgTAUQXULvOzZBf68OBoPBYDCUSDejZmYW\n0N0lP2Zgl3hV0mhDTgAJGyd1945JMlaUErZPySBA2KGVupPBMECsQgIwhYTBYDAY1kknhQSIV0gA\n6woJkF4KCTAkPBuYUjIYSLeBz2AwGAwGg8EYHJw8NtAtMMRFG+j+zjvv4M0330RzczNGjx6N9evX\no6KiYqCbxWAwGAwGg8Fg9B+DxLh9Ua6UvPfee3juuedw6aWX4t5770VmZiYeffRRNDQY2PmXwWAw\nGAwGg8Fg9CsXnVIiCAK2bduG5cuXY82aNaiqqsLmzZuRnZ2NP/0pgQwMDAaDwWAwGAwGIyVcdEpJ\nXV0dGhsbMWfOHHrMbrejqqoKBw8eHMCWMRgMBoPBYDAYDCUuupiS2tpaAMCwYcNkx4uLi1FXV4dI\nJAKbzbgutmXLFsXjW7duRWGhws7LqeD191H/bwv751oMBoPBYDAYjIuGktffH+gmGOKiWynp6RF3\nx3S73bLjbrcbgiDA7/cPRLMSRuuFSqQsVfWmU1m6tcdqWbq1JxVl6dYeq2Xp1h6rZenWnlSUpVt7\nrJalW3uslqVbe1JRlm7tsVqWbu1JRVm6tSfZZenGRbdSogbZI9LMKgkgroio0dTUlFCbkoVWO6yW\nparedCpLt/YM9X4MhT6mW3uGej+GQh/TrT1DvR9DoY/p1h7Wx4GRVdnmiQA8Hg8AxK2I+P1+cBwH\np9M5EM1iMBgMBoPBYDAYKlx0SgmJJamvr5cdb2hoQGlpKTiOG4hmJRWtJUWrZamqN53K0q09Q70f\nQ6GP6daeod6PodDHdGvPUO/HUOhjurWH9VG7H+nMRaeUDB8+HAUFBdi7dy89FgqFsG/fPkybNm0A\nW5Yc+OfeVPw7kbJU1ZtOZenWnqHej6HQx3Rrz1Dvx1DoY7q1Z6j3Yyj0Md3aw/qo3Y90hxNIsMVF\nxM6dO/HrX/8aX/3qV1FZWYmdO3fi888/x49+9COUlJQk7To1NTVJq8sMJOuXkp+g1bJU1ZtOZenW\nnqHej6HQx3Rrz1Dvx1DoY7q1Z6j3Yyj0Md3aw/qo3Y/+wkpMyUUZ6L5ixQr09vZix44d+POf/4zR\no0fju9/9blIVEgaDwWAwGAwGg5EcLkqlBABWrVqFVatWDXQzGAwGg8FgMBgMhg4XXUwJg8FgMBgM\nBoPBGFwwpYTBYDAYDAaDwWAMKEwpYTAYDAaDwWAwGAMKU0oYDAaDwWAwGAzGgMKUEgaDwWAwGAwG\ngzGgMKWEwWAwGAwGg8FgDChMKWEwGAwGg8FgMBgDClNKGAwGg8FgMBgMxoDClBIGg8FgMBgMBoMx\noDClhMFgMBgMBoPBYAwoTClhMBgMBoPBYDAYAwpTShgMBoPBYDAYDMaAwpQSBoPBYDAYDAaDMaAw\npYTBYDAYDAaDwWAMKJwgCMJAN4LBYDAYDAaDwWAMXdhKySBky5Yt2LJlS1LLUlVvOpWlW3uGej+G\nQh/TrT1DvR9DoY/p1p6h3o+h0Md0aw/ro3Y/0hmmlDAYDAaDwWAwGIwBhSklDAaDwWAwGAwGY0Bh\nSgmDwWAwGAwGg8EYUJhSwmAwGAwGg8FgMAYUln2LwWAwGAwGg8FgDChspYTBYDAYDAaDwWAMKEwp\nYTAYDAaDwWAwGAMKU0oYDAaDwWAwGAzGgMKUEgaDwWAwGAwGgzGgMKWEwWAwGAwGg8FgDChMKWEw\nGAwGg8FgMBgDClNKGAwGg8FgMBgMxoDClBIGg8FgMBgMBoMxoDClhMFgMBgMBoPBYAwoTClhMBgM\nBoPBYDAYAwpTShgMBoPBYDAYDMaAYh/oBgxl3nnnHbz55ptobm7G6NGjsX79elRUVMSVeb1ecByH\np556Kq7MbrfD7/fD6XRixowZGD9+PN59913FskAggN7eXjQ3N6OhoQGCIMBms4HnedjtduTn58Pv\n96O1tRWCIEAQBPA8D4/Hg1GjRqGxsREtLS2yeseMGYOjR48iPz8fnZ2dsNvt8Pl8iv11OBwIBoPg\neR42mw3hcBg2m41eKxwOK55HfsNxHG0vx3EIh8Pwer0IhULgOA6tra3wer0YMWIEGhoa0NTUBJvN\nhtzcXLS2tsLlcqG8vBytra1oaWmB1+tFV1cXpkyZgnA4jFAohMbGRjQ3NyM7OxtdXV0AgHA4jGHD\nhsHv96OjowNer5e2vaWlBU6nExzHIRgM0mdVVVWFpqYmFBcXY8eOHbDb7cjLy0NPTw+6u7vj+pib\nmwuHw4Hs7GycP38eNpsNOTk5CIfD9HkowXEc8vLy0NbWBpvNhszMTBQXF6O9vR2tra3gOA69vb0A\nAJ7nMXLkSPT09KClpQU8z6O3txccx9F3ISMjA6FQiJY5nU6MGDECHR0d6OzspM+PnEfaYLPZEIlE\nYLfbEQgEaJnb7caYMWNQX1+PpqYmCIIAh8OBUaNGYcGCBXjxxRfh9XrR0dFB34NYeJ5HRkYG/H4/\nrTcnJwc8z6OzsxORSET13eE4jv4nCALsdjtsNhvtYyAQoO+X0+lEOBxGMBiUnWuz2ej5oVCIltnt\ndlRVVcHn86Gmpgatra20HTzPY86cOQgEAggEAjh37pzsufM8D4fDAa/Xi7a2trgxY7fbMWbMGNTU\n1NDzpNf0eDz429/+pthntXtgt9tp+yORiKFzSVvcbjeKi4tRW1uLnp4eWmckEkFeXh7Kyspw+vRp\ndHd3g+M4uFwu+t6NGzcO1dXV9LySkhIMGzYMNpsNjY2NqKurQzAYBMdxKCwsRCgUQldXFx3j4XAY\nHMehoKAAvb296OrqgsPhoM8qts6SkhK89dZbtO9ZWVno7OxU7JvD4UBmZia9XnZ2Nrq7u+H3+3Xv\nqcPhoHPPqFGj6L0B+uYsAMjIyIAgCPS9ip3PANDnQua30aNHo729HS0tLbIyMk7tdjsyMjLQ09Mj\ne5Y2mw1utxujRo3CmTNnaHsAYNiwYfjSl76EF198ES6XS7ePpD7yf/IcjLw7PM/T/tvtdvocY+9N\ndnY27HY72traaL12ux2zZ89GIBCAz+fDuXPnZOOD9N3r9aK1tTWuH3a7HaNHj8aFCxfoeTzPg+d5\nzJo1C263G3//+9/p78nYVusH+b+ZscPzPCKRCHieh8vlks1VbrcbNpsNPT098Hq9GD58OE6fPg2f\nzweO4+gc7Ha7UVpaiurqalpWWVmJkpISHDhwAKWlpTh79iwdV2PHjkVnZyed90OhECKRCDiOw5gx\nY9DW1oa2tjb63pJ3l9TZ2dmJoqIi7Ny5k96XwsJCNDY2KvYxMzMT2dnZaG5upmOnq6sLgUBA896Q\nMUmezciRI1FbWyt7VuQeZ2RkIBKJ0Hsvfa/Ie0TeK7vdTr9xbW1taG1tjTtPOm+Ra5DrkXnL5XLR\n95HMm6NHj6bfK4fDQec2Lcg3kfS3t7dX997E9lFr7OTk5ODSSy/FihUr8OKLL+Kjjz4Cx3H027Jg\nwQLccMMNeP3117Fnzx4EAgEqB5aUlOCb3/xm3LUff/xx5OXlKZb1F/xDDz300IBdfQjz3nvv4Zln\nnsHVV1+NL3/5yzh37hxef/11LFq0CB9//DEtmzBhAj755BP09PRgyZIlsrL29nY0NzcDANavX49/\n/OMf2Lt3L6666ip0dHTQsptvvhl/+9vfcP78eTQ2NsLj8VBBRxAEZGZmwmazoampCX6/XzYpZGdn\nQxAEVFdXIzc3F7m5ubTeG2+8Ee+++y4AYO7cuQiHw7SspKSECsSXX345fD4fOjo6APR9eIPBIIYN\nG4b29nY60K655hrU1NSgt7cXU6dOpcoTAGRlZcFms8Hv98PhcMDj8aC5uRl+vx8+nw/jx4/H1KlT\n8cEHHyAjIwOBQADhcBg9PT0oLy/H9OnTsXfvXuTm5mLZsmX45JNPEAqFEAgEcOrUKTQ0NODqq6/G\nmDFjcODAAToZEGEsGAxi2bJl+PTTT+Hz+dDT00MVAJvNhoKCAtTX16O7uxsnTpxARkYGPv74YwDA\n4sWLcfToUSqYDB8+HN3d3bjiiiswcuRIHDt2DD09PWhtbUV5eTkWLlyI/fv300mMTFTjxo3DFVdc\ngc8//xwAMHv2bJw6dQqCIGDEiBGYOXMm9u7dC5fLhYyMDPqcr776anR0dKC6uhpOpxNut5sqAllZ\nWcjLy6PPhzw3QRDw7//+7/joo4+ogmW32+l55H3o6OhAdnY2enp66EegqKgI5eXlqKmpQUNDA3p7\nexGJRJCRkQGO41BWVoa//vWvAACXyyUT+CsrK+mH7vLLL0d7ezsVKi+77DIEg0H6rubk5FDB62tf\n+xo6OzvR0dGBsWPHorW1lb7jpI/t7e1UOCfXy8vLg81mg8/nQyQSoUJfOBymikwwGEQkEoHT6aQf\nt3A4jKamJtTU1KCnpwd5eXkIhULweDwIBAJobGzEhQsX0NTUhFAoBLvdTgUgUm9bWxtCoRAKCwtl\nAiTP82hsbEQoFMLw4cOp4BEOh9HS0oIvvvgCAFBQUIBAIEDHCFGsiBJMyogwK1XeyAeajC1BEGQf\naoLD4QDP86irq0MoFMIll1yC8+fP02tWVFTg0KFDCIVC+NKXvoQLFy7A7/fTuaW2tpaWAcCFCxdQ\nW1tLjQMAsHz5cgiCQM/NzMykQorNZkNZWRnq6uoQCASo4hCJRFBUVIRhw4bROnt7e/Hpp5/SseF2\nu1FXV0fHXH5+Ptrb21FRUYElS5bg888/R09PD3JycnDFFVfgwIEDCIVCmDVrFnw+Hx1/8+bNw4UL\nFwAAU6ZMQVtbGx0jmZmZ9N7k5eXRdwUAvF4vbSt5/qQsJyeHvnOkDBCNIH6/H21tbRAEIa7OYcOG\nya7vdDppWXFxMVpbW9HY2IhgMAin00mNTZ2dnfTeCIIAr9crE5Ly8/Ph8/nA8zxyc3PpNUkfpb8l\nykrseWRskPOKiorofEGUM/KbnJwcdHd3o6enhwqUZKw1Nzejuroazc3N1DhGxk4kEoHL5aIKW3Fx\nMZ3nnE4neJ5HQ0MDIpEIysrK0NXVRe9Tc3Mzjh8/Tuco8p4SZS4YDMLhcKCgoIAqfKTtRAEm7SfX\nczqdCAaDsNvtVAiVfj97enrg8/ng8Xjo3B0MBlFWVoaZM2fiww8/RCQSwZe//GWcOnWKXueSSy7B\nvn37aFkoFMKJEydw5swZBAIBNDU1ged5XHXVVejt7aVGgby8PKpkcxyH0tJSqtgVFBSgo6MDkUgE\nJSUlKC4upnXyPI/9+/cDABYsWACHw0Hf+fHjx8PhcKCrqwuTJk3CokWLcOTIEarILFmyhH4z58+f\nT5UTnuexatUqHDt2DAAwY8YMNDc30/c+Ly8PNTU1CIfDdC4j97C4uJi2lYwPMicXFRWhs7OTzqPk\nnjudTvh8Pjqv5Ofno7e3l857hYWFMgOFVHGYMGEC6urq6LvscDhgs9nos3r77bcBgF6HfCPIcydz\nZU5ODp13HQ4HioqK0NLSgnA4TMeVIAhwuVwoLi5GZ2en7tghMhMxDHi9XmRkZKC2thbbt29HQ0MD\nQqEQHA4HsrKy4HQ6ce7cOezatQunTp3CTTfdhAULFuCtt97C+fPnMW7cOFRVVdH7IAgCXnrpJeza\ntQvjx4+XlfU3zH1rABAEAdu2bcPy5cuxZs0aVFVVYfPmzcjOzsb27duxbds2LF26FH6/H6+//jo8\nHg9sNpusrLm5GefOnaMCQ3V1NRUkjh07Jivbvn07tSY5nU40NjYiIyMDgCjUzJw5E52dnRg/frxM\nOOR5HjNmzEBXVxfKyspQU1Mjq1dqbTp//jwtA0QLOfm7qakJ9fX19JpTpkyBx+MBIAqE5FpEUSHn\nEcGbnDdx4kS4XC4AQFlZGRU6yfm5ubnYt28fSktL0dbWJrvnPp8Phw4dwowZM1BTU4PXXnuNlhFF\nqry8HH6/H2+++SYts9lsaG1txWWXXQaO47Bz5076QSJlS5cuxcSJE1FbWyuzLJ4/f5725R//+IdM\n0GtubsbSpUuRmZmJf/zjH7LzCgsL8cEHH2DkyJF0wiSTUUdHB/7+978jIyMDpaWl+OSTT2R93L9/\nP6ZPn47m5mZ6f4iSSaxvLS0taGpqkj2PUCiEzMxMeDwe+gx4nse2bdtoPc3NzWhqaoLdLi6wTps2\njZ5H+kbuzdSpU/H973+fChqhUAhOpxMlJSXUwkloa2ujqyAcx2H06NHgeZ5azcnzIYJDb28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fo0/H4/HnzwQQwbNox+VMiH749//CPttzSYlNw7IhRI6wTEScnhcNABLW3PX//6V7hcLlx77bVx\nZS+++CICgQBuu+02cBwns0gWFxcDEC3O0tUmAHR14Omnn6a+tECfRT4rKwvPPvssFUSl/Vi1ahXG\njBkDh8NBV2MITU1NeOCBB7BkyRL4fD5Ze/x+Pw1GJy5hhEAggK985StwuVxxlnoA1CpEno/0vNbW\nVjgcDupKIggCnn76aQDi6s9NN90EQBR6pP0ARKWFfKSkAkJBQQEee+wxNDQ0wOfzxQWDf/755/Rj\nGfs+fuMb36DHpc+ZJHt44IEHZO8OIAqdTz/9NLVWS13M1q5dSxU8n8+H6upqAKAWL+m4+uCDD+h4\niy0DgMOHD9O/6+vraTKG2DISg0POJfFbQN+4dTgcsg88II4rIsw3NDTI+kHelc7OTjQ0NNDrkXtE\nXOR4nsfYsWNlMShkzC1duhT19fXUbxsAfve739HVSbfbLeuH2+2mq4fE1ZRAnltpaSl4npedl5GR\ngV/+8pdwOp246qqrZGVPPfUUuru7cdttt4Hnedm9IRbcwsJCmUsKsdza7Xb88pe/RGNjI86ePSub\nk3JycvDss89Sy6h0zC5fvhyAOIds3LhRprBVV1fjgQcegNPpxOLFi2WCVU9PD9ra2uh7JR2PPp8P\n11xzDVwul+y9IpBVH6Dv3SDvbHt7OxwOB6688kps27aNuoIA4nMkY+7VV19VDLAl741UKBUEAUeO\nHMGFCxfQ0dEhe3fICuoPfvAD2btDuOWWW/DMM8/gvvvukyl6Pp+Prip+/PHH9J0i7SX36rPPPsOx\nY8eoIYe0x+1245lnnsE999wjc6MhY4fjuLgyMnYCgUBcGRk7wWCQuucQpGPnk08+kbnpkbHT1dUV\nVyf5zgQCAVn/n3/+eQDi2CHxoWTlnfC73/0OdrsdPT09OHPmjOyaZOycOXMGP/7xj2XXJPPz9u3b\ncd9998nKyNix2WzYtWuXrIyMndzc3LjzyNj54IMP8JOf/IQeJ8YXu92Op556Cvv27Yv71pOx8+ST\nT+Kee+6R9YOMnR07dqC2tlb2PpKx8/zzz+Nf//qXbM4hYwcQ5yxpnWTstLW14eOPP5aNR0AcO0rv\njc/nowrOO++8Q8teeOEFAOLYcblcuOeee3Dw4ME4OQ3oe67Sb1JeXh7+67/+C59++inOnDkjGzuC\nIOCzzz7DE088EdceQHvsBAIBjB07VjZ2Lly4gLlz52LPnj3UqyQSieDdd9/FuXPncMstt8i+V+Xl\n5XSuJUkQCNKydIApJWmEWgYQtd9qvUhGy6TXJIFn+fn5ceeQD4rf78e5c+fosnRsvV6vl7oIAaKA\n7fF4qLAuvR5xLSLxF1KKioqo8N/V1SULTquoqIDD4cCjjz4Kp9NJ20I+6MOHD9fU+olbl9Z9ieXE\niRO4/fbbqcIg5Tvf+Q5GjRqF73//+2hsbFTNsJGXl/f/t3etMXFd1/qbJ8MAA2YA8xhsD4+xC9hN\nTMCuW8Kf1IoayYnjOokSyVLlOLJKWv9K2igRqeRUbv9UKbYa9aG6cYSlVolL3Qw1TVsnfmEwnnTA\nYPPMPIDxDMwMDON5D3N/cNfy2cDt46q3Tq/O98vy4szZe5+99l577bW+JYzNww8/jNdff51jwWls\nyED81re+ta7RCdwPC5iamhI8MsDKbdlbb72F0tJS1NTUCO2pq6vjw8xqPPPMM8LBQ/qt9Ho9G3Wr\nZaWlpXA4HGhra+NEcOD+dbjZbOaxCYVC614RNzQ0oKGhQfgur776KnQ6HbxeL5LJpHC4OHDgAHJy\ncjj/R9qempoa1NXVAcCaudPS0oKqqiqeO9I8htOnT0Or1aKqqkpIjgSAkydP8pW3x+PhhFCp0ahQ\nKNDV1YVz587xYUUqA4Curi5+Flg5JNXX10OtVq+R5eTkMHnAr371K/boSfurUCjWGLJSnXM6ncIG\nJNW5H//4x/w++m3SudOnT+PixYuCnpPOFRUVoampSfhd0jmFQgGfzyf0Y/X4rCdbb2x++tOf4vbt\n22hra8OlS5cE2be//W1UVVWhvb0dHR0dwthIIR0b0t1jx45Bp9PB6XQiHo8LuRZtbW3Izs7G+fPn\n0dXVJRwuaCwcDgfeeOMNYX5UVlbi+9//Pu7evcs5N1JYLBaeV9L5KNU56byi95HOSceGbtrdbjeP\nzblz54R1m3Sut7cXv/3tb9dl2mpoaEBra6vQngMHDkCv1+P8+fOcvE949tln0d7ejuzsbGHuAPd1\nbu/evXjppZeEtbSlpQUGgwGJRAIvv/wyh4aeOXMGAPims7a2Fo8//riQqwiAHWXt7e1oaGjg/yfd\nycvLWyMj3dHpdDh69Oi6ex4ZnpRXAIi6I12rSUZOskceeUR4H+1Xubm5Qv/pb7RaLUZHR/Hyyy+j\nvr5eMK5Jd7RaLaqrq9fdWzdu3Limj4T1xoZ057nnnlsjI90ZHBzE17/+9XX3vUceeUQYG5oHx44d\nQ3Z2No+NdN0l3QGA9vZ2oR/074aGBmg0GkFGutPY2Ij29naeHwSLxYLKykrhvcB93SkoKMC2bduE\nPpLurDc2tF+tHhvpfiUdm9XORGDltuTZZ58Vbq5ovyInh3RvoP2Kxkbanr+nO1VVVTx3jEYjf4+u\nri5UVlaiurqaD8UtLS1obm7GD37wA96v1vu+n6dDyGrIh5IHAFLI1RvFao+2FFLlp9AaOqVLZZFI\nhBkoSKZQKNgQJJYhgnRDWr0YSn/XZDJBrVYjGAyirKxsXW93NBqFVqsVPOy0MRBzhBRSGTGkECgM\nKBqNchwvgQxHtVrNMdrA/aS1J554gilvpWEbNHY0fqsh3YABkfrx0KFDQg7QaiP4u9/9LrRaLcbG\nxgSDn74jee6kY0PtqK2tFd5FXrRMJoPa2lpeFKWHC/rdP/zhD0wSQHjppZegVCrx/vvvo7y8XPDK\nHT16FIFAALm5uWu+yZ49e/i3V1/xSg03acIgAE7Ebm5uFmQUpnP48GFkZ2djbGwMarVauPKmsdHr\n9fB6vWuoFi0WC9P2Svvf2toKi8UCl8u1pj0UowxgjcF+8OBBvPbaazx3pF6wiooKhEIhuFwuDiMh\nmEwmuN1uJJNJuN1ubN26dY1ezc/P4+zZs9i7dy8nbJIsEong3XffxdmzZ1FTU8Oy0tJSbNq0iZ+l\n3AR6J7By89Xd3S0ciOn7Ly4u4ty5c8JztAYA928/15ONj49zP2j8NBoN5ufnOenxySef5GcrKyuh\nUqnwwx/+EDdu3BDG7oknnoDf70cymYTH4xHaQ+x4MzMza/pIoVU0NlLZ0NAQvvnNb+L27dtrZDU1\nNXjllVeQyWRw5coVYWxofbtw4YIwNqR7xHwTjUaZelvantraWty4cQNnz54VDq3kVDl58iTS6bSQ\nO3DkyBEem7NnzwrG04kTJ/D888/zvJKunXv27EEmk8Hs7CzcbrfwHLHS0diQjNr7wgsv8Nhs3LhR\nuLk6fPgwdDodTp06tS6FbTqdRldXFz755BPhna2traitrYXNZltz0NuzZw+ysrKg0+kwPj4uPEc6\nV1NTw3k8hIMHD+JLX/oSFAoFBgcHmYWNnqeY+rKyMnzjG98QDgm0XxGzovSwX1pais2bNyOdTqOh\noUFgDDKZTDyvP/roozXzEVhZd5qbm5nUAri/JxGjknRdlcrC4bDwPtqv0uk0JiYmeN6Qs62yshI6\nnQ6Dg4N48803hXlF+5Ver4dareYwImprLBZDSUnJmj7SfkVjI5UNDQ1xEvxqWU1NDV599VXk5OSg\np6dHGBvSnbKyMmFspLdwdXV1zEAl7Ucmk4HFYsHi4iIaGhqE9ZPGfGRkBHq9Hk899RTLjhw5ArVa\njRs3bmDbtm3C/nnixAm88MILzF4mvUWg/UqhUDClMYHeXVNTg23btgky2q+efPJJQUZ9OXz4MHJy\ncnDhwgVs3LgRq7G8vIz6+nq4XC7hNgRY2a/S6TS0Wq1gI9B+tbi4uKY9f093XnvtNZ479Lc6nY7p\nzP1+P49ZSUkJ2trasGXLFkxPT3NYoBSrcxI/b5APJQ8AFEvr9XqF//f5fBzus1pG1IckKysrYy7t\n1bKSkhJBVl5ezt4HrVbLsnQ6jQ8//JDfUVRUhIWFBQwPD8Pv9wu/azAYUFBQwBuodJECVsJyAoEA\n88avbo80aXA9GfWRvAlLS0vcDwrtkj5nMpmYl50ogmmzdjqd8Pv9nPchZfTyeDyYm5vjuGKi7QPE\nUI1EIiGE4EiZSEKhEGKxGBv7Xq+XY6xTqZRgWNMV+Pe+9z3YbLY1zwFiQiUlypN8dfgMgZ71+/1M\n50r9CAaDMJvNmJiYwMDAAJLJJG90n332GQKBAEwmE1Mqkszr9WJ5eRlerxeLi4vCc9QOolwl2kpg\nxQvU0tKCRCIBm83G9Vqefvppbg8xaa0++AHA8ePH8eKLL2Jubg5ZWVnCd04kEtDr9ZwzIW0r0UNS\ne6j/NIeoJg8A4bm7d+/CYDDw3KH3FRUVYXx8HNFolFlpSEaUyZlMBl/4whdgNpsFvVpeXkY8Hse+\nffvw4osvCrqs0WgQDAZhtVrxta99TdDt8vJy3LlzB/F4HM3NzTCbzfycz+cT6LmlOQOTk5M8d/Ly\n8pheEgB7uKkmglR3pLLCwkKYzWbk5eWxgUhc+sXFxbBYLAgGg8IakMlkOHSANkaaV5Q7oNPpYDab\neZ7Pzs5iZmYG09PT2L9/vyBLp9Ow2+2wWq3Yv3+/kOj59NNPs6y1tZWZ04AVPf7Zz37GB1Xp2FBI\nTl9fHwwGA7eT9OrnP/85vF4v5witXh+mpqYQDAaxf/9+wUDweDw4deoU3G43iouLUV1dze0h6tqZ\nmRmmZKU5t7i4iLfffhvRaJRzVEjm8Xhw8uRJLC0tcb0UkqVSKQwNDcFqtaK4uBjhcJgPuADw17/+\nFVarFQUFBfD5fKirq+Nv5ff7hZo7q/HWW2+hu7uba0tI2zM5OYnFxUU+dEplp06dgtfrZcOcxm1i\nYgKZTAaJRAIDAwNYXl5mWU9PD7OuTU5Owm63C/lapN+0xq4OWw4Gg7weS/errKwspkfu7e2Fz+dj\n3Xe5XFwrZ3JyUshjI91ZWlrCK6+8Arvdzs85HA7OCaI8tvVkmUwGPp+Pv7/P50NxcTGTjZCnnm7a\nDAYDqqqqMDU1hWvXrgnz6sMPP8T8/DxKS0uZPp0OQxcuXIDX6+X9yufz8aFWSjQDiA6YnTt3cj7q\n2NgYbt26xc/19PRAo9EwHa10bEh3jh8/ju985zuw2+2C8+ru3bvMfkikGISenh6BiUq6n/b09DDD\nVmVlJQKBALfn4sWLMJvNcLlcePPNNwVH4+XLlxEOh3lticfjwrxKp9Pwer0YHR0VnotEIjwfT5w4\nIRCr0NgkEgm88cYbcDqdQv5oIBDg3Kf1bmBpv7p+/Trq6up4fvT09HBuUTKZFNpDY5PJZLg99I3/\nnu54PB4YDAaOijAYDIjFYkgkEhgZGcH8/DznFX7wwQd4/vnnsXnzZiSTSWZ+kyKdTq8JXf88QT6U\nPACUlZXBaDQyHSCwsgHZbDY8/PDDa2TLy8uIRqOCrKGhgZOCSVZQUIC5uTns3r1bkEm9S+QBmp+f\nx9zcHMLhMMtpc7BarRgZGUE0GuU42ng8zgeRLf9N2bp7924+hZPXguoM1NXVcXuIFYU8EeXl5Swj\nDw4VXczOzoZGoxH6QWOwfft2fq6urg4Oh4PrlhCLjtFoxPnz59mgTKfTCIfDyM/Ph9Fo5IWDxo8S\n4hQKBS9AqVQKg4ODvCFptVrhe/h8Pn4nyUKhEGZmZlBSUgKVSoXc3Fyo1Wq+pj169CjTpUqfCwQC\nGBsbY0+vRqPhfly7dg3j4+NsDFJysXR+lJWVcVEu6mNfXx+cTieCwSAikQiUSiX27NnDY6NQKGC3\n29mgq6ur498cHh5m75z0Ofrbjo4ONgAVipVaAzQvOjo6eBNubGzkeX716lVMTU0BAJMw7N69mw3h\nLVu28O9TjQij0YgrV64wOxu9j9pz7do1jI6OIhQKceFIg8Eg1Ibp6Ojgjaa5uZn7ODIywoYJzVWj\n0Yjh4WE+UD722GOIx+Mwm80sIwPvi1/8Imw2m6BX9J6qqirWZdIdKV3j6OiooHMul4vDfWw2G27e\nvNFUhEoAAAssSURBVMm6PD8/z8YAhQrs3r1b8KQCK+EGdruddW5+fl7Q1eXlZda51TKbzYasrCz2\nTpMhUV5eDofDgZs3b3I/XC4X/H4/jEYjBgYGEA6HYTAYYDQacfr0aQAr60AsFkNfXx8fIn75y18y\ny87Bgwdhs9lYFolEMDs7i0OHDmH//v24dOkSrwf9/f24fPkyDh06BIPBgE8//RR6vR4ajQadnZ24\nfPkyNBoNvvzlL2PDhg1MzkEhDrt27cKuXbtgt9uh1+vZy3/r1i2o1Wo0NzcjmUwiFApxP9577z0E\nAgHU19fj4MGDgs7RO7du3YpoNIqbN29yPzo7O9kRQvrR2NjIz/X29gpzgeZcZ2cn06lSvRSa4/fu\n3YPH40FlZSX8fj8nFr/++uvcj8rKSiwuLnJCK+nOe++9x95Y6dyR3m5QUcm8vDx+55kzZxAMBpGf\nn89OkOrqaqH/FJNuMBhgMBigVqthtVoxPDwsrAEUJmi1Wtl4TyaTsNvtzIoI3M8pISIPp9PJRhNR\n7vb39+PEiRNYWlriPcfpdHKNlpMnT+LmzZvQaDRQKpUIh8Osr9T/vLy8NWErGzZsYMObniOnYXFx\nMVMFk4wOzTk5ObDZbHwoCIfDrGt0kJL2cWFhAQ6HAzqdDm+//TbH+xsMBvzxj39k5qtgMIilpSUO\nx+nu7kYsFmP6dZvNxn1wOBxMxx2LxVh3AGBgYIBlfX19Qqid1WrFwMAA3G43687qyIcjR47AYrFw\naDAZyVarFaOjo6ivr2dyGeqH1WrF7du3YbFYuP4XAJ4Dw8PDKCsr43WF+mG1WjE+Po7FxUVeC6Vz\np7OzE5lMhtcmqh9itVp5fLxeLwwGAzMqEllJR0cHhoeHBcp9GpuOjg7el4j0A1jJe5mamoJCocAz\nzzwDtVoNvV7Ph/SNGzcK1Lo056j/wP1cKGoPyUKhEIaHh2EwGIT+/y3d+ctf/oLJyUkolUrWHZPJ\nBKPRyHvB448/DmCFae748eO4ffs25wFJb/s8Hg/S6fS6IfqfF8jFEx8AiFXngw8+YE/+mTNnMDs7\ni7a2NhQUFAiyCxcuIBqN4tixYyyjq/3R0VFkMhm0trZieHgY8XgcmzdvFmSPPvooTp8+zXUcysvL\n8dlnn3GtBvI+zM7Oori4GA6Hg73QCwsL0Gg0mJmZgUqlgtls5mJ9e/fuZWNt06ZN0Ov1fF1NXuhU\nKsXJpOQZo0JCqVQK09PTiMfj7NkgKr90Os39oGTVSCTCMfZTU1N8bUlUjFQBd2hoCGazGdnZ2Zib\nm8Py8jKMRiMsFguuX7+O0tJSbN26lce1urqaC1GmUilcu3ZNKLCVlZWFoaEh+Hw+hEIhLmpIRvnQ\n0BA+/vhjTvCOxWKoqKhAbm4uL7Lbtm3DyMgIh4Dk5+fzc7SxxmIxvvrdvn07/vznP7OXe3Z2FqlU\nCoWFhXjqqad4flRUVPABha74//SnPyEajbKBTbSskUgEY2NjKCwshMPh4KrYi4uLqKqqwtWrV3H1\n6lW+dUmn09i3bx9ycnIwMjKCYDCIiYkJDotrbGxERUUFLl68CJvNxsbe8vIyEokEkskkkskkent7\nkclk2MOTSqVw4MABZDIZOJ1OLCwscMHETCbDuSW9vb1QKpW8SWcyGVRVVUGv1+Pq1atIp9NYWlri\ntqpUKq7dcufOHXz66afsmU4kEti0aRMuX77MRAk0xxOJBIcYASsG8Z07d/hgL739omv0WCwGi8WC\nd999lzeooqIiXLp0CZ988gkCgQAWFhYQj8c5ydfpdLKH8N69e1AqlQiFQigoKOCbG2KEcbvdbOBU\nVFSgr68PyWQSO3bswMTEBBu3JSUlHA4XCAS44BsZWRRXPD8/z/TGRAJBxQiJ2UZaYMztdvOYUG0a\nujlsaWnhXBWDwYC8vDx4PB5kZ2fjK1/5CsbGxhAOh9l4pCrSTU1N6OzshNfrZVa0SCSC7Oxs1NXV\n4Sc/+QnC4TByc3Oh1Wrh8/lQUFCAwsJC2O12vqmiWksUtqhQKBAIBJjBjsKY6uvrMTk5iWAwiHQ6\njXg8zn3QaDRoamrC1NQUk4bU1tZiZGQEwEq4xa9//Wu+idyxYwcGBwdRUlKCnTt34tatW4hEImw8\nSxnjqD5BNBqFwWBg+nJg5daBin8WFhYK+WJ5eXmIxWJobGxEIpGAx+PhAptarRbJZBJbt25FIBDg\ndSUUCrFjIhKJoKKiAplMho2tTZs2we12IxAIMAuT3+9HTk4Oh+wlk0mUlZUhFovx+k1FKqmQ4YYN\nG+B2u7miNDlYaG2lOi9ut5s9xfF4HLW1tfD7/ewoIMOSnB6U+K/VajE4OAir1Yp4PM71iIAVQ7K/\nv5+/KxG5pFIp2O125OfncxFGIvIgh1NJSQlu3LjBXnYpa2JhYSF70OPxOD/ndDoF3ZEWRiRdu3v3\nLusayWZmZlBUVMT7I93wU8HTSCSCHTt28A0H3bbNzs5CpVKhpKQETqeTi1bW19fzzQhRapNRSQZt\nf38/lpaW8Itf/EKg7o7H47h+/TrcbjdGRkb4VpwK+/X29vKaSnuktIBrVlYWHA4HFzYuKiqCSqWC\ny+WCUqnkdYjyUEmXUqkUKisrce7cOf5ee/fuxeTkJPr7+1FeXo6xsTFm5aT2kCNMWvncYrHA7/fz\nmJMskUhwcVwqBpqbm8v2TVNTExdTnZmZYVprYIXpbG5uDleuXMH09DTPB3pubm6OC8OaTCYMDQ0h\nFoth3759yM/Ph8vlwr1791BaWopIJMLvrK6uRiAQwPLyMiKRCFQqFe8rpAPEdEe6l06nUVpainA4\n/Dd1hxxoxFZKt9EURUIEQ0ReQgQwVIT21q1byMvLg8/nwzvvvMOO6vUKJHZ3d6OsrOyBFk9UZP6Z\n7GoZ/1L8/ve/R3d3N1PCHTp0iL2SUplOp+P44NUyooPT6/XYvn07TCYTPv7443Vl4+PjKC4uxtzc\nnBB//LegUqmEq15aTOh3q6urMTQ0xAYnyaTF9GgjA8RQpdUJ5+TZMplM2LVr15p+0N+SUQWAmYJo\nYTMajSgvL8f09DSCwSC3nxSUYq/D4TBTTz700EMYHx9HeXk5fD4fAoHA/0g6oFQqOR8jHo9zvgMt\n8MSWQp65kpISdHd3M4uSdLGSjg3dntDCpVKpuMBeKBSCWq1GIpGAyWTCj370I2EOUOV2pVKJnJwc\n5OXlcUXn9UBt/Xfhf/s+tVrNNV3+1e+kOZhKpfgg/K+GUqmEVqtdN8n4Pw1SOur/r1gv7+0fkcn4\n5yBlATIYDExlSvqq0Wiwc+dOjI+PY8OGDey4+kehUqm4ijatHQqFAlqtlmvmSG8ViNyB2iXNQ6T/\npzWCcqBIRsbrQw89hI8++ojbKe0jORBpfyQnFBVSparq5Oii383NzYXRaGS2KoVCwUxJs7Oz2LJl\nC1wu1z80NhTmRmG3dGiT/qbRaITRaOTK5QqFAqWlpQgGg1zvSK1Wsw1AjHq031MdLXISZjIrNNnv\nvPMO3n//ffzud7/jtup0Or5lKSoq4oPRfzKkh+H/K0jnVX5+Pr761a/i0UcfFW5jiT1s165deO65\n5/Cb3/wG169fRyaTYTuwqakJhw8fXvP7bW1t2Llz57qyfxfkQ4kMGTJkyJAhQ4YMGTIeKOScEhky\nZMiQIUOGDBkyZDxQyIcSGTJkyJAhQ4YMGTJkPFDIhxIZMmTIkCFDhgwZMmQ8UMiHEhkyZMiQIUOG\nDBkyZDxQyIcSGTJkyJAhQ4YMGTJkPFDIhxIZMmTIkCFDhgwZMmQ8UMiHEhkyZMiQIUOGDBkyZDxQ\nyIcSGTJkyJAhQ4YMGTJkPFD8F/ED+bqb83nTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a352928d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_merge['timing'], df_merge['duration_sec'], '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like that one trip is really throwing us off here. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121168</th>\n",
       "      <td>811077</td>\n",
       "      <td>2020-01-09 16:23:21</td>\n",
       "      <td>2020-01-19 01:41:19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.336035</td>\n",
       "      <td>-121.899836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.336549</td>\n",
       "      <td>-121.900961</td>\n",
       "      <td>231296</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>clipper</td>\n",
       "      <td>13517.950000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>0109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121168</th>\n",
       "      <td>1373</td>\n",
       "      <td>2020-02-18 16:44:54</td>\n",
       "      <td>2020-02-18 17:07:47</td>\n",
       "      <td>312.0</td>\n",
       "      <td>San Jose Diridon Station</td>\n",
       "      <td>37.329732</td>\n",
       "      <td>-121.901782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.361640</td>\n",
       "      <td>-121.896811</td>\n",
       "      <td>857401</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>22.883333</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>18</td>\n",
       "      <td>0218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121168</th>\n",
       "      <td>345</td>\n",
       "      <td>2020-03-15 17:16:12</td>\n",
       "      <td>2020-03-15 17:21:57</td>\n",
       "      <td>375.0</td>\n",
       "      <td>Grove St at Masonic Ave</td>\n",
       "      <td>37.774836</td>\n",
       "      <td>-122.446546</td>\n",
       "      <td>440.0</td>\n",
       "      <td>Carl St at Cole St</td>\n",
       "      <td>37.765910</td>\n",
       "      <td>-122.449257</td>\n",
       "      <td>156552</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>0315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec          start_time             end_time  \\\n",
       "121168        811077 2020-01-09 16:23:21  2020-01-19 01:41:19   \n",
       "121168          1373 2020-02-18 16:44:54  2020-02-18 17:07:47   \n",
       "121168           345 2020-03-15 17:16:12  2020-03-15 17:21:57   \n",
       "\n",
       "        start_station_id        start_station_name  start_station_latitude  \\\n",
       "121168               NaN                       NaN               37.336035   \n",
       "121168             312.0  San Jose Diridon Station               37.329732   \n",
       "121168             375.0   Grove St at Masonic Ave               37.774836   \n",
       "\n",
       "        start_station_longitude  end_station_id    end_station_name  \\\n",
       "121168              -121.899836             NaN                 NaN   \n",
       "121168              -121.901782             NaN                 NaN   \n",
       "121168              -122.446546           440.0  Carl St at Cole St   \n",
       "\n",
       "        end_station_latitude  end_station_longitude  bike_id   user_type  \\\n",
       "121168             37.336549            -121.900961   231296  Subscriber   \n",
       "121168             37.361640            -121.896811   857401  Subscriber   \n",
       "121168             37.765910            -122.449257   156552  Subscriber   \n",
       "\n",
       "       rental_access_method  duration_min  start_year  start_month  start_day  \\\n",
       "121168              clipper  13517.950000        2020            1          9   \n",
       "121168                  app     22.883333        2020            2         18   \n",
       "121168                  app      5.750000        2020            3         15   \n",
       "\n",
       "       timing  \n",
       "121168   0109  \n",
       "121168   0218  \n",
       "121168   0315  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.loc[df_merge['duration_sec'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df_merge = df_merge[df_merge.duration_sec != 811077]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>timing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83118</td>\n",
       "      <td>2020-01-31 15:23:47.733</td>\n",
       "      <td>2020-02-01 14:29:06.2630</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>13052</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1385.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68239</td>\n",
       "      <td>2020-01-31 15:40:31.616</td>\n",
       "      <td>2020-02-01 10:37:51.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>12235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1137.316667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55407</td>\n",
       "      <td>2020-01-31 17:48:16.635</td>\n",
       "      <td>2020-02-01 09:11:44.3170</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>12822</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>923.450000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54929</td>\n",
       "      <td>2020-01-31 17:53:03.413</td>\n",
       "      <td>2020-02-01 09:08:32.6340</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>11705</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>915.483333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55700</td>\n",
       "      <td>2020-01-31 17:12:33.460</td>\n",
       "      <td>2020-02-01 08:40:53.6460</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Lombard St at Columbus Ave</td>\n",
       "      <td>37.802746</td>\n",
       "      <td>-122.413579</td>\n",
       "      <td>3673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>928.333333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11333</td>\n",
       "      <td>2020-01-31 22:48:22.639</td>\n",
       "      <td>2020-02-01 01:57:15.7160</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>12137</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188.883333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11341</td>\n",
       "      <td>2020-01-31 22:48:12.989</td>\n",
       "      <td>2020-02-01 01:57:14.1650</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>12321</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.016667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4038</td>\n",
       "      <td>2020-01-31 23:32:03.907</td>\n",
       "      <td>2020-02-01 00:39:22.0210</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>1589</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4059</td>\n",
       "      <td>2020-01-31 23:31:01.161</td>\n",
       "      <td>2020-02-01 00:38:40.8570</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>12884</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1980</td>\n",
       "      <td>2020-01-31 23:49:09.230</td>\n",
       "      <td>2020-02-01 00:22:09.7540</td>\n",
       "      <td>238.0</td>\n",
       "      <td>MLK Jr Way at University Ave</td>\n",
       "      <td>37.871719</td>\n",
       "      <td>-122.273068</td>\n",
       "      <td>198.0</td>\n",
       "      <td>Snow Park</td>\n",
       "      <td>37.807813</td>\n",
       "      <td>-122.264496</td>\n",
       "      <td>10464</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1969</td>\n",
       "      <td>2020-01-31 23:49:03.972</td>\n",
       "      <td>2020-02-01 00:21:53.5730</td>\n",
       "      <td>238.0</td>\n",
       "      <td>MLK Jr Way at University Ave</td>\n",
       "      <td>37.871719</td>\n",
       "      <td>-122.273068</td>\n",
       "      <td>198.0</td>\n",
       "      <td>Snow Park</td>\n",
       "      <td>37.807813</td>\n",
       "      <td>-122.264496</td>\n",
       "      <td>12120</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.816667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1611</td>\n",
       "      <td>2020-01-31 23:49:10.931</td>\n",
       "      <td>2020-02-01 00:16:02.4310</td>\n",
       "      <td>174.0</td>\n",
       "      <td>Shattuck Ave at 51st St</td>\n",
       "      <td>37.836801</td>\n",
       "      <td>-122.264004</td>\n",
       "      <td>259.0</td>\n",
       "      <td>Addison St at Fourth St</td>\n",
       "      <td>37.866249</td>\n",
       "      <td>-122.299371</td>\n",
       "      <td>838</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.850000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1133</td>\n",
       "      <td>2020-01-31 23:56:49.475</td>\n",
       "      <td>2020-02-01 00:15:42.9960</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>13068</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.883333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1119</td>\n",
       "      <td>2020-01-31 23:57:02.284</td>\n",
       "      <td>2020-02-01 00:15:42.0630</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>12801</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1110</td>\n",
       "      <td>2020-01-31 23:57:09.019</td>\n",
       "      <td>2020-02-01 00:15:39.4330</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>9916</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1257</td>\n",
       "      <td>2020-01-31 23:51:43.435</td>\n",
       "      <td>2020-02-01 00:12:40.5780</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Telegraph Ave at Ashby Ave</td>\n",
       "      <td>37.855956</td>\n",
       "      <td>-122.259795</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Telegraph Ave at Ashby Ave</td>\n",
       "      <td>37.855956</td>\n",
       "      <td>-122.259795</td>\n",
       "      <td>12914</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.950000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1247</td>\n",
       "      <td>2020-01-31 23:50:06.665</td>\n",
       "      <td>2020-02-01 00:10:53.7830</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>9672</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.783333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>803</td>\n",
       "      <td>2020-01-31 23:56:51.379</td>\n",
       "      <td>2020-02-01 00:10:15.3390</td>\n",
       "      <td>241.0</td>\n",
       "      <td>Ashby BART Station</td>\n",
       "      <td>37.852477</td>\n",
       "      <td>-122.270213</td>\n",
       "      <td>252.0</td>\n",
       "      <td>Channing Way at Shattuck Ave</td>\n",
       "      <td>37.865847</td>\n",
       "      <td>-122.267443</td>\n",
       "      <td>3582</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.383333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1147</td>\n",
       "      <td>2020-01-31 23:46:05.956</td>\n",
       "      <td>2020-02-01 00:05:13.3540</td>\n",
       "      <td>482.0</td>\n",
       "      <td>Brannan St at 7th St</td>\n",
       "      <td>37.772658</td>\n",
       "      <td>-122.403681</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>12711</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.116667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>777</td>\n",
       "      <td>2020-01-31 23:47:40.692</td>\n",
       "      <td>2020-02-01 00:00:38.5070</td>\n",
       "      <td>368.0</td>\n",
       "      <td>Myrtle St at Polk St</td>\n",
       "      <td>37.785434</td>\n",
       "      <td>-122.419622</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Folsom St at 19th St</td>\n",
       "      <td>37.760594</td>\n",
       "      <td>-122.414817</td>\n",
       "      <td>2104</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>435</td>\n",
       "      <td>2020-01-31 23:51:06.263</td>\n",
       "      <td>2020-01-31 23:58:22.2370</td>\n",
       "      <td>220.0</td>\n",
       "      <td>San Pablo Ave at MLK Jr Way</td>\n",
       "      <td>37.811351</td>\n",
       "      <td>-122.273422</td>\n",
       "      <td>230.0</td>\n",
       "      <td>14th St at Mandela Pkwy</td>\n",
       "      <td>37.810743</td>\n",
       "      <td>-122.291415</td>\n",
       "      <td>3645</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>535</td>\n",
       "      <td>2020-01-31 23:47:01.549</td>\n",
       "      <td>2020-01-31 23:55:57.5240</td>\n",
       "      <td>340.0</td>\n",
       "      <td>Harmon St at Adeline St</td>\n",
       "      <td>37.849735</td>\n",
       "      <td>-122.270582</td>\n",
       "      <td>269.0</td>\n",
       "      <td>Telegraph Ave at Carleton St</td>\n",
       "      <td>37.862320</td>\n",
       "      <td>-122.258801</td>\n",
       "      <td>11447</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>377</td>\n",
       "      <td>2020-01-31 23:48:30.312</td>\n",
       "      <td>2020-01-31 23:54:48.1000</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Mission Dolores Park</td>\n",
       "      <td>37.761420</td>\n",
       "      <td>-122.426435</td>\n",
       "      <td>9963</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.283333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>873</td>\n",
       "      <td>2020-01-31 23:40:11.939</td>\n",
       "      <td>2020-01-31 23:54:45.6910</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>321.0</td>\n",
       "      <td>Folsom St at 5th St</td>\n",
       "      <td>37.780645</td>\n",
       "      <td>-122.402866</td>\n",
       "      <td>2911</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>554</td>\n",
       "      <td>2020-01-31 23:43:48.432</td>\n",
       "      <td>2020-01-31 23:53:03.2580</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>459.0</td>\n",
       "      <td>Utah St at 24th St</td>\n",
       "      <td>37.753123</td>\n",
       "      <td>-122.405393</td>\n",
       "      <td>10133</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.233333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>525</td>\n",
       "      <td>2020-01-31 23:43:50.111</td>\n",
       "      <td>2020-01-31 23:52:35.6630</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>459.0</td>\n",
       "      <td>Utah St at 24th St</td>\n",
       "      <td>37.753123</td>\n",
       "      <td>-122.405393</td>\n",
       "      <td>239</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>180</td>\n",
       "      <td>2020-01-31 23:49:07.194</td>\n",
       "      <td>2020-01-31 23:52:07.8770</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Haste St at Telegraph Ave</td>\n",
       "      <td>37.866043</td>\n",
       "      <td>-122.258804</td>\n",
       "      <td>252.0</td>\n",
       "      <td>Channing Way at Shattuck Ave</td>\n",
       "      <td>37.865847</td>\n",
       "      <td>-122.267443</td>\n",
       "      <td>12276</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>761</td>\n",
       "      <td>2020-01-31 23:39:05.307</td>\n",
       "      <td>2020-01-31 23:51:46.5050</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>351.0</td>\n",
       "      <td>10th St at University Ave</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.293400</td>\n",
       "      <td>3050</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.683333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>749</td>\n",
       "      <td>2020-01-31 23:39:11.393</td>\n",
       "      <td>2020-01-31 23:51:41.2990</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>351.0</td>\n",
       "      <td>10th St at University Ave</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.293400</td>\n",
       "      <td>3256</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.483333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>624</td>\n",
       "      <td>2020-01-31 23:38:52.841</td>\n",
       "      <td>2020-01-31 23:49:17.8300</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Fifth St at Delaware St</td>\n",
       "      <td>37.870407</td>\n",
       "      <td>-122.299676</td>\n",
       "      <td>1947</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176769</th>\n",
       "      <td>1090</td>\n",
       "      <td>2020-03-04 19:05:51.000</td>\n",
       "      <td>2020-03-04 19:24:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809134</td>\n",
       "      <td>-122.415192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784234</td>\n",
       "      <td>-122.388862</td>\n",
       "      <td>557816</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176770</th>\n",
       "      <td>1183</td>\n",
       "      <td>2020-03-01 12:26:56.000</td>\n",
       "      <td>2020-03-01 12:46:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809138</td>\n",
       "      <td>-122.415689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806195</td>\n",
       "      <td>-122.405260</td>\n",
       "      <td>866334</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>19.716667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176771</th>\n",
       "      <td>1025</td>\n",
       "      <td>2020-03-04 19:06:07.000</td>\n",
       "      <td>2020-03-04 19:23:13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809140</td>\n",
       "      <td>-122.415247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784262</td>\n",
       "      <td>-122.388924</td>\n",
       "      <td>886830</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>17.083333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176772</th>\n",
       "      <td>443</td>\n",
       "      <td>2020-03-02 22:07:56.000</td>\n",
       "      <td>2020-03-02 22:15:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809149</td>\n",
       "      <td>-122.415776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.796818</td>\n",
       "      <td>-122.405370</td>\n",
       "      <td>240222</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>7.383333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176773</th>\n",
       "      <td>720</td>\n",
       "      <td>2020-03-21 19:18:05.000</td>\n",
       "      <td>2020-03-21 19:30:06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809152</td>\n",
       "      <td>-122.418323</td>\n",
       "      <td>478.0</td>\n",
       "      <td>Greenwich St at Franklin St</td>\n",
       "      <td>37.800254</td>\n",
       "      <td>-122.425919</td>\n",
       "      <td>438673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>clipper</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176774</th>\n",
       "      <td>317</td>\n",
       "      <td>2020-03-16 16:02:59.000</td>\n",
       "      <td>2020-03-16 16:08:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809153</td>\n",
       "      <td>-122.415734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806566</td>\n",
       "      <td>-122.420714</td>\n",
       "      <td>721149</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.283333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176775</th>\n",
       "      <td>787</td>\n",
       "      <td>2020-03-21 19:16:56.000</td>\n",
       "      <td>2020-03-21 19:30:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809165</td>\n",
       "      <td>-122.418325</td>\n",
       "      <td>478.0</td>\n",
       "      <td>Greenwich St at Franklin St</td>\n",
       "      <td>37.800254</td>\n",
       "      <td>-122.425919</td>\n",
       "      <td>585226</td>\n",
       "      <td>Customer</td>\n",
       "      <td>clipper</td>\n",
       "      <td>13.116667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176776</th>\n",
       "      <td>1663</td>\n",
       "      <td>2020-03-04 14:55:02.000</td>\n",
       "      <td>2020-03-04 15:22:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809172</td>\n",
       "      <td>-122.415235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.766544</td>\n",
       "      <td>-122.398083</td>\n",
       "      <td>451085</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>27.716667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176777</th>\n",
       "      <td>1018</td>\n",
       "      <td>2020-03-01 12:29:54.000</td>\n",
       "      <td>2020-03-01 12:46:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809176</td>\n",
       "      <td>-122.415754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806158</td>\n",
       "      <td>-122.405295</td>\n",
       "      <td>572370</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>16.966667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176778</th>\n",
       "      <td>877</td>\n",
       "      <td>2020-03-05 10:17:10.000</td>\n",
       "      <td>2020-03-05 10:31:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809177</td>\n",
       "      <td>-122.415263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.808295</td>\n",
       "      <td>-122.415003</td>\n",
       "      <td>454861</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176779</th>\n",
       "      <td>492</td>\n",
       "      <td>2020-03-08 10:13:01.000</td>\n",
       "      <td>2020-03-08 10:21:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809197</td>\n",
       "      <td>-122.412139</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>517726</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176780</th>\n",
       "      <td>1100</td>\n",
       "      <td>2020-03-13 13:15:28.000</td>\n",
       "      <td>2020-03-13 13:33:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809579</td>\n",
       "      <td>-122.410255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.805522</td>\n",
       "      <td>-122.422260</td>\n",
       "      <td>331159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176781</th>\n",
       "      <td>1115</td>\n",
       "      <td>2020-03-02 20:29:04.000</td>\n",
       "      <td>2020-03-02 20:47:39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809675</td>\n",
       "      <td>-122.409954</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>888753</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>18.583333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176782</th>\n",
       "      <td>476</td>\n",
       "      <td>2020-03-08 16:17:59.000</td>\n",
       "      <td>2020-03-08 16:25:56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809718</td>\n",
       "      <td>-122.409923</td>\n",
       "      <td>487.0</td>\n",
       "      <td>Powell St at Columbus Ave</td>\n",
       "      <td>37.800496</td>\n",
       "      <td>-122.410887</td>\n",
       "      <td>420959</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>7.933333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176783</th>\n",
       "      <td>5650</td>\n",
       "      <td>2020-03-22 17:09:35.000</td>\n",
       "      <td>2020-03-22 18:43:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809927</td>\n",
       "      <td>-122.476421</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>539344</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>94.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176784</th>\n",
       "      <td>1429</td>\n",
       "      <td>2020-03-28 16:52:56.000</td>\n",
       "      <td>2020-03-28 17:16:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810162</td>\n",
       "      <td>-122.412339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.787604</td>\n",
       "      <td>-122.393953</td>\n",
       "      <td>473795</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>23.816667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176785</th>\n",
       "      <td>1416</td>\n",
       "      <td>2020-03-28 16:53:19.000</td>\n",
       "      <td>2020-03-28 17:16:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810173</td>\n",
       "      <td>-122.412357</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1st St at Folsom St</td>\n",
       "      <td>37.787290</td>\n",
       "      <td>-122.394380</td>\n",
       "      <td>229168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176786</th>\n",
       "      <td>804</td>\n",
       "      <td>2020-03-17 15:25:02.000</td>\n",
       "      <td>2020-03-17 15:38:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810488</td>\n",
       "      <td>-122.476507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.804016</td>\n",
       "      <td>-122.448654</td>\n",
       "      <td>942930</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176787</th>\n",
       "      <td>2547</td>\n",
       "      <td>2020-03-17 15:23:09.000</td>\n",
       "      <td>2020-03-17 16:05:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810513</td>\n",
       "      <td>-122.476495</td>\n",
       "      <td>512.0</td>\n",
       "      <td>Divisadero St at Clay St</td>\n",
       "      <td>37.789688</td>\n",
       "      <td>-122.440688</td>\n",
       "      <td>401842</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>42.450000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176788</th>\n",
       "      <td>131</td>\n",
       "      <td>2020-03-17 15:16:10.000</td>\n",
       "      <td>2020-03-17 15:18:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810528</td>\n",
       "      <td>-122.476450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810528</td>\n",
       "      <td>-122.476488</td>\n",
       "      <td>942930</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>2.183333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176789</th>\n",
       "      <td>192</td>\n",
       "      <td>2020-03-23 16:17:02.000</td>\n",
       "      <td>2020-03-23 16:20:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.840358</td>\n",
       "      <td>-122.267302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848314</td>\n",
       "      <td>-122.268623</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176790</th>\n",
       "      <td>63</td>\n",
       "      <td>2020-03-23 11:31:18.000</td>\n",
       "      <td>2020-03-23 11:32:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848441</td>\n",
       "      <td>-122.265775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848441</td>\n",
       "      <td>-122.265775</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176791</th>\n",
       "      <td>310</td>\n",
       "      <td>2020-03-23 16:11:17.000</td>\n",
       "      <td>2020-03-23 16:16:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848457</td>\n",
       "      <td>-122.266072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.840393</td>\n",
       "      <td>-122.267316</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>clipper</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176792</th>\n",
       "      <td>78</td>\n",
       "      <td>2020-04-01 13:27:39.000</td>\n",
       "      <td>2020-04-01 13:28:58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848478</td>\n",
       "      <td>-122.266082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266023</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176793</th>\n",
       "      <td>165</td>\n",
       "      <td>2020-04-01 12:53:13.000</td>\n",
       "      <td>2020-04-01 12:55:58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848479</td>\n",
       "      <td>-122.266063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848602</td>\n",
       "      <td>-122.265745</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176794</th>\n",
       "      <td>61</td>\n",
       "      <td>2020-04-01 10:42:35.000</td>\n",
       "      <td>2020-04-01 10:43:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848493</td>\n",
       "      <td>-122.265797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848476</td>\n",
       "      <td>-122.265779</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176795</th>\n",
       "      <td>78</td>\n",
       "      <td>2020-03-18 17:10:42.000</td>\n",
       "      <td>2020-03-18 17:12:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848499</td>\n",
       "      <td>-122.265794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848477</td>\n",
       "      <td>-122.265815</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176796</th>\n",
       "      <td>122</td>\n",
       "      <td>2020-04-01 13:32:28.000</td>\n",
       "      <td>2020-04-01 13:34:31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848507</td>\n",
       "      <td>-122.266010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848485</td>\n",
       "      <td>-122.266063</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.033333</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176797</th>\n",
       "      <td>340</td>\n",
       "      <td>2020-04-01 13:07:34.000</td>\n",
       "      <td>2020-04-01 13:13:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848530</td>\n",
       "      <td>-122.266013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266029</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176798</th>\n",
       "      <td>333</td>\n",
       "      <td>2020-03-23 16:39:57.000</td>\n",
       "      <td>2020-03-23 16:45:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848625</td>\n",
       "      <td>-122.266660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848446</td>\n",
       "      <td>-122.266022</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>905006 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                  end_time  \\\n",
       "0              83118 2020-01-31 15:23:47.733  2020-02-01 14:29:06.2630   \n",
       "1              68239 2020-01-31 15:40:31.616  2020-02-01 10:37:51.0000   \n",
       "2              55407 2020-01-31 17:48:16.635  2020-02-01 09:11:44.3170   \n",
       "3              54929 2020-01-31 17:53:03.413  2020-02-01 09:08:32.6340   \n",
       "4              55700 2020-01-31 17:12:33.460  2020-02-01 08:40:53.6460   \n",
       "5              11333 2020-01-31 22:48:22.639  2020-02-01 01:57:15.7160   \n",
       "6              11341 2020-01-31 22:48:12.989  2020-02-01 01:57:14.1650   \n",
       "7               4038 2020-01-31 23:32:03.907  2020-02-01 00:39:22.0210   \n",
       "8               4059 2020-01-31 23:31:01.161  2020-02-01 00:38:40.8570   \n",
       "9               1980 2020-01-31 23:49:09.230  2020-02-01 00:22:09.7540   \n",
       "10              1969 2020-01-31 23:49:03.972  2020-02-01 00:21:53.5730   \n",
       "11              1611 2020-01-31 23:49:10.931  2020-02-01 00:16:02.4310   \n",
       "12              1133 2020-01-31 23:56:49.475  2020-02-01 00:15:42.9960   \n",
       "13              1119 2020-01-31 23:57:02.284  2020-02-01 00:15:42.0630   \n",
       "14              1110 2020-01-31 23:57:09.019  2020-02-01 00:15:39.4330   \n",
       "15              1257 2020-01-31 23:51:43.435  2020-02-01 00:12:40.5780   \n",
       "16              1247 2020-01-31 23:50:06.665  2020-02-01 00:10:53.7830   \n",
       "17               803 2020-01-31 23:56:51.379  2020-02-01 00:10:15.3390   \n",
       "18              1147 2020-01-31 23:46:05.956  2020-02-01 00:05:13.3540   \n",
       "19               777 2020-01-31 23:47:40.692  2020-02-01 00:00:38.5070   \n",
       "20               435 2020-01-31 23:51:06.263  2020-01-31 23:58:22.2370   \n",
       "21               535 2020-01-31 23:47:01.549  2020-01-31 23:55:57.5240   \n",
       "22               377 2020-01-31 23:48:30.312  2020-01-31 23:54:48.1000   \n",
       "23               873 2020-01-31 23:40:11.939  2020-01-31 23:54:45.6910   \n",
       "24               554 2020-01-31 23:43:48.432  2020-01-31 23:53:03.2580   \n",
       "25               525 2020-01-31 23:43:50.111  2020-01-31 23:52:35.6630   \n",
       "26               180 2020-01-31 23:49:07.194  2020-01-31 23:52:07.8770   \n",
       "27               761 2020-01-31 23:39:05.307  2020-01-31 23:51:46.5050   \n",
       "28               749 2020-01-31 23:39:11.393  2020-01-31 23:51:41.2990   \n",
       "29               624 2020-01-31 23:38:52.841  2020-01-31 23:49:17.8300   \n",
       "...              ...                     ...                       ...   \n",
       "176769          1090 2020-03-04 19:05:51.000       2020-03-04 19:24:01   \n",
       "176770          1183 2020-03-01 12:26:56.000       2020-03-01 12:46:40   \n",
       "176771          1025 2020-03-04 19:06:07.000       2020-03-04 19:23:13   \n",
       "176772           443 2020-03-02 22:07:56.000       2020-03-02 22:15:20   \n",
       "176773           720 2020-03-21 19:18:05.000       2020-03-21 19:30:06   \n",
       "176774           317 2020-03-16 16:02:59.000       2020-03-16 16:08:17   \n",
       "176775           787 2020-03-21 19:16:56.000       2020-03-21 19:30:03   \n",
       "176776          1663 2020-03-04 14:55:02.000       2020-03-04 15:22:46   \n",
       "176777          1018 2020-03-01 12:29:54.000       2020-03-01 12:46:53   \n",
       "176778           877 2020-03-05 10:17:10.000       2020-03-05 10:31:48   \n",
       "176779           492 2020-03-08 10:13:01.000       2020-03-08 10:21:14   \n",
       "176780          1100 2020-03-13 13:15:28.000       2020-03-13 13:33:48   \n",
       "176781          1115 2020-03-02 20:29:04.000       2020-03-02 20:47:39   \n",
       "176782           476 2020-03-08 16:17:59.000       2020-03-08 16:25:56   \n",
       "176783          5650 2020-03-22 17:09:35.000       2020-03-22 18:43:46   \n",
       "176784          1429 2020-03-28 16:52:56.000       2020-03-28 17:16:45   \n",
       "176785          1416 2020-03-28 16:53:19.000       2020-03-28 17:16:55   \n",
       "176786           804 2020-03-17 15:25:02.000       2020-03-17 15:38:27   \n",
       "176787          2547 2020-03-17 15:23:09.000       2020-03-17 16:05:36   \n",
       "176788           131 2020-03-17 15:16:10.000       2020-03-17 15:18:21   \n",
       "176789           192 2020-03-23 16:17:02.000       2020-03-23 16:20:15   \n",
       "176790            63 2020-03-23 11:31:18.000       2020-03-23 11:32:21   \n",
       "176791           310 2020-03-23 16:11:17.000       2020-03-23 16:16:27   \n",
       "176792            78 2020-04-01 13:27:39.000       2020-04-01 13:28:58   \n",
       "176793           165 2020-04-01 12:53:13.000       2020-04-01 12:55:58   \n",
       "176794            61 2020-04-01 10:42:35.000       2020-04-01 10:43:37   \n",
       "176795            78 2020-03-18 17:10:42.000       2020-03-18 17:12:01   \n",
       "176796           122 2020-04-01 13:32:28.000       2020-04-01 13:34:31   \n",
       "176797           340 2020-04-01 13:07:34.000       2020-04-01 13:13:14   \n",
       "176798           333 2020-03-23 16:39:57.000       2020-03-23 16:45:30   \n",
       "\n",
       "        start_station_id             start_station_name  \\\n",
       "0                  400.0  Buchanan St at North Point St   \n",
       "1                   99.0           Folsom St at 15th St   \n",
       "2                  197.0    El Embarcadero at Grand Ave   \n",
       "3                  197.0    El Embarcadero at Grand Ave   \n",
       "4                   12.0    Pier 1/2 at The Embarcadero   \n",
       "5                   60.0           8th St at Ringold St   \n",
       "6                   60.0           8th St at Ringold St   \n",
       "7                  450.0       Funston Ave at Irving St   \n",
       "8                  450.0       Funston Ave at Irving St   \n",
       "9                  238.0   MLK Jr Way at University Ave   \n",
       "10                 238.0   MLK Jr Way at University Ave   \n",
       "11                 174.0        Shattuck Ave at 51st St   \n",
       "12                 127.0         Valencia St at 21st St   \n",
       "13                 127.0         Valencia St at 21st St   \n",
       "14                 127.0         Valencia St at 21st St   \n",
       "15                 248.0     Telegraph Ave at Ashby Ave   \n",
       "16                  14.0          Clay St at Battery St   \n",
       "17                 241.0             Ashby BART Station   \n",
       "18                 482.0           Brannan St at 7th St   \n",
       "19                 368.0           Myrtle St at Polk St   \n",
       "20                 220.0    San Pablo Ave at MLK Jr Way   \n",
       "21                 340.0        Harmon St at Adeline St   \n",
       "22                 134.0         Valencia St at 24th St   \n",
       "23                  74.0          Laguna St at Hayes St   \n",
       "24                 133.0         Valencia St at 22nd St   \n",
       "25                 133.0         Valencia St at 22nd St   \n",
       "26                 240.0      Haste St at Telegraph Ave   \n",
       "27                 244.0     Shattuck Ave at Hearst Ave   \n",
       "28                 244.0     Shattuck Ave at Hearst Ave   \n",
       "29                 244.0     Shattuck Ave at Hearst Ave   \n",
       "...                  ...                            ...   \n",
       "176769               NaN                            NaN   \n",
       "176770               NaN                            NaN   \n",
       "176771               NaN                            NaN   \n",
       "176772               NaN                            NaN   \n",
       "176773               NaN                            NaN   \n",
       "176774               NaN                            NaN   \n",
       "176775               NaN                            NaN   \n",
       "176776               NaN                            NaN   \n",
       "176777               NaN                            NaN   \n",
       "176778               NaN                            NaN   \n",
       "176779               NaN                            NaN   \n",
       "176780               NaN                            NaN   \n",
       "176781               NaN                            NaN   \n",
       "176782               NaN                            NaN   \n",
       "176783               NaN                            NaN   \n",
       "176784               NaN                            NaN   \n",
       "176785               NaN                            NaN   \n",
       "176786               NaN                            NaN   \n",
       "176787               NaN                            NaN   \n",
       "176788               NaN                            NaN   \n",
       "176789               NaN                            NaN   \n",
       "176790               NaN                            NaN   \n",
       "176791               NaN                            NaN   \n",
       "176792               NaN                            NaN   \n",
       "176793               NaN                            NaN   \n",
       "176794               NaN                            NaN   \n",
       "176795               NaN                            NaN   \n",
       "176796               NaN                            NaN   \n",
       "176797               NaN                            NaN   \n",
       "176798               NaN                            NaN   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0                    37.804272              -122.433537           400.0   \n",
       "1                    37.767037              -122.415443            99.0   \n",
       "2                    37.808848              -122.249680           197.0   \n",
       "3                    37.808848              -122.249680           197.0   \n",
       "4                    37.796389              -122.394586           371.0   \n",
       "5                    37.774520              -122.409449            75.0   \n",
       "6                    37.774520              -122.409449            75.0   \n",
       "7                    37.763934              -122.470651           450.0   \n",
       "8                    37.763934              -122.470651           450.0   \n",
       "9                    37.871719              -122.273068           198.0   \n",
       "10                   37.871719              -122.273068           198.0   \n",
       "11                   37.836801              -122.264004           259.0   \n",
       "12                   37.756708              -122.421025           324.0   \n",
       "13                   37.756708              -122.421025           324.0   \n",
       "14                   37.756708              -122.421025           324.0   \n",
       "15                   37.855956              -122.259795           248.0   \n",
       "16                   37.795001              -122.399970           134.0   \n",
       "17                   37.852477              -122.270213           252.0   \n",
       "18                   37.772658              -122.403681            13.0   \n",
       "19                   37.785434              -122.419622           123.0   \n",
       "20                   37.811351              -122.273422           230.0   \n",
       "21                   37.849735              -122.270582           269.0   \n",
       "22                   37.752428              -122.420628           120.0   \n",
       "23                   37.776435              -122.426244           321.0   \n",
       "24                   37.755213              -122.420975           459.0   \n",
       "25                   37.755213              -122.420975           459.0   \n",
       "26                   37.866043              -122.258804           252.0   \n",
       "27                   37.873676              -122.268487           351.0   \n",
       "28                   37.873676              -122.268487           351.0   \n",
       "29                   37.873676              -122.268487           257.0   \n",
       "...                        ...                      ...             ...   \n",
       "176769               37.809134              -122.415192             NaN   \n",
       "176770               37.809138              -122.415689             NaN   \n",
       "176771               37.809140              -122.415247             NaN   \n",
       "176772               37.809149              -122.415776             NaN   \n",
       "176773               37.809152              -122.418323           478.0   \n",
       "176774               37.809153              -122.415734             NaN   \n",
       "176775               37.809165              -122.418325           478.0   \n",
       "176776               37.809172              -122.415235             NaN   \n",
       "176777               37.809176              -122.415754             NaN   \n",
       "176778               37.809177              -122.415263             NaN   \n",
       "176779               37.809197              -122.412139            12.0   \n",
       "176780               37.809579              -122.410255             NaN   \n",
       "176781               37.809675              -122.409954            81.0   \n",
       "176782               37.809718              -122.409923           487.0   \n",
       "176783               37.809927              -122.476421            12.0   \n",
       "176784               37.810162              -122.412339             NaN   \n",
       "176785               37.810173              -122.412357            26.0   \n",
       "176786               37.810488              -122.476507             NaN   \n",
       "176787               37.810513              -122.476495           512.0   \n",
       "176788               37.810528              -122.476450             NaN   \n",
       "176789               37.840358              -122.267302             NaN   \n",
       "176790               37.848441              -122.265775             NaN   \n",
       "176791               37.848457              -122.266072             NaN   \n",
       "176792               37.848478              -122.266082             NaN   \n",
       "176793               37.848479              -122.266063             NaN   \n",
       "176794               37.848493              -122.265797             NaN   \n",
       "176795               37.848499              -122.265794             NaN   \n",
       "176796               37.848507              -122.266010             NaN   \n",
       "176797               37.848530              -122.266013             NaN   \n",
       "176798               37.848625              -122.266660             NaN   \n",
       "\n",
       "                           end_station_name  end_station_latitude  \\\n",
       "0             Buchanan St at North Point St             37.804272   \n",
       "1                      Folsom St at 15th St             37.767037   \n",
       "2               El Embarcadero at Grand Ave             37.808848   \n",
       "3               El Embarcadero at Grand Ave             37.808848   \n",
       "4                Lombard St at Columbus Ave             37.802746   \n",
       "5                  Market St at Franklin St             37.773793   \n",
       "6                  Market St at Franklin St             37.773793   \n",
       "7                  Funston Ave at Irving St             37.763934   \n",
       "8                  Funston Ave at Irving St             37.763934   \n",
       "9                                 Snow Park             37.807813   \n",
       "10                                Snow Park             37.807813   \n",
       "11                  Addison St at Fourth St             37.866249   \n",
       "12      Union Square (Powell St at Post St)             37.788300   \n",
       "13      Union Square (Powell St at Post St)             37.788300   \n",
       "14      Union Square (Powell St at Post St)             37.788300   \n",
       "15               Telegraph Ave at Ashby Ave             37.855956   \n",
       "16                   Valencia St at 24th St             37.752428   \n",
       "17             Channing Way at Shattuck Ave             37.865847   \n",
       "18           Commercial St at Montgomery St             37.794231   \n",
       "19                     Folsom St at 19th St             37.760594   \n",
       "20                  14th St at Mandela Pkwy             37.810743   \n",
       "21             Telegraph Ave at Carleton St             37.862320   \n",
       "22                     Mission Dolores Park             37.761420   \n",
       "23                      Folsom St at 5th St             37.780645   \n",
       "24                       Utah St at 24th St             37.753123   \n",
       "25                       Utah St at 24th St             37.753123   \n",
       "26             Channing Way at Shattuck Ave             37.865847   \n",
       "27                10th St at University Ave             37.869060   \n",
       "28                10th St at University Ave             37.869060   \n",
       "29                  Fifth St at Delaware St             37.870407   \n",
       "...                                     ...                   ...   \n",
       "176769                                  NaN             37.784234   \n",
       "176770                                  NaN             37.806195   \n",
       "176771                                  NaN             37.784262   \n",
       "176772                                  NaN             37.796818   \n",
       "176773          Greenwich St at Franklin St             37.800254   \n",
       "176774                                  NaN             37.806566   \n",
       "176775          Greenwich St at Franklin St             37.800254   \n",
       "176776                                  NaN             37.766544   \n",
       "176777                                  NaN             37.806158   \n",
       "176778                                  NaN             37.808295   \n",
       "176779          Pier 1/2 at The Embarcadero             37.796389   \n",
       "176780                                  NaN             37.805522   \n",
       "176781                   Berry St at 4th St             37.775880   \n",
       "176782            Powell St at Columbus Ave             37.800496   \n",
       "176783          Pier 1/2 at The Embarcadero             37.796389   \n",
       "176784                                  NaN             37.787604   \n",
       "176785                  1st St at Folsom St             37.787290   \n",
       "176786                                  NaN             37.804016   \n",
       "176787             Divisadero St at Clay St             37.789688   \n",
       "176788                                  NaN             37.810528   \n",
       "176789                                  NaN             37.848314   \n",
       "176790                                  NaN             37.848441   \n",
       "176791                                  NaN             37.840393   \n",
       "176792                                  NaN             37.848497   \n",
       "176793                                  NaN             37.848602   \n",
       "176794                                  NaN             37.848476   \n",
       "176795                                  NaN             37.848477   \n",
       "176796                                  NaN             37.848485   \n",
       "176797                                  NaN             37.848497   \n",
       "176798                                  NaN             37.848446   \n",
       "\n",
       "        end_station_longitude  bike_id   user_type rental_access_method  \\\n",
       "0                 -122.433537    13052    Customer                  NaN   \n",
       "1                 -122.415443    12235  Subscriber                  NaN   \n",
       "2                 -122.249680    12822    Customer                  NaN   \n",
       "3                 -122.249680    11705    Customer                  NaN   \n",
       "4                 -122.413579     3673    Customer                  NaN   \n",
       "5                 -122.421239    12137    Customer                  NaN   \n",
       "6                 -122.421239    12321    Customer                  NaN   \n",
       "7                 -122.470651     1589    Customer                  NaN   \n",
       "8                 -122.470651    12884    Customer                  NaN   \n",
       "9                 -122.264496    10464    Customer                  NaN   \n",
       "10                -122.264496    12120    Customer                  NaN   \n",
       "11                -122.299371      838  Subscriber                  NaN   \n",
       "12                -122.408531    13068    Customer                  NaN   \n",
       "13                -122.408531    12801    Customer                  NaN   \n",
       "14                -122.408531     9916    Customer                  NaN   \n",
       "15                -122.259795    12914    Customer                  NaN   \n",
       "16                -122.420628     9672    Customer                  NaN   \n",
       "17                -122.267443     3582    Customer                  NaN   \n",
       "18                -122.402923    12711    Customer                  NaN   \n",
       "19                -122.414817     2104    Customer                  NaN   \n",
       "20                -122.291415     3645    Customer                  NaN   \n",
       "21                -122.258801    11447    Customer                  NaN   \n",
       "22                -122.426435     9963    Customer                  NaN   \n",
       "23                -122.402866     2911    Customer                  NaN   \n",
       "24                -122.405393    10133    Customer                  NaN   \n",
       "25                -122.405393      239    Customer                  NaN   \n",
       "26                -122.267443    12276    Customer                  NaN   \n",
       "27                -122.293400     3050    Customer                  NaN   \n",
       "28                -122.293400     3256    Customer                  NaN   \n",
       "29                -122.299676     1947    Customer                  NaN   \n",
       "...                       ...      ...         ...                  ...   \n",
       "176769            -122.388862   557816    Customer                  app   \n",
       "176770            -122.405260   866334    Customer                  app   \n",
       "176771            -122.388924   886830  Subscriber                  app   \n",
       "176772            -122.405370   240222  Subscriber                  app   \n",
       "176773            -122.425919   438673    Customer              clipper   \n",
       "176774            -122.420714   721149  Subscriber                  app   \n",
       "176775            -122.425919   585226    Customer              clipper   \n",
       "176776            -122.398083   451085  Subscriber                  app   \n",
       "176777            -122.405295   572370    Customer                  app   \n",
       "176778            -122.415003   454861  Subscriber                  app   \n",
       "176779            -122.394586   517726    Customer                  app   \n",
       "176780            -122.422260   331159    Customer                  app   \n",
       "176781            -122.393170   888753  Subscriber                  app   \n",
       "176782            -122.410887   420959    Customer                  app   \n",
       "176783            -122.394586   539344  Subscriber                  app   \n",
       "176784            -122.393953   473795    Customer                  app   \n",
       "176785            -122.394380   229168  Subscriber                  app   \n",
       "176786            -122.448654   942930    Customer                  app   \n",
       "176787            -122.440688   401842    Customer                  app   \n",
       "176788            -122.476488   942930    Customer                  app   \n",
       "176789            -122.268623   633095  Subscriber                  app   \n",
       "176790            -122.265775   999960  Subscriber                  app   \n",
       "176791            -122.267316   633095  Subscriber              clipper   \n",
       "176792            -122.266023   633095  Subscriber                  app   \n",
       "176793            -122.265745   633095  Subscriber                  app   \n",
       "176794            -122.265779   999960  Subscriber                  app   \n",
       "176795            -122.265815   999960  Subscriber                  app   \n",
       "176796            -122.266063   633095  Subscriber                  app   \n",
       "176797            -122.266029   633095  Subscriber                  app   \n",
       "176798            -122.266022   633095  Subscriber                  app   \n",
       "\n",
       "        duration_min  start_year  start_month  start_day timing  \n",
       "0        1385.300000        2020            1         31   0131  \n",
       "1        1137.316667        2020            1         31   0131  \n",
       "2         923.450000        2020            1         31   0131  \n",
       "3         915.483333        2020            1         31   0131  \n",
       "4         928.333333        2020            1         31   0131  \n",
       "5         188.883333        2020            1         31   0131  \n",
       "6         189.016667        2020            1         31   0131  \n",
       "7          67.300000        2020            1         31   0131  \n",
       "8          67.650000        2020            1         31   0131  \n",
       "9          33.000000        2020            1         31   0131  \n",
       "10         32.816667        2020            1         31   0131  \n",
       "11         26.850000        2020            1         31   0131  \n",
       "12         18.883333        2020            1         31   0131  \n",
       "13         18.650000        2020            1         31   0131  \n",
       "14         18.500000        2020            1         31   0131  \n",
       "15         20.950000        2020            1         31   0131  \n",
       "16         20.783333        2020            1         31   0131  \n",
       "17         13.383333        2020            1         31   0131  \n",
       "18         19.116667        2020            1         31   0131  \n",
       "19         12.950000        2020            1         31   0131  \n",
       "20          7.250000        2020            1         31   0131  \n",
       "21          8.916667        2020            1         31   0131  \n",
       "22          6.283333        2020            1         31   0131  \n",
       "23         14.550000        2020            1         31   0131  \n",
       "24          9.233333        2020            1         31   0131  \n",
       "25          8.750000        2020            1         31   0131  \n",
       "26          3.000000        2020            1         31   0131  \n",
       "27         12.683333        2020            1         31   0131  \n",
       "28         12.483333        2020            1         31   0131  \n",
       "29         10.400000        2020            1         31   0131  \n",
       "...              ...         ...          ...        ...    ...  \n",
       "176769     18.166667        2020            3          4   0304  \n",
       "176770     19.716667        2020            3          1   0301  \n",
       "176771     17.083333        2020            3          4   0304  \n",
       "176772      7.383333        2020            3          2   0302  \n",
       "176773     12.000000        2020            3         21   0321  \n",
       "176774      5.283333        2020            3         16   0316  \n",
       "176775     13.116667        2020            3         21   0321  \n",
       "176776     27.716667        2020            3          4   0304  \n",
       "176777     16.966667        2020            3          1   0301  \n",
       "176778     14.616667        2020            3          5   0305  \n",
       "176779      8.200000        2020            3          8   0308  \n",
       "176780     18.333333        2020            3         13   0313  \n",
       "176781     18.583333        2020            3          2   0302  \n",
       "176782      7.933333        2020            3          8   0308  \n",
       "176783     94.166667        2020            3         22   0322  \n",
       "176784     23.816667        2020            3         28   0328  \n",
       "176785     23.600000        2020            3         28   0328  \n",
       "176786     13.400000        2020            3         17   0317  \n",
       "176787     42.450000        2020            3         17   0317  \n",
       "176788      2.183333        2020            3         17   0317  \n",
       "176789      3.200000        2020            3         23   0323  \n",
       "176790      1.050000        2020            3         23   0323  \n",
       "176791      5.166667        2020            3         23   0323  \n",
       "176792      1.300000        2020            4          1   0401  \n",
       "176793      2.750000        2020            4          1   0401  \n",
       "176794      1.016667        2020            4          1   0401  \n",
       "176795      1.300000        2020            3         18   0318  \n",
       "176796      2.033333        2020            4          1   0401  \n",
       "176797      5.666667        2020            4          1   0401  \n",
       "176798      5.550000        2020            3         23   0323  \n",
       "\n",
       "[905006 rows x 19 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge[df_merge.duration_sec != 811077]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ad2a8ce90>]"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jsz331Ttv4YxPAlUKR+8Zn0Qf3xXyH52K7NlzcfToUQBA3/mXIL/1kYpjRN9HrsEaD8rj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a36dfa710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_merge['timing'], df_merge['duration_sec'], '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This looks a little more consistent with scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a39b568d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sbVVdPniCpIZfFAh61XFCiBbmCkUUO4XLSmDiJWRRKyH2CJ188ABZDb8owLQ7\nQuRCaAE2UQc7hctKYLKsvaxFrYTISujkAwkVPW0H+1LvCCGBw8hEhAlTCpCpl5C5zYTYJkzygYQL\npt0RIhc0JiJOJFKAmNtMiCMiIR+I9DDtjhC5YZpTxIlCChBzmwlxRhTkA5Efpt0RIjeMTESdCKUA\ncQYQufDiefAZCyZC8oHIDdPuCJEXRiaijknhctjgCthy4cXz4DMWTITkA5Ef2VYdJ4SkoTERcaKU\nAsQZQOTCi+fBZyyWKMkHIj9MuyNETpjmRCIDZwCRCy+eB58xkQmm3QnGw7Q7PitCnENjgoQWzgAi\nF148Dz5jIjPn9u5Mf/jCTcE2JCx4ODMfF8IjxDk0JgKCXhBvyL+vRgV7Wahk+osXz4PPmMjM2V0Z\nRZfGhBC8TLvLpUfSmCA6UGczh8ZEQNAL4g2F95UzgMiFF89DxDk5UBAvSLz7ZwBAWcDtIKVhiiQx\ngzqbOTQmAuLcy3vSH/hiCkXPu8SFt+TCi+fh9pwcKIgIjNLuEky7kw6mSBI7MHJlDo2JgOg9nFZe\nOJ2WWPS8S7nZPioGpf/v7kLq9TbEqkb62zgCQDsjCwB0NT/u+nm4PScHCkKiBVMkiR0YuTKHxoRP\n0GPlDVa8Szm6u3IfUxseQ2LDY7znQeDFjCwuz8mBgoQFLgZpHabBRgs77zEjV/agMeET9IJ4Q6n7\nitqJwJHDurt4zwPCixlZbJ6TA0U08VoplkHOe5GyF+Y0QKbBRgc777EMfVklmGXjE4bKS4bU1k1I\nPrPRxxaFh3hDE2LzGou2x+Y1omzlg4b7KAiCwe2MLIn29/uUQofnNHpnsvD9CCd+rJAeb2gCxk8t\n3jF+qi/vFBeDtAcXwosOdt9jM92C44MWRiZ8glaut5h5l+h5CheivKRMcYgevtXGdJ2xts0DVFoM\nUor0KQ8XwiNy4eQ9pv5gDRoTPhJvaELi3beB1he1O668lsqLS8wKcL0o+CXBIVIh5EARLXyrjTnb\nDUArc7LbRBP0YpBuDAIp0qc8XAiPBIuIvkH9wRo0JvwmrpNZpreN2MPMu0TPU6gQqRByoAg3gdXG\nDLkYself1Mic1B9bxf5GhqAXg3RjEMgwi5qXC+GRYBHSN6g/WILGhN9UVAIo8FhlthEXmHmX6HlS\nGk8VQg4UoSao9NJY3dximTPMOwM1yMUg3RgEnEWNeI3rvkH9wRI0JnwmlkwAhcpLR3vArVIfM+8S\nPU9q46lCyIEi9ARRGxOEzAlqMUg7BgFnUSNB4KZvUH+wBo0Jv1FQeZGiSI5EGq8UQg4U0SAKtTF+\nLQaZbF4LHHqp+GALBgEnIiFs1dRBAAAgAElEQVRBwHRW76Ex4TMqKi9SFMmRyBMFhZC4w8jxEQll\nwq/FIDvagXmNjg0CzqJGfIfprJ5DY4KUxO8iOUZCiB6RUAiJKwwdH1FQJnxcDDI+c7Yrg4COAeIr\nCmaEqAaNCVISv4vkGAkhukRBISSuMHR8RECZ8CLqbXZONwYBHQPET1TMCFENGhOkiKCL5GSYLjAf\nRkokIQIKIXGHkeODyoR4XBkEdAwQEipoTJAigi6Sk226QEZK5IAKISkkaMdHpHFjENAxQEiooDER\nIkR60P0skpNdIZAtUkIISRO04yMopIiWujAI6BggJFxw6eUQ0dN2oM+LLgC9/FcviuTiDU2IzWs0\nbkfACoHo+0oIEYeR/AhabniJU5mUaH+/zxBxCQ0CQkgWRiZChGgPup9FcjJNFyh7pIQQoiVqswM5\nlfVepGxKESUhhAQKjYkQIbzWwOciOVkUgqimThCiKlGbHciprHdqhJgZDKwpI4TQmFAYzz3oPhfJ\nyaQQyBQpIYSUIOSzA4mS9c6NEGODgTVlhBAaEwrjtQfd95xYyRQCWSIlhJASSDo7kKgUIKeyXpwR\nYmwwyDb7HiHEf2hMKE6oPOiSKQQyRUoIIcbIWgwsMgXIiawX5XDKNxhYU0YIKYTGRAgIiwddOoVA\nskgJIUQtRKcAOZH1TowQKwaDYRtpSBASOTg1bAjI96BnvejZbcQF4yZrBsV4QxMwbnKADSKEqITo\naaWzcj1WUYlYRaVmmxlWpvnOnzbWynTdUZuOlxBiDCMTYYAedE+QLlJCCJEar1KACs+b6u7s+7zh\nMSQ2PGZ6Xispm4UpWaUiGqmOk6GIiBNC3ENjIgxkag3yvUoyFB8SQkiU8GpSDNfnteBw0kvJMkur\nYk0ZISQLjYkQkPWW53uW6EEnhBD/8WpSDFfntTC5hd6sTKYGAyPihJAMNCZCRFjm++aKqoQQlfFq\nUgyn59V1Lp06iYReQXX+tjE1xgaDZLPvkeDgmE1oTISIsMz3zRVVCSEq41UKkMjzWkmdwrCRhgaD\nlZoyKpnRgGM2oTGhMGGd7zssERZCSETxKgVI8HkdrV1hw2CgkhkNOGYTGhMK4/UK2EERlggLISSi\neJUC5MF53aZkmRkMVDKjAcdsQmNCcex4lmQNOYc1wkIIiSZeTSvtxXndpk6ZGQxUMsMJx2xSCI2J\nEGDVsyRryDmsERZCSDiQ1REjBJepU/kGA5XMaMAxmxRCYyIEWPUsyRxy9mo6RUIIcYusjhgh2Eyd\nsmIwGEF5Hh44ZpN8LBkTp06dwpYtW/Dmm2/inXfeQU9PD370ox9hxIgRmuN6enrQ3NyMXbt2oaur\nCzU1NfjKV76CCRMmaI5LJpPYsmULtm/fjtOnT2P06NFYsGABpk+fXvTb27dvx7Zt29De3o7hw4ej\nvr4ec+fOdXHJIcSiZ0n2kLNX0ykSQogb/HbEuImE2P2u3dQpS7NA6eynkhk+OGaTLJaMiRMnTqCl\npQVjx47F+PHj0draqnvcmjVrcODAASxevBgjR47Eb37zGzzwwAN44IEHUFNTkzuuubkZW7duRWNj\nI8aOHYu9e/fikUcewapVq3D11Vfnjtu+fTvWrl2L+fPnY/LkyWhra8P69esBgAZFPgaeJdVCzlxR\nlYSNUKfHRAi/HTFuIiF+RFFKeaVTHSepZEYAjtkkiyVjYvz48Vi7di0AYMeOHbrGxNGjR7F79258\n/etfxw033AAAmDBhAr71rW+hubkZq1atAgB8/PHH2Lp1K+bPn4+GhrRSO2nSJJw4cQIbN27MGROJ\nRAKbNm1CXV0dFi1alDvuww8/RHNzM2bNmoXycmZpAeaeJaXyGrmiKgkZoU6PsYCqxlTQjhg3kRC/\noihmXmkqmRGBYzbJELd0ULz0Yfv370dZWRlmzJiR21ZWVoaZM2eitbUVvb29AIDW1lZcuHABdXV1\nmu/X1dXh3XffRXt7OwDgyJEj+OSTT3Dddddpjrvuuutw5swZvPbaa1aaHnniDU25sHM+0hkSQDrC\nktemeEMTMG5ygA0ixB09bQf7lLsI0tN2oM+gUggjuZnFa/np5r75dc/zDYas0ZDdlq9kVjYuT9/L\n11/1vE3EZzhmkwzCXPvHjx/HiBEjcNFFF2m2jxkzBhcuXMCJEydQXV2NY8eOoV+/fhg1apTmuOrq\nas15jh07ptmud9ykSZNstzMbIQGA1atXo6qqyvY5RJCNquj9vuh9iVtvQ0eBB2nYrbehLO84P9tj\nuO9vFxbvy2yzck4AwKn2zP4RRbukuEYL+2RrT9Svw801dhw+KE1bg3hWZw4fSu+bVe/L7wm9xmXf\nRGdFBbqaH9ccN2jhspwnVlRbOzetK/odAJpISP7vOv2uyPv68TtvoGzhMlTO6fudxDtvYOjfLsTZ\naZ/HwFn1ue+NWPZNnN35awwsOLdMfUCKd07SfYb7XY7ZMu2TrT0ldR3JEGZMdHZ2orKysmh7dltn\nZ2fu/4qKCsRisZLHAcCgQYNMjyOlyXqpBi9KD0RnnlyHnrYDGJg3wF84+V76w7BiJVwlzr+avtaL\nrr854JaQKGKk2J38Ul/EtlAZlQUvZMD5rOwRdkZ/GTirvuh5DtQxjNySfR90jQKYvzNuvuuG/pOu\nwsBZ9Tj/wv/k2nF2568B6N8jL+4bIUQOhBkTqVTK8nGFhoTZ9/WOdcPq1as1f3d0dAg9v1Wy1qbe\n74vel9zfgti8RlR8OR2K7uzsxJn9LejKy6kd9MrLAKDZFkRb3ewDgH6v7AcAnJk0LfD2uLkOmdoT\n9euwdY03zkesu9u0TuncjfNxrqNDuusQIQOMag3yjalsipDTtl6STKfMfqRThyH6fUy2/B6ANvf/\nVMvvNXVqwp6HwbuT/864/a7Qd2fKtejq6NDK3Mw2K+cU3h4J98nWnqhfR6lrdCpbgmirX4wePdrS\ncZZqJqxQWVmpGy3IbstGFCorK9HV1VVkPHR1dRUdl/99o/MRC1jIawxLbreqOdokPChVp5SHCBng\nR62Br33c59x/vRmPrM6C5Oa7bqDMJWGB77JzhEUmqqur8dJLL+H8+fOauonjx4+jvLw8VyNRXV2N\n3t5enDx5UlM3cfz4cQDpGov8/48dO4ZLLrnE8DhSGivziMu+BoURQc+6QogeKs6/LkoGeL2Yla9r\nPlhY0E3kjFVWZ0HS+02/ZlCizCVhReaFfWVHmDExbdo0PPXUU2hpacH1118PID29a0tLC6ZMmYJ+\n/dJCb+rUqSgvL8euXbtw22235b6/a9cuVFdX5xbCq62txeDBg7F7925MmTJFc1xlZSU+97nPiWp6\nJAnLgGBlASXZr4GEDxWmxvRSBnhpTPnp+LDjiBGigNhcgFTzmz5N00mZS8KKqk5VGbBsTOzbtw8A\n8NZbbwEADh06hCFDhmDIkCGYMGECampqMGPGDDzxxBNIJBIYMWIEnnvuObS3t+POO+/MnWfo0KGo\nr6/H5s2bMXDgQFx++eXYu3cv2trasHLlyr6GlZdj4cKFWL9+PS699NLconXPP/88li5dyjUmXBKm\nAcFrTyghtlFg/nUvZYBIY0p2x4dQb6aFSIjhb1r8rggoc0kYkF22qIRljfzhhx/W/L1u3ToA6YXp\nvvOd7wAAbr/9djz55JPYtGkTuru7cdlll+G+++7D2LFjNd9dtGgRBgwYgGeffRanT5/G6NGjcffd\nd2PaNG3R7Ny5cxGLxbBt2zY888wzqKqqwrJly3DTTTc5uVZSQJgGBBXTSkiI8VGxc4NnMkCgMSW7\n40OkN9NKJMToN61+VxSUuUR1ZJctKmHZmHjqqadKHtO/f38sWbIES5YsMT0uHo9jwYIFWLBgQclz\nzpkzB3PmzLHaTGKTsAwIKqSVEHkRvVKz34qdGzyRAYKNKZkcH3a8maLeKxk9qJS5JAzIJFtUhrlC\nESc0A4ICaSVEXoTmvVtEtAHjFC9kgBfGVJCOj/xnZcebKeq9ktKDSplLQkJYnKpBQmMi6oRlQFAk\nrYTISRCzeARhwOiiiAzIGj0Db01P3HF22899c3wUPiur3kyR75V0HlTKXBISQuNUDRAaE1EnoAEh\nymklRD6CmMVDmmkIVVEKM0ZPv5rPAgDOZdd88KGf6z0rK95M0e+VTB5UylwSGhRxqMgMjYmIE9SA\nII1XNmTIkjojOzLkoMsyDaEySmHG6OnZuAYAEG/6mm9Gj96z0vNmJpvXAodeKj6BoPeKHlRCPEAV\nh4rE0JgggSCNVzZk0EizRhA56DIYMCqTHezzFXuvjB4rzwpjaoq9mR3twLxG794relAJEY4yDhWJ\noTEREFH3IDv1ykb9vpWCRpp1/M5Bl7KIVgGCMMKsPCsMG6nrzYzPnO3de0UPKiFEQmhMBETUPMii\nFIKo3Te7yJI6owp+56BLV0SrAEEZYU6eVVbR9+q9ogeVECIjNCYCImoeZFEKQdTuWymYOuOOIHLQ\nZSqiVYWgjDCnz8rte8UILCFEJWhMBEQUPcgiFIIo3jczmDqjxbYSFkAOetiKaP1SfIMwwrLPqqzm\nCgBA4uifrT0rl+8VI7CEEJWgMeET9CCnsasQ8L6VhqkzfZzbuzP94Qs3WftCEDnoISui9UvxDcQI\nyzyr1L4XAGRqJaw8K5fvFSOwhBCVoDHhE/Qgp7GrEMhw31RIOWDqTJrzL+5Kf7BoTASSgx6yIlrf\nFF8fjbBCJ0Yq+yGzLdGy01LtRKltRjACSwhRCRoTPkIPMhwpBEHfNxVSDsKWOuOU3qNvAADKBJ6T\nCyya45vi66MR5rcTgxFYQojK0Jjwmch7kB0qBEHeNyVSDiwYaSpEWOxipIQlBCphKhiTfhKU4uu3\nEeanE0OGCCwhhDglHnQDoka+BznrRc5uiwJOFYIg71tP24E+hVJWxk3WKBvxhiZg3GTNIUpch4T0\ntB3sMygJ4g1N6doBA8Kk+Oo5LLxyYhjd1zDdT5lJtL/f53AhhNiCkQm/CVnxpW8EmC+dQ+KUAytG\nmlmERdWohR8eXeavFxN06qFf+J0+GPnIdYAwAkmIc2hM+E3Iii99I8T50n5hphSrPJDGG5qQ6O4E\ndmzT7ph9q6PnpKIxGQSRUHx9dv6w9ik4ZEtnVdXBQ6IJjQmfCVvxpV+EOV/aK+woxbINpFYxvEYA\n2LENiR3bbD+zsBqToomE4uu384eR68CQLQKpsoOHRA8aEyRUiPTmqO55taMUyzaQWsUrxT8MxqTn\nREDx9d35w8i1b8gegVTVwUOiCY0JUhKVwq0ivTlh8LwaKtu1E5HaugmJwu2SDKR2iDc0IbH7t8BH\np7Q7Lhnmqv2qG5OeQ8VXONn7mS9zGbn2BtkjkKo6eEg0oTFBSqJSuFWoNycknlc9pTi+9C6k9u6Q\ndiC1zawG4On/LN7mgjAYk17ClE3vUEnmqoxMEUjZIyVOUckZSZxDY4KURKVwq5k3x7ZQC9DzKlIA\nGynFMg2kbokNHoIUtNcYGzzE3UlDYkwS9VBJ5qqOLBFI2SMlTqFhHA1oTJCS9LzyMgAg1vS1gFtS\njL0iY3tCLUjPq1MBrGuEmCjFsgykrvFC8WcaDwkIprj4h0wRyDA5eLJE3TCOSmSGxgQpQqVwq70i\nY3WEmtO26hohJkpxdiAtq7kCAJA4+mc1U3k8UPyZxkP8wq7MjYqC4guSRSBD4+DJEHXDOCqRGRoT\npAjVwq1WvTkqCTWnbdUzQkyV4sxAmtr3Qvr35jUqmcpDxZ+ojF2ZGxUFxRcki0DKFClxgkrOSD9Q\nyYnpBhoTRBerCrosHjI9b06quxOJFTpFuBIKNVEC2KoRUvh7qeyHzLZEy05p7g0hUcBOiktUFBQ/\nkM4RIVmkxC6qOSO9RiUnphtoTJAizBYCy04n6rQOwSv0vDmx6s8C8xqVEGpOBbBTI4QCnxQii2Mg\nylhNcYmKghJJJIuUOCGMtR9WiWpkhsYEKULJOgQDb0582V3KCDUnAtiNURBlgU+KkcUx4CWyG0xZ\np0j/a+oAAD0v7ULq9TbjaZxDrqBEEekiJQ4JW+2HVaLqqIsH3QAiJ/GGJmBMTfGOMTVFubs5JSRI\nxk3WtCve0ASMmwxAX4CJEmqJ9vf7FBQBOGlrvKEpXetQ+D0LQsvLe0OcI/q9skJP28E+50BIkUZe\nGZFxisQHDUZ80OBcDZNRH88SVgWFqEt+tkA2YyC7Ley4GZNVhZEJYkhs+vVI/WKDduPgoVLWIZh5\nc7wsaBPtzXXaVqdeINWL/cJKEFECp6kzsnv785EmkmpEJsWl576vAgDi//vHuRQXRhKJUihe++GW\nqEVmaEwQYyqHAiioQ7j2euCK8WqF8DwUasKVE4dtdWwURFzgy4ofSq/oon9pFfQ8zre+BACIS7Zm\nTv6zSGx4rG9H5lkkNjyG2LzGyCkoRGFCUPvhhqg56mhMEGNCUIcAwFOhJrwQ0mlbnRoFERf4suJH\nga2o3F6Zvf1GBlNCsloDq88i2zejoqAQdQlL7YdjIuaoozFBjMkomtk0hvzBTCUPmUih5vVMDY7b\n6tAoiLzAlwQ/ZgDRS0cSkToj88xCKhVDWnoWFhQUldLOvCDq108kIWKOOhoTxJBsR8hPY/CjDkFm\nZFVOaBSojR/vlVE6kl3HgGpTH6pUa1DyWVhQUFRKO/OCqF8/kYOojck0JkhJdNMYIhbCy0cl5YSo\ng9fvlVE6kl3HgKwGtR521syRgeyzGHjrlwEAZ7c9pXkWVhQUmdPO/CDq109IENCYICXRTWOIWAiv\nEJXSvMJGmNMYvHyvDNORHDgGVDGoVTJ8AOSeRb+azwIAzmWmhrXjpOk5uA8AEJOsyNwvZE67IySs\n0JggALQKmtU0hkLCHMIrJKppXjIQ5jQGke+V5X7s0DGgikEdb2hC4uA+4PhR7Y6CNXOkIDs17MY1\nANKzTpV6FqqlnYkm6tdPiAzQmCAACuoiVPPmBUGE07yC5tzLe9IfQmhMiHyvXK2ObuH3lDKor70B\nOP6fxdsko7BOLYbSzyLq8jrq1x9WwhyBDiM0JgiA4jxTVdIYAiPiaV5B0ns4rWjFA26HJwh+rzzt\nxxnDZ+CsegByG9SxwUOQAlBWcwUAIHH0z4gNHhJso/Jw612PuryO+vWHkTBHoMMIjQkCQD/PVMY0\nBlm8FVGbqSFIVFkrQARevFee9eOM4XNu928ASG5QZwyf1L4XAGTSu3wwfKzKKzfe9cS/3gscOay7\nL1tkjtqJKFv5oOV2q4iM4xVxDgvp1YLGRESx4gnDlelOLFMaA70V0UNUGoMshqjfeJWOlDVyzr+4\nK73hCzdJZ1AXyrlU9kN2temWnZ4aonbklVPvetnKB00NCpkNCZF9Uqm0O1ISFtKrBY2JiGJFQUNH\nOyBZXQC9FdHDbHpPIP0OJ4GSCmFkDVGP63t6j74BACgTcjaxBJ1Pb1deOfWul618EIl/+nvgk9Pa\nHUMultaQAAT3SdaxKQ0L6dWGxkSEKeUJS+7ZIV1dAL0V0UOUQhhZQ1RwHYZqaWdB5tPblVeuvOsz\nbwT++xfF2yRGaJ9kHZvSBG34E3fQmIg4Zp4wGeoC6K0gQGZ6z3ffBlpf1O648lrLzz6qhqgM/Tho\n/Mqndy2vHHjXTSN3//0LJP77F9LKSJF9ku+5+rCQXl1oTEQc2fNM6a0IL7bzpeM68zfpbctAQ9Qb\nVOyTfsk51/fGgXdd5udR2MfZJ0kpWEivJjQmoo4Ceab0VoQT2/nSFZUAtAphdpseMitZqhNvaEKi\nuxPYsU27Y/atct5TH+WcG3nl1Lseb2hC4tBLwLG3tDuqxwb6PAr7OPskKYXsDk67RGXiDxoTIcLR\nS6tInim9FeHDdnFqMlE8IUBHu+l3aIh6R+ySqr7ZkfK2iUD4AOyznAtCXsVvvxfJe1cUbQsSvT4e\ntj4ZFWXRNxRwcNohKhN/hHLdp6jS03ag78W1iCp5pvneiqzHIrstaiTa3+8bwBTG9vs6brJG2Yg3\nNAHjJpf8mp4SR0PUPamD6foVTZ88+KLZVyzjRJaZ4becsyKvRPdjGWWk0XMMU58U/a5GHodyXlZ6\n2g72GdUhhpGJEBHq2WpC5q1wg6qeDter/DpUCMMWNpeJwj6Z+mOrkPMqL8ssyCs3/VjXGy6BjLTa\nxzEs3ffC0CeVf1clQxUHp1WiMvGHUGPitddewy9+8QscPXoUvb29GDVqFG666SbMmjUrd0xPTw+a\nm5uxa9cudHV1oaamBl/5ylcwYcIEzbmSySS2bNmC7du34/Tp0xg9ejQWLFiA6dOni2xyqHDz0kof\nqlUkHcsPVB28AsuXlkDJCiOxurnFfXKYGGVQ+QHYgrxy0491DREJZKTVPp58/NHQ9Enl31UilKhO\nMiDMmHjnnXfwve99D7W1tfjHf/xHXHTRRdi3bx/WrFmDCxcuYO7cuQCANWvW4MCBA1i8eDFGjhyJ\n3/zmN3jggQfwwAMPoKamJne+5uZmbN26FY2NjRg7diz27t2LRx55BKtWrcLVV18tqtlKI/Klld3b\nHTZvhRtUHrwCyZeWQMkKIyL7ZNgGYCv3xk0/1q1FkERGWurjCvfJsL2rVpHe4SgJUZ1kQJgxsWfP\nHiSTSaxatQoDBgwAAEyZMgXvvPMOfve732Hu3Lk4evQodu/eja9//eu44YYbAAATJkzAt771LTQ3\nN2PVqlUAgI8//hhbt27F/Pnz0dCQ7qCTJk3CiRMnsHHjRhoTGUS+tKp6u6NA2AYvv4tTZVGyiDFR\nGIC9cP7I6lAo1cdV7pNReFf1kN3hKBNhm2TACsKMiQsXLqC8vBz9+/fXbK+oqEBXVxcAYP/+/Sgr\nK8OMGTNy+8vKyjBz5kxs3rwZvb296NevH1pbW3HhwgXU1dVpzlVXV4d///d/R3t7O0aMGCGq6Uoj\n6qWVfXCKMmEbvFjDoB5+eCXDPgC76ceqORTC3sfD/q7qQYejPaI2A2UslUoVzu7niHfffRf3338/\nvvjFL+Lv/u7vcNFFF6GlpQXr16/HN77xDcycOROPPvoo3n77bTz22GOa7+7duxePPvoofvCDH6C6\nuho/+9nP8Oyzz+KnP/0pYrE+9fbPf/4z7rvvPtxzzz2OoxPZ6AcArF69Gj09Pc4u2CXl5Wk77sKF\nC673JdrfR8c/LtAcV/UfT6NsxKcMv9u5aV16nn4TBi1chsrG5ULb6uU+2doj+jr0nln2Gal0HR//\nn++jbMQoXLz4awCA0z9dg0T7CQz95j9LeR1u3jmcykxdO6zY+aHSdZx/4X8AABddf7On7bEiy1SX\nD077sZnMzv++DNdopY/L0lan+5yMuzJeh5V9AHLXWvUfTwfeHhX0h7M7f41PfvgABi9K98szT67D\nkDvvx8BZ9ULa6heFAQIjhEUmPvOZz+Db3/42/u3f/g3PPfccgHTUYcWKFZg5cyYAoLOzE5WVxYtM\nZbd1dnbm/q+oqNAYEnrHkTTZqEL+S9vTdkDz0haSHXisDE5EDgbOqi96XmbPWFb6T7pK0+7KxuU4\nu/PXAbbIO86/mu6bekr4hZPvpT/oGBqy0fPqHwDoX4fQ33Egy0Th1/Nw2o+NZLaMsjrbx7P3NIx9\nPMh31Q+MjNeTX+rLLJHx3ZOFnraDGLRwGYY0fRVAelKhnraDoXk/ChFmTLz//vu5yMKKFSvQv39/\nvPzyy1i7di369euHuro6WA2CpFKpIkMiu90tq1ev1vzd0dHh+pxOuCTZCwD4SCdtoKoqvfCTXtv0\n9iX3tyA2rxEVX06HlDs7O3Fmfwu6CsKRRd+9cT5i3d26odpzN87HucxxdtsT1D433xX5PNzuM9qf\nbPk9AG3qwKmW32tyjZV4HlOuRVdHh3ZfZpuM1+Hmnev3yn4AwJlJ04r2DXrlZQAo6qdetdXNdaQy\nxsR5j9tjRZZ5dY1+PQ83/Tg19fNAgYJ3durnc7K6VHt8k3OZ/qy5pwV9XPhv+rzP8biryj4D/SBL\nvp4QeFsF7BN93uRlf4X4zNm5yMK5G+cjuWeH5hg3bfWL0aNHWzpOmDHx5JNPory8HKtWrcqFZyZP\nnozOzk5s2LABM2fORGVlpe6NyUYaspGHyspKdHV1FRkV2doLveiGaggtZnIxM0bU8vqMUKK4jFOc\nKodZLZLMOciB5ei7nOXHTW2Hb8/DRT92W4vghZwzu+cyv+OuUXhGKqtEsTZEFCpPMuAEYcbEu+++\ni8suuyxnSGS54oorsHv3bnzyySeorq7GSy+9hPPnz+Oiiy7KHXP8+HGUl5dj1KhRAIDq6mr09vbi\n5MmTuW3Z4wBgzJgxopodGCKFrJuXNjs4Dbz1NgDA2W0/D1WhnFXMnoc0U+JFYPBSHTtKuMyTHgRV\n9O92AHajLPv2PNz0Y5cOBS+Ue7N7LvM77paoKIt0OBIrCDMmLr74Yhw9ejQ3q1OWN954A/369UNl\nZSWmTZuGp556Ci0tLbj++usBAIlEAi0tLZgyZQr69Usra1OnTkV5eTl27dqF2267LXeuXbt2obq6\nOhQzOUkjZDODU7+azwIAzs1rjKS329yD7H/UQs+AicrgpTKllHBk9iXy90s6I4+KXkk7ynJQ0RdX\n/dilQ8GLcSf/nqs26xQpTdhn5iJiEGZM3HzzzXj44YexevVqzJ07F/3798f+/fuxZ88e1NfXo7y8\nHDU1NZgxYwaeeOIJJBIJjBgxAs899xza29tx55135s41dOhQ1NfXY/PmzRg4cCAuv/xy7N27F21t\nbVi5cqWoJvuKH0LWkQc9Mzj1bFyT/mrT1yLh7bbnQfY/VK9E2hXRxUwJh872/P2yKVmqeSXtKMtB\nT7nsRF7bNUT8GHfy73nQ95R4ANNriQWEGRPTp0/Hvffeiy1btmDNmjXo7e3FyJEj8Q//8A+YM2dO\n7rjbb78dTz75JDZt2oTu7m5cdtlluO+++zB27FjN+RYtWoQBAwbg2WefxenTpzF69GjcfffdmDat\nuJBRBfwQsk4U0OxApBtVKAYAACAASURBVBkQIiAk7DyPIKJIoc41toA0qWUOMVLCY1UjlfL2y+6V\ndKssBxl98cphkN93vBh3rNxz1E4Ejhx2/VtEApheSywgzJgAgKuuugpXXXWV6TH9+/fHkiVLsGTJ\nEtPj4vE4FixYgAULFpgepxJeD1wqhPhlwnCgrZ1YnIoC+HpvpEmDCwjVIzNmSrhS3n4LXskgDT8R\nynJQz8Mrh0Fh3xE97li557EZs5G8d4V2u6zvODHFSjRMdecPcY9QY4KUxsuBS6UQvyzoPY/40ruQ\n2rvD13tD406L8pGZjBKenVM8XwmX3duvwYJXMmjDz62yHNTz8MphoNd3RI87pe559h1R4h0nrgla\nBpDgoTHhMyIHLpVD/LJg9Dz8vjc07rQoH5nJ1iK98iKAAiVcoRxkK15JGQw/V8qyT8/DL4eBXt/x\nwmAyvecKvePEPTLIABIsNCb8RqCQVTnELw0mz8PvexNl4y5skZm+WqS+QTanhIcsB1kGw8+VsuzT\n8xDpMMhPK7FUwzCmRrhyb3rPQ/aOE3NkkAEkWGhM+I1gIetUATUcgIBcrqtKyptjTJ5HEOkPUTXu\nwhqZ0RtkVZ/iV0rDz4WTxs/nIcphkJ9WYqXvYNhI8cq9yT3P/pamGFyhd5yYI6UMIIFCY8JnvBi4\nnCigYVXe7GL6PAII1SuVTy+YMERmojDISik7FPKEi3AYFKaVOOk7rpV7BeppiDdIKQNIoMSDbgBx\nT74CmlVCs9vMiDc0AVOvKd4x9RoKAiA9WObdh3hDEzBusre/mWfAVDYuT3sVX3/V29+UCD2lSqXI\nTLyhKbeehB5hGWSNrjOo61Mp2uNUXufT03agT1HP4HffsVpPkzN8SKiQTQaQYGFkIgy4CfEvXIHk\noZeKtpGAFBSFPKxeEIbITBgiLFaIakqeaxzIa0t1EVemvf8y9R3m0ocbygCShcZEGHChgIZBeQsT\nKnlYPSEks8CoOMjanSuessMhDuS1pbqIjnYg4L4ThTQ/0gdlAMlCYyIEuFJAQ6K8kZAQksiMioOs\n7fx2yg5HOJXXVtZ2CLrv2M2l52JnikMZQDLQmIg6IVHeSDgITWRGwUHW9lzxAcmOKCugZhEvWfqO\nnTQ/FmgrDvUHkoEF2BFHlgGIkFARRPG+S/SKes0ISnbYbWeYEFG87QdWi8FZoK021B9IFkYmiGdE\n2YMI8PqjjAqDrKr57ZFebVeRiJfVND8WaBMSDmhMEM+Iegj73N6d6Q9fuCnYhhCig6pzxfe88jIA\nINb0tYBbEgCqpJUYGD3JUyeVNGAJIebQmCCeEWkPIoBzL+5Kf1DAmGAUJZqoMI2tqhEUL1Ah4gXA\n0OiJz5ytpAGrEpTlJAhoTBDPCCKELZMgvfD2EQBAWcDtsELUo0hRRvZpbFWNoJRCJlklGjOjRwUD\nVmUoy0kQ0JggwpDBgxikIDW6/oQCHtSoR5GijArT2IZRAY2y0ie7AasylOUkCGhMEGHI4EH0QpCG\n2YOYhYWQEUbyol5DJwXSsiaxdZOSRkWUlT4VDFhVoSwnQUBjggglaA+iF4LUqgdRBmPKKjJEkYgk\nSF7Uq1K/skOklT7JDViVoCwnMkBjggjHzxC2H4LUjgcx3tCERHcXsGOrdsfseVIJ87AqaMQ+KhT1\nxhuakDi4Dzh+VLtjTI0y7ymVvjwkN2BVgrKcyAAXrSPC8XNhpXhDE2LzGg33ixCk51tfwvnWl6x/\n4eIqa9sCxujecfAhMhKbfr2lbbLih6xSBRUMWJWgLCdBw8gEEY/PIWzRqVV2Cql16ykO7QMAlNVc\nkf7e0T+nt938Jdtt8RoWQhK3+FZTVDkUgDbPPrtNFYJOAyXhhbKcBAkjE0Q84yZrBsZ4QxMwbrKn\nP6knNJ0KUjsexJ62A301FQXHJD85jeQnp03PFTR+RpFIODHqA8LJc1JUNi5P96vXX/X+dwVjRVYl\n2t/vM9IIsQBlOQkSRiaIcIIIYYueHcSqBzG/niI/opF68099X8psS6xokM8DyULIUBHEzGO+zUpk\nIc9ehZnXrMiqKE8bSxxCWU4ChMYECQcCBamdqSjzZ2RRshCOhZChIggl1K9Ziaw4KZRQwi3IqihP\nG0scQllOAoTGRECo4EFTCoGCtJRRAPQZFTny6ilQOxE4clhzvJSGBFgIGTb8UEJlnpVICSXcgqyK\n9LSxxBGU5SRIaEwEhBIeNIUQLUjNpqKMXTXd1NAoNCQAtRfXIurghxIqcwROBSVcVy6dOqmZ4CGH\nBAYaIYSUgsZEQCjhQYs6194AHP/Pom3xm79kWE8Bne35+6kMEJEEFSWQZVYimaMkdpDZQCOEkFJw\nNqeA8G0GFOKY2OAhALSzY2S3Gc3IEm9oAsbUFJ9MocW1iDqIXLvA7gxCImdQc0qY1m7gWgGEEFVh\nZMInwuJBixQmhZJ6M7Ikm9cChwwWtzt+VM7ZnEhJZK9vEhUlsJt6aWVWIj/unSxREhFwrQBCiIow\nMuETYfKgRQaz9TJ05ryPDazkMw4hKkQRRUQJetoO9qVfWsHCug9+3TsZoiQi4FoBhBAVYWTCR8Lk\nQYsCpkXdBjOyxGfONizc5jNWkyDqm+x69EWss2K7eNnSrET+3DvR68wEhsUprmWPlhFCogWNCZ9h\nGDscmBkasenXI/WLDZp9senX+9Aq4gVBzBBke7Y3B+usuE29tLPug+f3LiwLdlmc4pqzARJCZILG\nhM+ExoNGjKkcCkD7jLPbiPzIUN9k5tHX9Uo7WGfFixmEArt3IVmwy+oU1zLNBsgoCSGExoTfhMWD\nlkG2gUSK9oTsGUcNGabpNPPo63mlna6zYjX10mq/CureRW3BLi8iPk5lZ1iiJFKMHYQoCo0JvwmJ\nBy2LbAOJFO0J2TOOIn7XN9nx6Iv2SltJvbTTr1gbJh4/Ij5OZadMURI3SDF2EKIonM3JZ8LmQbM9\nA4zHyNCesD3jqOLnDEF2ZnsTPUOSlRmEzPqV3voUYZldSRb8mA3QqexUYbYzK8gwdhCiKoxMEFcE\nUZxqhmztIerid32TYYpQ7USktm5ConC7KK+0hbQ8u2lX2XtXXnMFAODC0T+zNswlXkd8rMpOGWqK\nvIBjByHOoTFBbCHbQCJbe0iICKD2RS/lKL70LqT27vCuDkEnLS/xf7+PRF4fymE17Spz75L7Xsgd\ny7oh94icDdCp7JShpkgEosYO1loQQmOC2ES2gUS29pAQEUDti1E0xEuvtF4KXtkd/2ysbKE47QpI\ne3QLv5PKfshsS7TsZJ90gchomRvZGYa6GFFjh0q1FjR8iFfQmCC2kW0gka09ssKBxB4ial9s33OT\naIjfa9S4Sbsywmqf5LtqgOBomRvZGYY1k0SMHSoVoKtk+BC1YAE2cYRsBZaytUdGwlIoqRK27/m4\nyRolJt7QBIybDMBaobRo9PpQfOldJYuB9fbbU9D4rupi8n44xansDOJ99AK3Y4dK7yqLzIlXMDJB\nHCHb4nuytUdGVPKghQW799w0GhJADYfTtKtUx0lXXmu+q/p4MVOcY9kZkvV07F6/ynV6LDInXkFj\ngjhDtoFEtvZICAcS/xF6z4NYv8Rh2pVb457vqjfopo85lZ1hWU/H5vWrVKfnl+HDtERCY4I4Q7aB\nRLb2SIDKHjRV8fKeu/VKOxrwTfqVqcFgU0Hju+oPujnzDmVnaNbTcXD9qtTp+WX4sBaDsGaCOEK2\ngUS29siAHwtdES0y33Mnud1W0q4GzqrHwFn1fdO/ArZz+2W+b2FCL2c+6rLT6fWrUqdn1LdE9inW\nYhBGJogrGN6UG1U8aGHC6j33u+8Ir0PIeHR7XnkRgNaj60RBC+O7Kpt8VCl9TLZ7V4hKdXpez7yl\n0ntFvIHGBHEFw5vyE4YpHFXDyj036zteKFKiB/yscZBvpLj1aIftXQ1aPqqcPhb0vSuJQnV6og0f\nld8rmZDdYLaDJ8bEgQMHsGXLFrz11luIx+P41Kc+hcWLF2PSpEkAgM7OTvz0pz/Fyy+/jJ6eHtTW\n1mLJkiX4zGc+ozlPT08PmpubsWvXLnR1daGmpgZf+cpXMGHCBC+aTRzAWVfkJzuQlNdcAQC4cPTP\n0nrQVCR/QDBb6C157woAJVaOziBCkfJrwBdppKjk7bVC0PJRpWLhQs7v35P+4JOxbRuV6vR8Wp8k\ni8zvlUxIbzDbQLgx8dvf/haPP/44brrpJixYsADJZBJHjx7F+fPnAQCpVAoPPfQQ2tvbsXTpUlRW\nVuJXv/oVvvvd7+Khhx7CsGHDcudas2YNDhw4gMWLF2PkyJH4zW9+gwceeAAPPPAAampqRDfdV6QQ\nhgJgeFMBMgNJct8LANCX2y6hB01F8gcEO4OsWd8RoYR6NeB7aqRI6u11Kq9lkI+qpo9l+4BeYacM\nSphStSYeGD6qvlcyEbSzQSRCjYn29nZs2LABixcvRn19fW771KlTc5/379+P1157Df/yL/+Si1TU\n1tbijjvuwJYtW7BsWdojdfToUezevRtf//rXccMNNwAAJkyYgG9961tobm7GqlWrRDbdd2QQhk5g\neFMdCp9VKvshsy3RspPPSgCFA0K8oQmJY28Bh17SHjhydMmVo/uiFmKUUC8GfE+9ki6VHq+cNFbl\ntazyUYX0MaN7l9DtH+FRwvzAK8NHhfdKZmRwNohCqDHx/PPPIx6PY86cOYbH7N+/H5dccknOkACA\niooK/PVf/zX279+fMyb279+PsrIyzJgxI3dcWVkZZs6cic2bN6O3txf9+qnr1T/3snEIV2YY3lQH\nPit/0BsQ4gtXIFlgTMTv+i5Se3cYPg8AxcaGACXU6oBvRxH3yivpVunxykljVXmVrc/ZSbsLGlFR\nPeIfYUtL9BpZnQ0iiKVSqVTpw6zx3e9+F93d3bjlllvw9NNP44MPPsDw4cNRX1+Pm2++GQBw//33\no6KiAvfff7/mu1u2bMHPfvYz/OQnP8GAAQPw6KOP4u2338Zjjz2mOW7v3r149NFH8YMf/ADV1dW2\n25gf0Vi9ejV6enocXKl72v/+FgDAiJ/8d9G+8vK0jXfhwgVh+0Sft3PTOnQ1P645btDCZbn0BK+u\nw89rDGqf6PMG9ay8Om/Q+/TuZyH9r6lDz0u7MHhR+h6feXIdhtx5PwbOqjd8HgAMz5v/vKy21Uo7\n8897/oX/AQBcdP3NpufNkmh/Hx3/uEBzXNV/PI2yEZ8y/Z6X+8788AEAwOA77y/aBwA41Z7+f9gI\nW+fNXmfVfzxt6Xte9rlS11G4z+w9cPJeidpXuN/K+2pG9lqClg9B/6af1/Hx//k+ykaMwsWLvwYA\nOP3TNUi0n8DQb/6zlNchw7MS0R/9pH///paOExqZ+Oijj/DRRx/hv/7rv7Bo0SKMGjUKLS0tePzx\nx5FMJvE3f/M36OzsxPDhw4u+W1lZCSBdnD1gwAB0dnbmthkdpxJGL9DJL/VFXgoHG5kZOKu+6HoG\nzqo3OJoECZ+VWLJ91GxASLSfwKCFy1Ax+1YAQDKZRE/bwdyaDHrPI6uEl1JCL5x8L/1BT5G02c78\n8/a8+gcA+saEHlnvcL7B1NN2INB363y2TUb7X03vL3WNbuW1133O7DoK91U2Lkfv22+g56VdmuP6\nX1Mn1XhT6n3tN3Eqeg8f0t2n0tgZJvpPukrzXlc2LsfZnb8OsEXyY/Seq/4OCzUmUqkUzp49i3/6\np3/Ctdemw8GTJk3CBx98gF/96le45ZZbYBQIKdwuMGCiYfXq1Zq/Ozo6PPmdIm6cj1h3t2kI99yN\n83GuowNVVVWGbXO6z8139fYlW34PQOtRPdXye01KghfX4ec1BrVP9HmDelZenVeKfQb9OduPk3t2\nID5zNspa02swZLd1dHSYPo/U1M8DBYPM2amfx7m83x70ysvp7+mk3BS19cb5wGuHgUw7clx5bU7e\nZElljInzVt+r/S2IzWtExZfT19HZ2Ykz+1s07fL6eRilDeQr/flpA/1e2Q8AODNpmvlvupTXXva5\nUtehty/1pb8HCoyJC1/6e835pZBzN84HXm8rrjeaeg2Sd/wzYjrPO/9ZBHEdUZCBhvunXIuuwj6Q\n2SbjdcjyrKzI+VJt9YvRo0dbOk6oMZGNGkyZMkWzfcqUKTh06BA++ugjVFZWoqurq+i72W3Zc1RW\nVurexGxEQi9qITNmuatAOk80CaiTKyfprCtmhGUGLdso+Ky8QPTzN6tFMF2DweR5WMlBtlt8Gm9c\njmSBMRFvXO4+f1eCqTHt1ijYybV3VRficZ8znwmseF/2vep/TV36mJd2+ZLb7qjPxcsMt7Hgl6iM\nSjVMdhFqTFRXV+ONN94w3B+PxzFmzBi88sorRfuOHz+OqqoqDBgwIHeul156CefPn8dFF12kOa68\nvByjRo0S2XTPka0wzzUSKBJ2UXUGLdcE+KxkMuBEP39rir+O0mf2PCwooXaLT43a6VYmyTI1ppnS\nD2hnA8ph0WByrLwK7nNWDD/TfWNq0tfZ+QkA/6aHdtTnBqYdhZo6osw2FvwSlQmdHpiHUGPimmuu\nwc6dO9Ha2orp06fntre2tmLYsGG4+OKLMW3aNLzwwgv44x//mFt8rru7G3/4wx/whS98IfedadOm\n4amnnkJLSwuuv/56AEAikUBLSwumTJmi5ExO8YYmJN59WzflQLUXyK0iEYSSGdXpBINU+mQy4IQ/\nfx3FP7V9CxIbHis+tkB5LST3PHSU0MT//b4rhdjMQLHqfZfJKNTDSOmPVY10NXg7XfBRdJ8rpYSg\ndiJw5LDurti8RmDYSMRnzkbPfV9Nn+9//9gXh4KTPhdLJoDC97UjU1TOKCtRHMOpw6deo5wemI9Q\nY+Kqq67CxIkT8eMf/xiffPIJRo4ciX379qG1tRW33347gLSRUFtbix/+8IdYvHgxBg0ahM2bNyOV\nSqGhoW9wrKmpwYwZM/DEE08gkUhgxIgReO6559De3o4777xTZLP9Ja6zBI/etpAThJLJ6QT9RyYD\nzuz5O1KW9bzPw0YCp04K9faX3fHPpuHxkt6sEl5yK953mYxCPcw81iLSlWRY8LHUdei+I7UTc9s0\nRm7GEE1seMxTb6gjmWv2vioYESekEN2pwxeuCKg1YhBqTMRiMaxcuRIbN27Ez3/+c3R2duLTn/40\nvvnNb+aiDvF4HPfccw9+8pOfYN26dejt7UVtbS2+/e1v5wpOstx+++148sknsWnTJnR3d+Oyyy7D\nfffdh7Fjx4pstr9U6IRwK9Sq/xCBH0pmmOd0VoUgDTg7z9+JsmzmffZroTgr5yzlJfeiTsN3Snis\n7aYrybrgo9l16O2LL73LdG0T0dcgQuaava9BRVllj8wRtQhjup5QYwJIL0C3fPlyLF9uPMVVZWVl\nLlJhRv/+/bFkyRIsWbJEZBMDxTSEGyH8UDLDnJ8oKzIZcPYWwRKrLHtRKOpZ8amF1JHzrWkvWrzp\na0Vfl0LRKuGxtjt4yyo7zK4ju2/grbcBAM5u+7lpbYyda7D6jGW9b26RPTJHFCOE6XrCjQlSgoiG\naYNSMr1aqZfoI5syYfX5izZuvfA8eebNslGnkRAU1RFNSY+1g8FbStlhdh2Zff1qPgsAOJeXkuXW\nELXzjKW8by6RPjJH1CKEeiCNCZ+RZQYUvwlSyYzSdIIyeIllUyb0nn+qu9NdUXMpvPA8eeTNclun\noYSi5XDwlk52WKgn6Nm4Jr2v6Wu5fW4NUbvPWLr75hLW2xGRhFEPpDEhITIohF4QlJIZxvxEI2Tw\nEgPeKhN2+4fe849VfxaY1+idceuF58lnb1ZQUR1AvAx0OnjLJjus1BPkPw8ra5tYQdR0xKrIXJnS\nNQlRARoTEiKLQugFgXisQpifaIQsXmIvlQnb/cPg+ceX3eWZceuF58lPb5ZZVCK1dRMSHita0shA\nBWSHpTUorrwWZXnPo5Qh6lqZVuC+mSFbuiYhskNjQkJkUQi9IBCPVQjzE42QJhyfUSYGzqoHIFaZ\nsN0/TJ5/kOkYMkcg3a5r4FbRkkYGKiA7nCq+Zoaoa2VagftWCtnSNQmRGRoTEiKNQugFHiqZgL6C\nFsb8xCzShuOz+duvpBdoFKlM2O0fZs8/yHQMabzvBsQbmpA4uA84flS7Y0wNylY+qPvuiZohSBYZ\nqIrs8GLxQS+nI1aFsNV+EOIVNCYkQFqF0CW6A5eHSiYgv4ImGlnD8X35230eZqfKhKf9I8B0DGm8\n72ZcewNw/D+Lt8G9opXfV8MqA/3Ei8UHo65Mq177QYhf0JiQAFkVQrfoDVwilUz931RAQROMzOF4\nER5mT/tHgOkYXnjfe/7Ymv4w4tNCzhcbPAQpFBSvDx4CQOwMQWGVgX7St87ElwEAZ7c95Xrxwcgr\n04rXfhDiFzQmJEFmhdApZgOXV2kMsqRH+I0sHkSvPMxe9Q8pipoFet/PPv/r9IeFX3V8Dg0W1jUQ\nNUNQGGWgGcJrZkzWmchiWz5GXZkOQe0HIX5AY0IiZFEIRZE/cHmlSDE9Io0sHkQvPcyq9w8/vO9Z\nAz620NVp+rCwrkFWKRYxQxBmzyvardIztoPwlEyddSaMFh+0LB8jrkyHpfaDEK+hMSERsiiETrGk\nLBjgVJFSNT3CK6+kDB5ErzzMqvcPwHvvezLzXpW5PlMaO+salEpZtNJXMWwkUgBQMSi9sbtLuWds\nFbPIrRP5oLfOhJ3FB83OWWobISTa0JiQCYkUQidYUhZ09rtOVVEwPcIrr2SWoD2InkQRFO8fWUTe\nm8S/3qs7RavGG107EWUrH3R0fivYycM3lBG1E7XburtyH1MbHkNiw2NS92cnmKUc2ZEPlpw4OlP5\nyn4/ZZ46mRCiJR50A0ge4yZrhHu8oQkYNznABtkn3tCUMxryyQ5cekqTiDQGr87rFT1tB/uUMAHI\n5kHMjyJkIwnZbY4JQf8AxN6bspUPphVFIzw2JIC04ptTfi2g1y/jS+/SlRu57/ig+Cba3+9TYD0g\n+cxGJFY05P4lPziB5AcntNue2QjAnnwwkrlZYvMaEV96V/F2ieUjAJzbuxPn9u4MuhmEEAswMiER\nsimETjHzvHqVqqJaCkzoC8U9iCKEpX+IvjdlKx/Uj1B4ZEi4rVMy6qtBRxi9nlbaTkqm7bVUSty7\nbJRSFfkIAOdf3JX+8IWbgm0IIaQkNCaIcEwVe69SVSRPgYlcobhkaVdSYbNw2Qqx+YuReujeom1e\n4LpOyaSvBllk78e00qXSvBKF223IB9N7J7l81KP36BsAxNX/EEK8g8YEEY/ZwOWVkim58qpqobhT\nrEQRopoTbbdw2QqpPel0kH4TpwIAeg8fSm/7K5MUKBe4iiKY9FWvI4wyrLqtp/THl96F1N4druSD\n6b2TXD4Cxg6XRFgdLoSECBoTRDwmA5dXqSoqpMAEncYRBFaUtygtMJiPUE/4kVeB2om49Pv/DwBw\nctWK9DYPcRpFMO2rHnvQczn4X7gpsGihZ2leJvfOS/moklNApbYSohIswCbCUUGxDwrVCsXdYlac\nK7oIXTXM7o3tYuDayZr6iHRhtrfF6SoW2Z97cRfOZXLxLRUue2Hk5yn9lY3L0214PW34uZIPAU1Q\nYLcA3wg/noeothLreD2xAZEDRiYI8RHVCsXdEsQq6LJixxNuN2pT9r/utLRNKAoW2V94+wiAvjz8\nQKKFHqV5BeXEERlhizc0IdHdCezYpt0x+1Yhz8OPuhiiJeoR6KhAY4IQF9gOmytYCOkGP1ZBVwV7\ns/kooPRYyMMPOq3ESh6+36tuB5nm5QWinQKxS6rSCxcWbBOBWVuDflfDihKyjLiGxgQhcD6Q2Pa6\nKFAI6YYgVkFXCauecBWiNlY84Up4JTva0//LsOq2AvLBa6dA6uCL6XNUVKb/7u5Mb7vp7zxtq2zv\naliMGxVkGXEPjQmiFF4JWKcDiV2vS9jrSYJaBV0l9AqXU92dWm95FoFRmyCUk6C9kqXeRwBAa1p5\nlWHVbRXkg1cz0xUq/qnuzr6db/4JiRUNts+tcjRQNuPGKjJHoMNioMkIjQmiFF4JWKcDCb0uxZTy\nvqc6Tnq2lkDPH1vTH0Z8umhf17NPpz9c88WifWaDTKkByGz/2cyUrRg3JbdNLy8+Vv1ZYF6jNaXH\n5BpN95XoO2eaH09/mF1s1JidV+8aC3/TblqJm+dRSDoPvwvYsVW7Y/Y8xCoGub7nZpjdGzfX6LQ9\nTpWpwr5jOcJWop357bGl+Nt4H1WNBvpt3Dh9pwqReRp0VQ00FaAxQZTCKwFrdSCR2esiE0Gsgg4A\nZ5//dfrDwq8W7evashEAENcxJswGmVIDkNn+7md/nv6Qr0xm8uIHzqpPH5PJi48vu8uS0mN2jab7\ndm03vY6z//NLAEBcx5gwO2/35p+lP6yaIiytJH8K10IcKQQX6+TcX1yF+M1fcn3PzdB9/hncXKNZ\ne8wMBrPfNPte1y9/kt6V13esTA1c6r4VXqdVxd/0fdS550FFA93gt3Hj9B3XQ9Zp0GWLPoUJGhNE\nKUQJWKdGgcxeF5kIZBV0AD2tLwMAYjoDYuqDE8bfM511ynwAMtt/4S2dVXwzefE9r6TTa/Lz4q0o\naNnfiy00bovevt7XWovbkkfq9EcGe8zPe+GdN3PnFZVWkp2+VV/RNv6eoUJ8aF/6/4GZuoizXelt\nN3/J9T03Q/f5Z3B6jQDQc8j4PTczGM6b/KbZ91IfdhRvy04D3L9/plE9RU6BUvdN7zrdPg+9e55t\na+ySYem/PzplKxroB0E7qpy+40YEuZq9EbJFn8IEjYkQEcZ8QK8ErBujQFavi1QEsQo6gGSmmFZX\nYU4VzhHTh9kgU2oAMt1/rrtoU98K2H2KVHZbTkGLxXJtLlTQkpl+rneN+fvsryhsfH/MfhO9PZo/\nRaSVFE7hmo/Z97qf25z+cPNtRfti8xqR2tbc9zmT1qG3Noade26KzvPP4vQaASB5yvg9NzMYeo86\nM250+06mj2efxGHENgAAIABJREFUc269jLy+nTz5nuHvAfrXmVP88wuw7TwPvXuebet/P61pq9Vo\noB8E7ahy/I4bIMM06EEbaFGCxkSICGM+oJcC1o1RIKPXRSp8WgU98a/3AkcOF283mT3KbJ/prFOl\nZqSy+puXDkfZ6vUaRaroOvKUt2wxsOk5bezLEYshtXUTEgXvsdPzavZdfGnR/lR3V8m0Eju/p6cQ\nnNvzfPrvjDGRf19Tb/6pry2ZazY6v6V7XjtRs1AgACRW/QPw4Qe2rsPx+2jQnnyDwY5BmW/cJO5s\n1FXKzdqau6ctO219D4DudeYXYIvuA5q2+jw1sBl+OqosyU6dd9wyEkxzHLSBFiW4AnaICOuKwkYr\no4oQBE5XnPVk9d88VF811K9ZadIrPU8Ufl6zc8bmNZqu1IsBFebn/vADJFY0IPnBCSQ/OJEevHUG\ndcvnLPV7RphEalxz2V8B0PaPWPVY8/vmkHw5kGx/D8n293L7vHw/9JSsstXrgUuHC/+52LzG0tdx\n5HD6XTp3Fjh3FokVDeazWGVIbd2U/t75c8D5c5lzGEdUXFE70fwdMLtGs/ta5sIvmolmeiXL7eJq\nFXQblOwbbgwJILAV2QvxUn8gfdCYCBE9bQf6ohMhwysB69goyPO6VDYu7wvxC8LvZ6mq8ZJ8ZmNp\nRdwAI6MgNq8RZSsfNB2AzAaosh9u0ld8Lh1uqkgZKoy1E9PndLCvpLKw9hl9RaysPL3P4LylriNW\nMUi3f5jdt5Ln1NtfOzGnDCdWNADJJJBM5v62ZKjp/mDM9PrNlCxDg8LC8ze6NwAcv+eBoHf9mftm\n2ndM+l3Z6vXGz2PNL/V/04qhfeqkp7LcLl47qvIxNCjcGhKQa5pjvwy0KENjQmGSz2zUDJr5ns7c\ntmc2Bt1MIXgmYJ0aBR57XfyOMqlqiBopJlli8xqBSX9dvGPSX6e/azLIlBqATPfftrT4N29bWtJL\nFpu/uHhfZpvePlSNMlaYjxxO/xs0uHjfyE/3KQv1Xy7en9lm1J54QxNw9Yzi7109I9cXjPqH0X2L\nNzTpppxg9jzDZ4Xe3uJtIkil0ve1alTRLt3nkCErk/VSnfDhB+lIweW1xftMrjF7b5x69GPzGoHZ\nt+r85q2l+47O9aNqVOkIk971ZyInyWc2Ou53Zv1Dt88t+UZpA+7Gv5XCg57DY0dVIab3NCT4aaBF\nFRoTCmNFkQpNGM8rAevQKPDa6+K3cq9yily8oQmovrx4x5CL08pb2x+K97X9Ia3YNK8FoD/I5Aab\nikG51ZHzByDTAWpHeprF+PCRiA8fqdlmpiylMnPk95s4Ff0mTtVs09uHWLy0QnjZFcU7ho3o+/z8\ns5mDY32F35ltZu3BKR2FMbNNty+cOpm+5/euKNqVvHdFWgl/793i72Wmdc3e27KaK1BWk76m2Bdv\nKRl9caWE/3/23j28iupeH39nTxLIhVtICCLRgDUUIxerRyzoc1Rs7WlN6uUoaeRURbFWaw+nPRS1\ntn5tUQp+e75aq6fHesH+eiyo9YbXqtQqCCgICIgiKkpUAiEKJCHZyez5/TGzZs9l3ea2L7Df5+Fh\nZ81tzcyatT7X96MYSyT1/imQUm6/Ns27wXWPtDFljPOx3mNrxxqKIUNhSDS1QBnmpcZVhlUJFTj0\nU5S1/j5j2xG13m1H1EqtSbz75G4zn70yZBiUIcMcbaxvLtHUAoyf5O3M+EmGlzHmudy31zfD4UHc\nb/xQQYYVtMMRhQTsPMdhwywUEwNQrrhis806EZQyL0xBt0jR1eltKyp2sM24oTQ2G/HSriRB/aUn\nncmejMrIaN/NTjDc+QFQWY3qex4HALRd/l2jDWnBqOTk0wAAyTdeS7OcbNsE1Degcv7dxnHzZhtt\nAHNb4tZ7oL28zNFPAEBZuVGwbeH1AGAJCn1bNgBJGwPTgS8BtQiJSkPgTHW0G22cawIAPt0BwMnW\nQtpokEmGJAxLjnOaFK4WI8/qV6z98d4mqHMXQLv1PwEzgdjCmHrL+6K9+KSRS2DHwFLjWEaibqKp\nBdoNV7Lvn3Of2rOPAlq/6wYVdg7DXx+A9tcHgNF11DGV2ust9Ghh54fGPYyf7NlElAh9vUFD7GDW\nWb8GOPt8Zg0OAMCXHcb/Reb329+Xbtv/pfe4/V8a988ZjwD4ybm8beZ4VL7Ya9xXfUP6fXC+OXRT\n5gdaWwzwS4yS8TWJ940fKoiRQbAAAwVl4hBAFMxCuU4rmytCf1zINOtEVMpLmIJukaJyhBFaYXoQ\n0N0FVI4wBJvnHgX6XYJdUZG1oHgWmeE1AEd4I8+FeixZoGqPgTovHXOsLrzPEuqJsJTo3G+dzxKW\n6idAvfTa9HFzF0BbfKfxB2/b+BOAdSucHR1/gvH/vr1AfQOG/PgXAID23/4S+GKvcwxo/Ui56nBo\ns5vSVm/aNUvLoJz+L05F7JXnqM/M/oy0nR8CG95wbph8srHtnY0eQVJf8ZJD4LfSxu2MPMeM917M\nXihu5JHAju3O7SPNSr8lA4Fkj3NbyUDjf94zN0GdO0ePAT5+33nOo74CZeKJfOV2eA11TCWmTQ+k\niFkKg7mP47mSisdmDQ6HArd8maHcEDg8FGYImFlfwnFcwgx24I1HgC/cUbZpd81Pj4E9u5AiG80x\nq81uAiqrjLwKE45vrttMKLfPD90xJZm7kPOF0iTGeL7jUJcfcgEFZSJLiFJ4j4LP+VCklc03ZNLL\nFJXyEqagW1DYvx2PUmS3hm432W3GTwK2bnScg8QE8xYZ0bvgHUsUCXtfLeXCFJZ65xnfa2Lh/ZYg\nZV/UCUgbb5sy4UTo61Y45gBlgpkrYgoLpEgeERaCjgHrmhdcSlfEBEjMmI2US5lIzDBCn5TTvhlI\nudNX/8PsnMmSr2nAupXABd8324151iH4mm1Kyw+gL77D+exafuC4V9r9E9DmTuWMb3vPeca3kZg2\nnatM0UCeR6KpBdr61UDrDucOo+vYipipMDCfK+kv5Tjl1LOEyoty3CT69Xjj0XZP9u/Duk/Kd6Ve\ncyPbAAL2WFXGT3B6nuzzw57Poc1uit2Ln+uF0mTGeAEFiFBQJrKESIX3CPicc956cpggk/UrolBe\nQhV0Cwj7tyMjEGN4DfStG52CZMUQ4XWieBf2vtqFIXsYFRF2SPhUIMGGMwcQwaB37UqrL6QtzBgI\nau3jGT/8KneEzcmCpqV/24RFwLin0jO/A8BlmQ85f1LnTs45ecqUCMopp0N/dLGnDeArDLznyjqO\n5Q2xPHM0LwpRUCSfqZ91MMhYzUadgWyHrBZQQDZQUCayhEiF9wjiAXPdenK4INNVQ/0KzDILJRUR\nLqTub0ckZKTuvz2QsBiNx89W5TpOwcacA4illzYHkL64WTcyXoAxoPBO62fisjnQX39ZHJJmCr7E\nO+MQfEPOn9S5k3POUOPKVILV0XUAAK11h9UWVLnjKhqcscG9nuQz9bsOBhmrmc4rLBRKK+BwREGZ\nyBKS6404VaXlqtDnCrKIHKrWk1zP/RAiw1VD/Qo2Up4AwfawY4omvHGFjKDCYiQeP2df4xJsyP05\nLL0me5Ib7urHMIVqakJ4HAj4PmhjNbXkXmDjGvYxy5YgZV4DcCl3nLAa3vwpO3cyzxlmXJnHpszc\nFEe+TQwIqvjIPlO/Rqyg/cm0wpxNYpSDhInJnjNUQAExo6BMZAi5JrznsvUkjEKQ97kfmWadCCDY\nyCyUUS6kUt6QScb79hs6w0WAdxHUc6MvWwJt2ZLQ313Pm+lQJtlvnHhuqAnhMSDw+6CNVZONS3Ye\ni8IDG3rujHJc2ZLQY5mzIzZuhF4HA/Yn0x5fIAsePxPdzz5i/CgoEwVkEAVlIkPIReE9V2llwygE\nPLd5PngtMs46EVB5ES2UUS6kUt4QCsVrWIFY9l04kkgFfUV9A7OScRTfXd8W49tJgCO4mSBWezIG\nkjdcaRx76z1Zp02kfquMsZqYNh3aJx95PRSmgkmlfg1pxMl0rkkm1g8/zzwosqGIAci4xxfIjgID\nAP0fGixiaqxXKaAAJwrKRAaRi8J7tqwnPITJJ+FZHnteN92/p54dsGde5IOCwkMcSbQy2333U5QX\nwaNpjRlu5ZcpMHEUCcAbkiMDlsLAqp3g7g8tIRxRJISHBM2gwB2rCXcmiNEWpxCeyblTVjEMl4vk\n85kHRDaS/rNSZyALCgwAoCczlLcFFGBHQZnIELTbrmcKEiTEAfUNDk73OMFbnEiF2mwLEjKhCH7c\n5r1rXjMaIlQm8j6sSgJUhUm0UEospH4VscDJoDGDpvyGSRb2A6lCcAwq0qB0m5mAb4NCWQUAF/Wr\n2SYrvPodj5m0PGfCM5FJRr+M5zBkY37IkAKjzbvcqLHjbrcbFCqrHTU4CiggahSUiQxBnbuAq1Bk\nUpEAcivsKkwcrZ/76NsRvfv3cKDUpSpMooVSYiH1q4hlK2xABJryy+qrqGibX8hYrHHEaO+GhPEV\n5KK3FPCf26CkNG+YW/vu9HYJ4dW35zLDlueox44bmWT0C/st54NHOFMKjLrwPqZCAaCgSBSQERSU\niQxCnbsA2k+/D+z/0rlh8NCMKhIEcS9OfvoRRrHhhZVYXh8b3Gw2UYQGHMqUujSFSbRQyiykvhWx\nbIUNuCCVZD26jt3XBEWdpbVJQMoz0bEX+LzVabUvrUjvkwOhjqETcwXKq4zw2uPXc5nB0Bmu0rjh\njUDF17JKChK6tseh7xH2A6ZCUVAkCsgQCspEpjHtLOC5R71tWUKYAkqR9iOkhZQmEKF2DDdGPQhy\njZUrE4hLYfJ9XgnhLQ6LpfucsoXymH0tpYTk2IR7v0g0tTATkAlbE89qH6eVWPZ9hDYocCoqA5AS\nXvs/2gZA3nMpozAHHY9BxlwcIXKxzWOha3sc+h5h37jwMuB/FnnbCiggAygoE5nG/n0AgOKGyQCA\nvi0brLZsgAgSGeOY5yCMhZQmECm1x/iijZSBnwU4G674KK4Zl8IU9rxy3o7oLZbUxNQAyq9VCMwM\nybFXY7YL94HASEAGIBbcYrQS+wkdiiLkitkXyjPQ7povVYcj6qTmoMclmlqgrV8NtO5w7jy6LnAf\n4w5zY81HYUOADgePsG+8/AwAIFFtrN2pPW1G20mnZrNXBRwmKCgTmca2TUB9Ayrn3w0AaJs322jL\nFkxBIlMc8zwQhaCo7isAgP4d2+UVG4ZAlJg1B1p3J/Dy0879p58T+wKcDVd8FNeMy2KZr0mkrHMG\nVn5NwbZnxQsAIgqP4SUgiwS3GK3EfkOHwoZcsfpCewZK7Vjo7hDPiNG7Nl33I5LjppwBtD7gbQuB\nOMPcopoDD0ePsG/s/ACorEb1PY8DANou/67RVkABGUBBmcg06idAvfRa60917gJoi+/MXn9yiWOe\nVHdd/QoAn4oNRyBShlVBd+2uDKsK1VWZBTgbrviorhmXxTJuS2gcFkvWOcNWB7azi4VNzBQlIMv0\nR9TGAu+Z+w0dIs9UNQ0Kmh+DgqAvbmRSuaX4jQIdpwwaDB0uD+ygwY59comVKu75iOCwVyQAoPYY\nqPPSuZfqwvugLbw+ix0q4HBCQZnIMOyKBK8tbtgtPdnmmHdbnSzB30d1V55ApK83Yskdi+X6NcDZ\n5wfus8wCnA1XfNBr0gSQuCyWUZ43Doul7DnRvjtUeFCk7GK5kAzMqW0hHTpkGhR0SYNC6NC5phZo\n3V3Ay8ucG6Y3RsquJbp/2eNkxhzPGxCI5jkEopwDc5V5LFdgVyR4bQUUEAdiVSZuueUWbNy4Eeef\nfz6am5ut9s7OTvz5z3/Gm2++iWQyifr6elxyySU46qijHMcnk0ksXboUr732Grq6ulBXV4eLL74Y\nxx13XJzdPiyQS5aeTPTFvVjq72wMdT7aAqy/9KRTMSOI0RUflTBNE0DislhGed5sJqbKFMqzC29B\nBU2pPmeQR1/0fIIgqEEhkvc/lOKlpLVJIGh//I45Mq7olMtsb0AgmmcASTJfjjiS2j+CuMOR4gzJ\nyge62QIKyFXEpkysWLECH3/8sadd13UsWrQIu3fvxmWXXYaKigo8/vjjuPnmm7Fo0SIMHz7c2vcP\nf/gD3nrrLcycORM1NTV44YUXcMstt+CWW25BXV1dXF0/bJBLlp44+6Kc9k3vYjk8pAuftgAPrwH2\ntmVUQYtKmKYKIHFZLCM+bxxjR+ac2UoIzwXwng8o7fbtYevF+OmL1PvfsBqAK99kw2rgW+eJj3Uh\naKVqv8fZxxVrzNG8AUFongHg4N+N5F7MuJLZRyB+w1C8IVmH5rdaQAGZgN9QTil0dXXhwQcfxPe/\n/33PtrVr1+Ldd9/Fj370I5x66qmYPHky5s2bh1QqhSeffNLab8eOHVixYgUuueQSnHXWWZgwYQL+\n4z/+A1VVVVi6dGkc3T4sQbPqZJpjnnfdKPoSh9WWdc5EU4slUNkRp4IWxTWTm99KL6YE4yY4jk80\ntQDjJoTqa1znjWPsRHHO5Ob16fh3xnuyzp1n4Rqs55NoagGmN3oPEIQOhRnHYd8VUW4rmq/gviMR\ngr5jv8fZx1XqqYegzW6y/qX27EJqzy5n21MPmcdRvnMJ2K8nQqxzoM0QYb2r96IhMPFzjwUUUIAT\nsSgTf/7zn1FbW4tTT/VSkq1duxbDhg3D8ccfb7WVlZXhxBNPxNq1ax37qaqKqVOnWm2qqmLatGnY\nuHEj+vr64uj6YQe7pYdYe0jb4dyXMMiGgub3mjICCPa2eY6LInQmDuWOjBOltAxKaZmjLew5w4xH\nt/BmCNrneHcMwS6WLXCfT8DQoaDfTph3pZz2TSN3Yvfn0HZ/bgjDp31T6lgA1nEEiaYWYPLJ3h0F\nxUD9HGcfVyJFBDA8BSJFg4fU7s+Rst2jCLHNgXEZOBBc0SqggAJiCHN699138eqrr+K2226jbm9t\nbfXkRgBAbW0tXn31VfT09GDgwIFobW3FiBEjMGDAAMd+o0ePRn9/P3bt2oXa2lrf/Zs3b571e+HC\nhaiqCsfqExRFRcajp10/jm2s7fs+fh/qjFkYOvMqq037+H0M+e6MjPc1bF8y2VfetoNvr8F+AIO+\nZ4TxHPjLvaj4bAdKv9oQ2334vuasH6OzrMwI6aCgfMYsKwwp6jEX5bbOJfc67kE/2J3+vfgOaIvv\nsO4ljvEo6o8FW8x48aR/gtsUUjH6aJTbrp0r8wNvG+/57N2yDv1wjseiLeswfOZs7jmDfjtB3pUF\nc5/eV543tp3+LatN5tk4jjPxZWkZel37DSgtw1DBO2YdV/TSE+Jx1TDZqFtkgyN0iwLWd95x49We\ncwHO/J7ihsmonH934PfovqYbvHfl2ObjXfn9VmXmDuzdbW4b4d3moz+5vC3X+nO432MuIlJlor+/\nH/fccw8aGxsxatQo6j6dnZ2orq72tFdUVFjbBw4ciM7OTquNtV8B4VFy/AlW8SwAqGi+AgeXP3PY\n9yUMkpvXo3zGLAxuMeKLU6kUkpvXO+4tF65JhAj3gmoXMHIdrHsgCHMvQcajTH+SGw0PrF3Q6l3z\nGsrPvThQP7MF3vNRAO943LiWdhoHgn47su+qv+0z48dwr+CX3LQOADDAphTIgHacUm6sU/Z3TNpo\nYAq2AHpXvYLeVa9QlQWC8hmzUHrmd9D+gwsc7aVnfgfqiCMA+PvOK+ffzVQogLQiwUI25kC/iGru\n6Hz+CeN8F/PzSQoo4FBGpMrEk08+iWQyifPPZ1Nu6rqb8Z/eztovLBYuXOj4u729PZbriEC0Tdr1\n49jG3D5xCrra253bzLaM9zVkXzLaV8621NHHIjFtOvr7+wEAPWedi9TKlx37RH0fQa+pT/464FpM\nD07+OnoieP9hjvW17axzoXR3UxNwe84617qXOMZjkP5oa1dBaWxG2UWG1bizsxN972yUHh8ZnR94\n2zjPJ3XKGUi5x2PpIOE9Bv52JN9V+dtvAgC6KEm2uqkU9Pp8drTjUl3dnnfc276bfR+MMUOgNDYj\n1dQChZKoTcZVt8nAZPdG7F31KhLTpgf7zuf8CrjtemDbFmdn6huQmvMra7+g75F1bEa3RTB36Cte\nMu6RQTUex32Uv/c2AKBr3MSMXC+u8+bStlzrj6ivmQLLMeBGZMpEe3s7HnvsMVx11VXo6+tz5DT0\n9fWhq6sLpaWlqKioQFdXl+d40kY8DxUVFdSHSDwSNK9FAQXkAjJJ0xn2mnGyo2QCPBYcfdkSaMuW\nZCWxmUdhGZZdLB8oLIOOx7i/HTubUVAaU6k6G5OmQHXF9ovqfiSaWqDt/BBwV+W25UxwqVE5LGnk\nOy8950IAwMGnH5H6zpVzZ0JfdL2nzQ7aeMzGHBgUYelmU7sNb1ck9WIk0f3E/xo/5nmViQIKyAYi\nUyba2trQ19eHO+/0VnNetmwZli1bhkWLFmH06NF4++23Pfu0traiqqoKAwcOBGDkULzxxhvo7e11\n5E20traiqKgII0eOjKrrBcSEbAg9+SBo5RRiLFh1OIOnpBGhys7dH6TidIHC0j/stKl+aEzt80rg\nWhIyytSM2Ui5lInEjNlcpTl1vZmHwlNgzO+8uO4YAECPoBgggb5yOQAjrAkA+rZsMNqOTec+5Pt4\n9GtQibJeTND1qv/jDwBkVoEpoAAeIlMm6urqcNNNN3nab775Zpx22mk488wzMXLkSJx00kl45ZVX\n8M4771jF57q7u7Fu3ToH+9NJJ52Ehx9+GKtWrcLpp58OANA0DatWrcLEiRNRXFwQFnMd2Vhk8n1h\nyzjMehm8IlhxIQrFLxeKLwatKizL3e8GryhZriHbyr2UF6G+wRPK46Vidc4rcdXFsVjJygzPu97d\nCf29zeEVGPM7Tz70B6O95Sq573zbJqC+wcqPaJs322izIe/Ho0+Dii5BRSuzDxBivepL+tu/AADZ\nn48OZUSmTJSXl6OhoYG6rbq62tp20kknob6+HnfeeSdmzpyJ8vJyPPHEE9B1HU1N6Qm+rq4OU6dO\nxYMPPghN0zBixAj87W9/w+7du3HttddG1e0CYkQ2Fpl8WthyAZaVnFMEKy5Epfglmlqgbd8KbHVV\nNR8/KSPhTYGrCptjVXGS0QDgL3q8omRhEMdCy3vHmVjYZYRwZer0tHWftLvCXGjzSpTVmD0VwLvT\nBCOElUxpbAYmTQE2rnEePGmKcJy7v3MFkmFH9ROgXppeb9W5C6AtdkYfxDUe40DQb9UOde4CaLRc\nEoL6BqhzF0j2R2690uZdDnTs8bbbleLKaqgL75O67uGKgrExPsRWAZuFRCKB6667Dn/6059w7733\noq+vD/X19bjppps8FFhXX301/vKXv2DJkiXo7u7G0UcfjRtuuAFjx47NdLcLCIBsLDK8axasEmzk\nveJ33Ne8ysRxXwt/XojHTdCqwoS3nxaqYF/0gsb2+0Ucgj/vHWdqYRd5EVKUxGV3mAttXoky30jW\n86C3tyHlUiYSLsYh+7vyM3Zo79iuSFj7Vw53CrGcc+Yagn6rbqhzF0C79T+Bj7Y5N4ypl1YkjP7I\nrZHKNK/iStunAD4Kxsb4ELsy8fDDD3vaKioqcPXVVwuPLSkpwSWXXIJLLrkkjq4VEDEyJfQEvWbB\nKsFGril+vrFhNQAXr/6G1cC3zgt9atG4kb0PljVTc4XcqHMXOBa9TIVyxSH4855NJhd2mcRlQlva\n3d0N/aUnoS2+w3si+7saXRdpvpFM6FQ6kfoiAMDBpx9mKj5+x47sO86F0MKgiHTOGTfRq0xQ2JXs\nCLpG5vMzzyXkkxct34yfGfdMFHDoIhsTnr/FMn+sEnFPJLmu+AWBW7DT39koOEIO7nET9D78hEe4\nF724YvTtiELw96XcmzStSstVnt2jHv9cLwLJJ3jbsPhb7Fp727jzCobX+AqPkYEwdEoikdr9rmTH\njp/5MRPjMQrEOuesWwnAmZyOdSuBC77PPCTMGploaoH26SfAW687N3xtak4981xCNta5qJBvxs+C\nMlFApMjGIiO/WOaPVSLuiSTXFT+/wmRYulUeZAV7qy+cZ8dUKCqrgW1bvOEj9r+nN3qvFTBGHwjm\n1RN9O6JnA6Qpe2WuF9n45yTZpvMJbN4gs83vXBY230gYOiWRSE17VzL5HX7nxyhyRuI2msQ556Cn\n25uc/nlr4D5Jzbl7vXkT1LYCAOS3RyefjJ9AQZkoIAZEmZgY5pp6d+chFdsbNfJB8ZO9/yh57WWF\nbKWxOdCzo3L3X/ETYOtGoSVcR3Q1QaIS/GXPqzQ2W+ekwX6+HrMQWGTjXyohPpgQHikEzEKW4mP3\n6uxtE85zmHwyAKCo7isAgP4d25Fa+kdvTQvXcbzxLBNyJUImrK9xzTmYeDJEyeksBB5Xn+4A4Arn\nNNsKoCNfvGhu5JPxEygoEwXEALLIlJx8GgAg+cZrvhaZINYqmkVPqT0GoAh8BLk8mWRqIskVxU+G\nPYcgV6yZers3DEbm2bG4+9VLr+UnCt9/e+Q1QaIQ/KnbGe9YqaqRE+y2GrWIouLRpyqWMkL4JGP8\nZayoI4OqWYrilgGlsRlo3w00NiO1+hVPW+D5MWDtCjsyZX2NY84hioR9Gy1hnYbAyfulZVBO/xdn\nOOcrz0ld83BGNtY5v8jnkCygoEwUEAfMRSbRuR+AuXD5WGQCWasYFr3ErDmxWiWiEmyzNZHEXQGb\n9nz8JJHS4/dzw5oZ+NlxuPu5i55PCktZhBX8aaA9G6YlHBQPiIkghcBkIaM0EoE7qALnd37gUTUL\n+0rbXt/gpJwlP+z7SdTZoMIcjz33/ZfR98t/4ns8ZspoIvOt9ry1CgCgUnJ4eHNOlOuVaFwpF1wa\nWzjnoYy417kokM8hWUBBmSjARKTWXhLXe4NRjCtx6z2+FpmeN43ENl+TM0fQitMqEWWthKxMJDFX\nwKY+H4pFU4Y9Jx1PnyPWzKDPjsPdL1M5244oaoLwrhn426E9G4ElnCbUWteMafzL0MaGKeoYnAWL\nQmEq6CuCkwg9AAAgAElEQVTNU5a4bA70118OXWeDBjL2+rYY95iAeDxmzfpKGY+sOYemwPKZzgLM\nRwENA3HNAYc8Yl7nokK+hmQBBWUiJ5ENSrAohGL7QuGYpM3JmRReEn0U9sVJFr7CGABr8Qz7kUYp\n2GZlIonJ2k3Qu5aiGNKSSCXYc9wUllm3ZgZ8drRQCKstG4se55qBQxYZzyYxbTq09auB1h3O/UsG\nsFmuYHou3tvki8NfFjyFKWxRx6DzQ+9Gw4OTMK3kTCEcNq+OGZLl9vjJ1tlQzXwKbcd27jtm9UXG\ni+SLfY+wsY04krqvL9DGo585h8M8FmQ+CqsU5BttaNYR8zoXJfIhJIuGgjKRg8gGJVgUQnFQ63qY\nxSmO/vhB1IJtpieSuC1dZFzZFUNeNV7q+zJDNTxhMFmwZtKSYe0I/eyysejxrmk+A6V9FwD5kEXe\ns1FOOR36o4sd25Sm70F/+81IqgrLgiegu40NgbylkJ8fZOZAkeeGeH5oOQwydTZ0ez4F5x2HnVdl\njSYH//6M8WPGlcxzyYI3HoPOOR5kMLY932hDs4188ujkQ0gWDQVlIgeRnWrE0QjFQazrcQr9iaYW\naJ98BLiqxmLSlEDnjNtNn68TCYGUUOSGgP5UJlQjloU7C4J9NhY97jWJF+kn/2a0/9f/F/4ZVAwB\n4GKkqRhihHvdeBXQ9plz/5pRsXgk/Mw7fRsNy7TIWxp3UTLt5h97vTqj6xyen977zRyGWekcBpkc\nFnc+hbZqObWvPCUM5j2kzHtiQcZo0rvBfOYUZSIKyzy35gvHSwbAV0heHF6EfKMNLcAH8iQky42C\nMpGDyEQYR5xCsV/rehSLEw+J5iuQcikTCfND9X2uuL0deTqREAjpRkXWVQb9aVZobPPImhU1WCGL\nms+QRaogxRvjVTVeZSJGRTrR1AJt54fexPCaUVTLtMhbGmZ+kBnjVK/OKacbx1u0seuMv21ttCrf\nQdmcopgDiXLDC63S9+5mHh/UMu9gXpIoIqmMm8AOD6OsW7R7j8OLkG+0oQX4QB6FZNlRUCZyANlI\nSotTKPZrXY+yLzz2INmYYBFiFWzzdCIhECmG2LaFyx7Doz/N11jSfIH924nqm6QKUpQxrt01n+29\n2rIe2uym+JKwZ8xGyqVMJObcHNgbFmR+kMmJIMo24MqL2P4ONJeCAbi8gZNPhto0x1nl2/RkBJnL\nQnt8KaFV+mMPChOiSbhbUMu8ezyqcxdA+8m/AQf2OXc0c3h0iqJhKZmSRSSj8CLkO21oAfLIVyOW\nnxzXAmJCoqnFovajIU4mE9p1o7SuVzRfkY7BFfSFJBA64DMcKbn5rfSC4eqP3tUJvatTqj8i0BaN\nKATbXJxItN2fpwVNAaTG8mVzvO12+lPb+040tQDjJgBwKqlEUSVtccLP/ecz7N+OlLfwqYckzrk+\nLUyZoI7n7i7hufSQ3yz7vN5xlVpyb6j79zs/SK8BhAltzLEoHnOskc9SWiE8Vr3mRgDO92HlsASd\nyxIU8YHWZkPqqYegzW6Cvmq58Qz37EJqzy7jWe//kn89W94MdZ43kXxnYzp5272NMh4x7RveHWvH\n8vsCGJ4diOcjXl9lkS0ZIZs4XObdQwUFz0SOIFuUYLFYexmFl4QIsDi5YbcCuQUi2ZhgGeR7boMf\n+K5GzQodmXyyYyz4pj/NUgjY4ZLsaP92ovZMiMIxZEJO4sibAECnDf3H88LDeMpNkPkh0dRCZ7oy\ncyIAUJnQtLvmA8uWs/tiCxOlvY/Ac1lZhec40sa7R1HVdSa2bfF6ryiWeV7itpslC4DllbAXkcTI\n0VCOmxSoBkmKxRAVE6HIoahIAIfPvHuooKBM5BBkBPuok7niEIoD0ykGWJzcsFP4xZrfkOe5DX4Q\nyE2foNQuJm1Bn12WQsDyKdkxzPxgFzSD5jGFCcdQ5y6Adut/Ah9tc24YUx+fIgEwaUP1118KrtzQ\nchRkxviUM4DWB7xtpG8UJjSldix0RjFAAk/+h10oH10XrIBaSvMK0+3sPAfrHjhCMSjtFiSTnpNm\nsrwy40o5QoiycmoRycSt90Db/i6wdYPz4PGTHWFijvvihI7R+uoXh1OoZz7NuwUUlImcgoxgH7m2\nHqNQ7HcyCLI4yQgvgSu88pDnuQ1+ECjZr5SiGJpt2SrYFFTQzqdkRz/zg9S3w0BsybnjJnqViXET\nhf0JA+a4mjYd2n9cDHQecG6sGCRWbogHwZWjIIIyaLDhQS0pMRqSSSiDBod6VzKkB4E8ySHmQF7V\ndW3DG8DOD50H1I6FOneBVNJzylwzVMiNR71jL1hFJHHcCV5l4rgTjPvlzEdxeREOR494Psy7PBwu\nNUEKykSW4JvlxETk2npIoZj3ofieDAL0RWaxCFrhlXvdHMxtiApRJPvxFMNsPTtZQTtTyY7ZpoyU\nErQ2rAF2fuTcUDsmkjoCVKwz6jk4Qk7WrQQu+D7/uLgwZhywaa23TYC0B8EWOuYa4/aibJ4xl0xa\nP3WSlMxRCrgQkB5Yl/TpSQ7zHXOF4q793gPMNpoSom9e560HAZf3obIa6Njj2M4bj5ZysWG1p5/Y\nsBr41nn8G2T0NbQX4RD2iB+qSeaHS7hWQZnIEmRZTtzCdNTaeljBzn4fYScDcl0Hq4zMoiYQXngx\n+gSHi/VABpGEh+Wg50ZW0I6d/tfqT2YpI2ljXPTtaJ9+7FUmqkcK+xE4ZLOn2xty8nmr8Hqxoe1T\nAC7lxmwjCGpQscf2y445lmUegmNlDCoZDSvhCcWVNUBHu1OArzTmapoSovzzt6EXc0LSAI8iAThZ\nsriGEXc+DSO523P+OLwIQfMR8wCZmnczjcMlXKugTGQJtAFGFZz3ttEpEykCut3SRUPcltBY6SQZ\nkKFUlIkJPlysByy4x0ZYN322vA9RecoykeyYacpI1hjnCv68cDUOAodsTjwZ6qXXWu/REXKSDRzs\noig3Ox279LxuJj+ferbP92G8f2WGfI4KNzxoxUvAF+3OA4dVSRtUMhpWwjE2KNU1wPgJ9HBXWrL8\nS096k9YlIVQkTvsmNZ9GCjF4EQLnI+YJDsUk80MlXEuEgjKRJcgOMD8COo/Fwn7NOC2hUUwGvWuN\nUIeoQjVITLDjGI/H5/CwHrBAGxv5mOwXpacs7vuPYpHxMz+4xzhPgLWs2DRF3JXHFGXIJgkv6Xz8\nf42GCy612rKCiVMgUm561rxm/Dj1bF/vI0XOCfn3aAncJjGF3t2ZVgoGDfEqE4OMauMsIZxW1yET\nYSUyVdetdvt8zUiWB4tBCWa4noglK0g/RYjRO3uorFe0uSMf1x07DtVwLREKykSGEGaAyQrodksX\nDZmyhMoW82HB4kGX3F+KUtF9jDt++TCxHrBAGxv5mOwXpacs6vuPa5GRnx/kFH/78TLJuXbLvIWQ\nIZu9q/5uHHfBpdS+xQGaYEMUGbuS6lZu+s2EccJfxnyuldXCqtooHwR0uRK+zfo79v307s7078V3\n0JUCAPjkA+O4SVOguuq3yAjh2RB4uInNnG0aJ79HKynlsmTFgTi9s4fKekUzYuXjumPHoRquJUJB\nmcgQwg4wGW3dbumiIVOWUAyvgQ75yUCGvk/4fE45HbqrCqxyyunM/TNhPcinPAzq2MjDZL9IPWUR\n33+ciww1MbW7SypEEpOmCCsZ88Iqem2WeYKwIZv6PkEBsxjA89zy6tcQOO5t+jme7Ym5tyL1wO1c\nulmUDwbWr3IdmBDXZ5CkTXWc1nxHh0xYSVcns42wZDlyLQYNzmDnwiHXrd3BmfIoBs48XHfcOBTD\ntUQoKBMZRJgBRtPWU7ffBLR95tmXumDbEbMlNHX/7cLJwJFkHYWQVWG484vqvgIA6N+x3Wrzcw++\nrilALudhSHmYaNbMHEv2k/OUeQU7qWTgGMIUMkkZqdSOBRqbhWNcb29DyqVMJMxvl4Dn1ez78F0A\nbCOGdU6RQAxvPQQ/BoWw4N2jb0PM3nR1ZCBtUGEW5zPrVpAQKlqtHWGy/O9vESqF+RRW4ltArRwB\ndOxxJW6PMLbluYCaifUqiho1tG/Hdx5bDpJ3BEGufldxoaBMZBiBBxhtMmzfDX3IMK6lSxk3QWoC\n8juRcO/DR4gDN7bdBKtAlgPm80m9bsYUNzZzFRirX4jPepDLca1BFye/bvq4vTNRecpooTpxhSmE\nXWR4OQruImmJWXOEY1wqWdpWDDKMJ5H5vgaWGUxOHOgrXwZiFJikivZJ1uBgVUfGtOlQzp0JfdH1\nzmPOnWn8L6i1wxs7ieYrJJRCdlgJFFOk0/WcCCvxa4zhJm5LrEm8sZELXub416vgxi8ZRdxPHpsb\nUYWHZfI95nu4ll8UlIkMQSbZkTspMCZDddocrqULkHNj+51IeB+KjBAWVWy7+7nq5IfZpq1azmWz\nidN6kOtxrVEsTqLJORPemSg8ZT2UUJ2oEXoOMMGjlaYVSROO8YDJuUFB60/ipjuQuu0GKoUnAKCy\nGurC+0Jf269gw4LS2Ay9uwt4eZlzw/RG69mzck30lYbiaqeb1VcuB45tEAq9vHlXjkGLE66l6+mf\nZh5GNsMyet6UJ+IAwH12cmsSL8wtN7zM8a5XwY1fvLUuDsbHoMjoe8xzb5hfFJSJDCHsR8SbDHmW\nLkBuAuJNJEHZWnhIrjeKASktVxmnbmqBtvNDYMMbzh0nn8x/Lj6eK40lKkrrQa7HtdIQdnESTc6Z\n8s6E9ZS5k2j9INNWSx6tNK1IGhnjJSefZuzzxmvOMR6AISdIjD4B65tTF94Hbe6lwJcdzgOGVkai\nSAD+BRtQtlthRc8/7j1oaBWA9Puwe7ysZ7xtk5dudtsmx3F2ONp4864Ug1Za6NPf20S9bztk9okL\nfVuMvkoTcYSumcSzrueGlzlOa7cf41cQSuooGR+DIqPv8RCuCUJDQZnIIIIKzCIQS5c6qhYAoH22\nM23pgj/Od9pEEpStxQ4pK+DI0d7tCbF455ftyrE4RWg9yLbVJQiEgqYAosk5Du8MTXgn91F6zoUA\ngINPP8L1lLGSgYPE6Mtau6IaH3wrIDuRPtG537qOfYzzhDAWKxGvQJi+bAm09zYZOQCShgimJwQA\nvuyANrsp0jwmWcFGb/cqVJaSusEwiNi/HXd15J4V5pxonzvrJ8DOCOWrloZP2lTtrvnCpHcmbN5t\nUQ2jKBAFEUcY+P6usoEI16s4GCbt8DCX+cxjiwOZfI+Hek0QNwrKRIaRmDEbKZcykZgxm7G3JExL\nV/GRRwMAtIrBlqULQGiOcVoIiF8rkIwgpXfsBXa1+iqQJVW0zgUtxiTjvGNxEAiaIrgn50x4Z6jC\nu3kfauVw6xqZcin7sXYlmlqgdXcCLz/t3DD9HGaibKBwHPu2ySdDbZqD5A1G/ZnErfcIxzg3j4kV\nikRgF0I5IVkEcdKUhqWyJkoqta4D6Zft23FXR+7/+AMATo8XrW6GbC0Nv7Sp6jU3ct+l0tgMffM6\n4KP3nRvGHGu9Q0BcwygKZNoYE/a7ysqcHmFycuhoCc5aB855AXjnP/gL9ZRFLkQL5IpXK24UlIkM\nIxY3pWnpSl53OQBA/c19TktXyMU7TAiIHTKx7bzkQz/ntBCCMjEo4mZxiDSshsTaSwqaUgtwiBAY\nGVAnZ/M+en97IwAg8dP53PuIUnDxa+1ShlWlc3tsbbRzyoTj8J43AGDDG04F2vzNi4mXUv5ffBLo\nOejcMLDUIYTyQrIc1yO1Ap5+GNBTroslAo8ZPwn6KCs3Gru7kFpyr4MZiVrXwWRKsn872s4PI/N4\nBYVfogmtv9+rTHx1kuNPUQ2jKPoaCRGHj+vFsXbEjahJIcIav3gV2YMoGlE/11yIFsgZr1bMKCgT\nmYaEm1JWWLRPvtrKF9MbiLCw8sVAHOOobxAWV/L7EVKTxE1Y16o5EuqsOek+SlpdEk2MonWDhwrD\nMcIuTjT3f9wsDkGTyFgLt8NDJRA0ZSdnmmAQNeuIfXIm47nvgzRNqWiRNbwE7CRaFsJau/T1hpDq\nGB/r1wBnn2/t4xbChYo443mDcoxMH8k1ecUgtbdWAZ9+7Nw2fITjT98L6VHHAB+/720LAeaYNec5\nC91d6d9uilUaNq6hKmnZhm+iiXVmLplqmos0zWg7//vWvrwaRlFRikYl+PH64w7ZDfpdsbyI+Ygw\nxi/eWsdTNLRPPhLSGEeFTEcLROUNybdxVVAmMg2ftKk8RDH50j541I7hWzoDgMmvTmALjbBD1upC\nLVr3zXOB3p5YrRJU9z+DptNPyA0vRjmo2zSqhVtmco7SOxMkHEFa8R1aJddmQyTfnTvs0BUeQxPC\nec80iIVQyvJI+a4waAi7ls2nH0sXprODa5X++P3QORNUBqnL5kB//eVACdi0dr+IS1iQrWbv9r5A\n09K/d38u9LCQOTsqStGoPBO8/tAKLAb5rmSvlw8gY0M1azRpO7ZTjV9+iVjIee21n6w8Ngka4yiR\nyZoPUefG5cu4KigTGYZf2lTh+QJq3dyJmxLPyDq37ILIVSQAYNsWaLddT1UopGAWqHPkW1QMQeLs\n82NJeifo3WDw7yfsygSHplMWvBjloG5Tv9ZugiCFrqL0zsgk+wWGmUTrGDeuJFo/fZJSJE77pjdR\n9pOPxImyk6Z4+uqmBUWR+Y76+4QWQh6488PWjfR2giChhTGHI1jPp6TE+D+ZhP7e5sAJ2FGEccQl\nLEhXszdrYTDfc2U1Oz/GkRcTDaVodEIYp8DiDsPrZfewREm3m5cwx4a++hUA7Hwz6fwnstaR2k+U\n82a6BkNYohHAn/IfhTck38ZVQZnIQfRuNATfhEmbyoJM8nGomGiJEBDZBVEZNwG6wNuhjJvA3c4F\nL3yMxgjlajtoMmJh3ERfl9X3enM6eDSdsrDHKEflNvVr7SagsXkJF4OIObaDJvtF4SVgHhfQ2kVN\nlP3Rz4WJsrRCaB4ihf4+66c9th+gWwiZfZSZHza8Aez80LmhdizUuQsChbklmqJnu/P0I5m0flrP\nRyIBe4DpZexd/oxcGMf2rV6la/wk19wZjbDgp5q9naZSu2s+sGw5+8Qde+gKhcuLHDWlKAYPBfZ/\n6dxeOzZQDpMMQxRG17E9yT7pdvMJfmo0AfL5T26mPNp5yTPPWA2GkEQjQIAiihHRrufLuCooEzmA\noJR4UbAx8GIXeTzqBLILYuyJUDwLickIxWOJ6n72EfM8fGWC5WHhuf+DTAb2GOWgz05q4Z58MgBg\nwJnfBgD0Ln/WWx2aVtBNtMhGyDpCQJucqUw09u3L0jSl1HNSvASp4XLWqqita8L4bbMQmqevAiIF\nooTQLIQsiDyJ+rIlwJhjvfdwtVHvJrCiFTHbnZ8EbOp7NMe5crAzvb+MddWWsG3B1RaVsOBnfrAL\nRDJMTxg/yVPDCFUjA4WyyfZV//Rj4K1Vzg3V7G8qTPFBAEDVCCORnuZJDki3m7PsfTb4XVdkx6vs\nNxf1+sCFT6IRGvwq/37Xh1xgngqDgjKRAwgbv85LkhRemxe7KBECwptgaKwi2t+eAHp7nDsOGBj6\nA7HYYOxsHaSoX0oTskT1f+h1f9MgzP0AjJAtRmIm611KKSkUK2GQ3Ab7sUTQRMfudJtL0KSyeQmU\nhahZRwD65Kz887ehF7/kOxcnkn5GSKZAwBPCWVZAUZy59Zv8YFge7ZDJcVKmngX9ozv4YVclA4z/\nk71SilbUCppMHD7XSkpYwuYZ/UksvN8TxkG1aHd3G7dvr0Gxh17bJAphQTasghbqSPUGlVcY56I9\nu9f5QlgYStFEU4vFRChLES4Mg5QMu6N5koPS7eaqwOeGyOMbtdLIXa98rA+BSGp8EI3Q4Fv597k+\nxG5wjRkFZSJHECoGm5Z8fMrpUtcVLd6eEJAVL0lPMNRidxWDvcpExWCpvsrAd1wnQU+31PlTTz0U\nODmd9y5lhLfEZXMsLm7rnAJrr6y1O/nTS4z9f/sg0/Lm9pR5rhV3TQfG5KzOXQDt2mbvOxxYFjwH\nRwYRkikQWEK4jVnHLUxHlbgrmlvUuQug3fhDoO1T54aaI41Qpvtv984P7rCrZK/1k4QVca8rE1bi\no4CaHysprVKtVUW8oz19Tpf3jWrRHjwUyin/7KlBoRw3KTZhwU+ysEMgooWBjpsAdO7nK5PjJkRO\nKQrIGX/cECooS+4R1nbxIyxGEROfK/BL3mDtI7jXOJOeM0FSE9pTEGB9yOdxVVAmcgBBcx+0eZcz\nE+T0RxdDe3QxUFkNdeF97ItzFm9mCIhkfQoacwaShiJhLwJF2qKAbFyn/tRD1KJ9bk+A+9lJCSeU\n7TKTAVOhMK3rZCLiWW39JktbwpItRlmpHWvEw0tAJNjGURODwDE5V1YBn33i3L+Sz8oUFjxvGEHv\nWpN2U7DoeeYAG7OOWwj3y8oFynbpxWn4CK8yQehfzffhEMJ9zA/UsSGxAPstoCYK5ySwP1f99ZfE\n80N9AzBtOtWiTeZOR1iFqbTEJSwQRbR40j8BAPo2vullbCIQhQCRMKOSAQ6FEAAweCjUuQv41cEl\n+0plEKKNK4mQFK6CQqntgs9aQ3mKMskQFCd4BsUwwm2cSdaZIKkJHUYekGwnX8dVItsdKMAYtDRL\nLwEzFGHhfUboCwsiRQIwJm7buRNNLYZVCk5hyZrYp01n9tfdz74d71vsGamnHjIm7gOGpU7v7kwX\ngjqwH9rsJsPqHxLJt99E8u03hfuFeXaJphagdqx3g5kkSPvwZScD5dyZ7Dab4lfRfEU6JMmG5Oa3\n0kKRCfukTib21O3/B9rsJuufEQSjQ5vdJGRNcsdgu68n6k9QcCfnskFG34YMgzJkmKMtbvDuMbl5\nfXrBiAgHlz+Lg8ufBSAXxqNT4velFyczYTkx6igkRh3lbLOIBmz3LxF2Rb5z2nOTXYB9P9MEZalz\ntdnPq85dYCgLLNjC53o3vmGRZpB5Tl98hzGn7dmF1J5djjbdXs/CRCTCAmHl6e2F3ttr5HqUlXPX\nFu49Al5FAgD2f2nc29I/AgASRx6NxJFHA7B51mT72tUJvavTMZeR993z+nLLuy3j9bRXK7eMVWab\nvbYLmQOR7A207rqvV3rORSg95yJHW15BsK4EXs8k1iu7bOEHfteVoPcgK+sEhex6nQ/jquCZyAGE\n4ddWF94Hbc7FQNcB54byQWJFArKLt1wRJL27S8xNzkDQjzOMK1JdeB/duyOjhHXtZ7aFscjoJqtU\nccNkAEDflg1G27ENkm5TisWG4n3S168W9gVj6gEzX8KCh82LbyHKGL3dvr1G6MUXewEAen0DYP4m\nyASvfybIFPq2mVZd2Q5SqJ5JuJzwuzOfa5GpeCcrBnmeq8My7+M+ZL02nr5zCqgxUUYhYShzxuHb\nw1x850VBfp7jJnxLghrqZc4P/T8yBFvVrAIv8oZov7+F6rVRjhojlW+lv/Jsuk2Q2O+HQYhK/CBz\nTlq18mHDqaxtocJKzHlVrRxuHRMrI1FcEKwrgdczCQ+TbLiSn3WeNs+HWZMzXjMp00xXEaGgTOQA\nQifezPwh8D+LvG0+wBO0eEWQHOFKgrhWANRkOPf9RcHnTDs39bwXXuZ9dhdeJrwuKmuAjnancFIZ\nAS3qtk1AfQMq598NAGibN9togz/FzxH3a07qhP5WNhxFLyn1KhMuNi8RjXHgmhicuHg7ja9jct6z\nCymy055dAOAodpYJXv8w4zHRxKjIPeZYK9zRDhnh1U/NB+r3UT8B6qXXou8nhndM/a8/WwmyBO53\nzEzqdVG8knmF5h6398UPgxoLtDh8ff1qcW2PABCFltFyTfzmhXQR9rlL5zCTTMm9GfS353jOoXd3\nssfQxjXQN64x6pbY6IYBAEXF9PEtkdjv5/ugEj9QEJg9yGRtCywsmvNqz29vNM5pKm/5BuG6EnA9\ns3suAVAp0uNgg6TO8yHWZLsHCgAOPv1wbDWTssJ0FREKykQOIHTlz5eNGOKESZ+X2tNmtJ10qnQf\nqMnSJni83Y4QClpsrg1KYzOUqdOFScR+hT55JpP0ebnP/H8WQfufRfx42eoaYPwEepJgGFpUU3gj\nUOcu8AhvdshabAAn/a0VwrZ3N0DePcHoOvaz+esD0P76gKdZKNhSLEhchYETF++4D1+LTDAviVvI\nDkNFaf+Wqd8crfr2106FcvyJfMXvHy8A+79wbhg8zBg/d82Xqt3A+j60lS9a+1gCqq3Ngv3+R472\nbt/1mVRif6KpBZ2P/6/RcMGlUuQEwkR72jcpUKhFihjgv9gdsy+u+cGuLNDQt8l4V9JxyqQeTlm5\n8X93F5TaY7hF65TGZuiftQLrVjg3TJoCZdToUJTk1G+2vkGoNMfBHmSFkNiejYywSI63Kz1RklDw\njGpxeVmpkPGIc+Zy3rxLvJOimlrWdeGfsUz2HpgwFZHiumMAAD0hPVBhxmouo6BM5ABCeyZ2fgBU\nVqP6HqMuRNvl3zXafMCeLB1GWFIamw0Fg8GcIZNE7Efo85O8HjQcgwqOCzcM3ahdkeC1Wef1Y+mj\n0d/SYqJHHgnlhFMiFbJo/eEJTNTK4ibc9+FXmfRfOVyOccOCpDdA6psDgL8+4E0etUFftgQoLvFu\nONaIh5et3eDn+xDdo96xF9jV6vTcHTMeyj+dKhcCtervRj8vuBSAmJzAugeGUMP7JlmCrbDOBqPd\nLQwT2EPLWH0hECkLdsIE4bsC0sqkLV9DJ16MQUOAA/uc+5vU4qmVL0Nft8JJcaslwxm/QPcGoHZM\nYKY81jndhiq7EO755lzPhsU8FjSU0S94RrW4vKw0yKxnPOOP3yKCYZPeafN8KApwwtr20B+M41qu\niqVmUj4kWfNQUCZyBImmFmiMirLCSan2GKjz0guquvA+aAuv9+zGs2aQRGkV4RmLUi885hF+lGGm\nxZXHzW4iCoo+d59o5000tUBb8TLwhStnYli18JnLuHAzBWlLB43+lhJLivc2I/GDecxQFRbPuv16\nMt0T05gAACAASURBVP3hCUy0yuK8+/CTw+OX17/nTW9sv+iZy3gD7N9cKFRWA8OqgA+2Oph88KWZ\nP2K56S8EABx8+hGq5VVm0bewbQs3ZDF1/+3UsCKd5tEwYRdC9X1feLYr5870FFBzExb4ZXoC6GMn\ncdkc6K+/HEiZ8hNaxoLuqgAtFeo1tBL4ssO5w/RGKGXl/Ll8wEA2tTitcjDJlQgQyscdV5TcHtZ5\naZCJi/fLhBZFki0PfsOLZbZlA6Q/yoxwntsg7zi19I/eudZ1vTDv0r3WK/ChiDAQJ9NVtlBQJrIE\n6iRSVeNVJiQGl12R4LXJhPl4qA8ZwgLPjW9nzgDMD2X9GuDs86nc7GEriiaaGIX7Bg+lWwrt15re\n6IlRT/zsVup1aIgqdCYsqML0a3+jWkkdz1pJeBMTn/srO2xpwxvMbXZPkIzlxS4wSQlLvG3lBnOT\nOqbe2PbRNii1Y6WFHh6Sm9Ya53Yf70eBAZjPTjZxV1/+DJNoQfvNPGOfdzak9ydWeoabPsUK8ZHs\nDzdkMUhYEbyWfcezMRnUqOQEJuxCjSxoBfaYAgrBti1Mi75saJnvvBARjjjKq0wMrULiW+cJa80A\nTmUTFUOMnRiVgxPTpkvR7bpD+WQEeJ2WN+QifqDOnzI1SihF+3yHnESohMh4H2hGtaiqp0cFOylC\nWGMkF7R37EO59YNYK1KHyavMURSUiSyBOomYycz2RB8340gYIdS34Fs71qtMEIWBxxMOeLwPRLBJ\na/npvkRRUZRauO+b5wK9PcJkpzDMKlGFzoQFLSFeOfffoD/1ELMWCSqroTS1eIQ+qpAgCbeAUmSO\nj/4d25G6/Sag7TPPMVICk1oEaP30bZXVUMZNAKpGQH/zNasfeG8TErPm+BYWpBRtFtxCUERgCuBd\nB4x+mYJq/3N/BQCo1y2yEkxZbnoWy49sf/RPPgJA93jwanBQPV5FJUB/kn/R1h1McgICP0xPnvds\nD/mTqbPiViTMPmqzmwwjhQvu0DK7oK2MmwBdFOIjCL3C1g3eNpLjROmPpfhRWInsxQdZlYOpeTEu\nul0aIxNzjjEVEe35x73ndeUSBS1OSmjDFVuMvt/wKOseJJQeEWSY4LjKfcShVTz4Vn4rq71rz8Ay\ncYggj02R8Y7jqN8Sq+cqTA5HjqKgTGQJ1GIlJuOIw4LoYkgKI4T6ZZ3hCtqEJ3z1K+n9XcXuHJVh\nh7NDKqx9KP2RZnoyLWmOcJ2KIUicfT7fKifBrGJHVNaKqNzUIlpEAMDAMm9oEGfCVo4aYzC58MCJ\nswZgjY+UbXxI0dGyoPVz78Oylj7zMABAtU3OfuNTg1rW7Nt1mqdsdF3gXBQhXF4PO5MP6a9dkCIL\nGe+7A6Xdsd20CPISE6nzFa3i8sQTgR3buYovxk+Gh5zgp98Xe3sYCdpSVnJa6OngoYArDMmDvW0A\n+AwwdkFbVnDRHrgDeN0ldEydDmV4NV8ppCi5lldp0hSoTS1OViIJLxJ2tXrb1q9yMKixGJkSzVcg\n5Z5jqqrZCrupFNHy36xz0ubsvW1ixq5JxjnshhheeJQFGlkCrY0DP2tyFOFzYWB/BjKkCInL5ng8\nl4mb7kDqthu43zmPlp2X+xBHHkJouYSxLVQOR46ioExkCTwKT16iDy1+mwUpS8eYY4GP3nduJ8nS\nFEHbbrEC2Dzhxj3aJnyZSZ1nPTPBVKZ4lbx5k4zPaqtRWSuiclNLCUSjjvJFfyt1zpJSwM3qNOUM\nIY88F6KkXtp9lAxwUmCaoLXZYS1ypqDJTcx0QTdDuTD5ZGYoS4oRh544+3xoDIICtflKaD+bBXzR\n7tw2rAqorgmsaHhC/VyKL+v7UKpquHlcRIHjzVdUpbmUUvNh83og2cO+iY49luXd/q7U3/4pMNOT\nzHumQqBIyCpadkFbltEPvRTChN5eQ9GgeXxqRlG9gY7+HjXG0x9ecrpI0STbWOFqxFAFAImRRqJ8\natenUuxSrPw3OxzeMJnkdFOpodWn4Ckv2GAYRxzjeMNq4FvnMS8ltSbzcpEEuWphIKIjdj8DESmC\nNReUmOQQyaQRPshSJACgYw9STz0U6H6Id95OFhBFHoKMkiLDhpkr+S1xoaBMZAh+KDxpFkSCvi3y\nlIBSFtYBA6G7lAkrWTpA3DNrwvfjCeElijKVKY7b0IqJVsylR9c94Rh+EqmDxNnGGX8pTAY2k/Fl\nqYOphfxsELIOiZSCvXuo1lX1sn+H9qs5TOHVIhUYMND4v7fH8EjVDwksaCtmtfcgEz6XJYnhKQOM\n74tJUDBoiFeZGDTEWLivudDLvpVQgZQm3WcHKg1rNu19WspWOaWKuDmOrG+HM1/R6pCwaj54PDku\nKJRvFZBnegICCJo8VI3w1tYZP8mhaPXcfYtxuat/zswN85UXse1tAK5xZbbRxiN6OQqaCW6eCqU+\nhaVoBlReAKQ9ly88YZzTFpZIDYGqGSXMf0sL/nLsa36Y51j1dDzGNiKQMyCzDvJykeJkARLREdMU\nOBopAqpqnGMoaQtfFIQPyq6BvJwZB1lABHkIMsnSvAKLvCiEjFL8xoxIlYnVq1djxYoV+PDDD7Fv\n3z5UVVVhypQpOO+881BaWmrt19nZiT//+c948803kUwmUV9fj0suuQRHHXWU43zJZBJLly7Fa6+9\nhq6uLtTV1eHiiy/GcccdF2W3MwKZhYtnQXRDlkpNKGj+Zh4AerJ0EDpFYcKzgAFGxOfMUqakXNx6\nWoSTsjyZoH3wcYTOhLEucfvjkzqYWRmcgBZuRK4pYUHT/rDIeyCxuHIqi5P7SKhG0ERK04CdH0D9\n/cNMYVIZN0FS8fUXdiYUwjlVTAlBgZ15ySIoqB0LfPIBMGiocZ4DXwJ6ii1wChQJUbKjyKqP6lHA\nyhedybl7JLyMLjj2n3wy1Ka0wOLfSCERZw5YVartx9ISglmsZUrtWL6iQSvSuXWjdU0ASG41BH0V\nMM4nk4tBgfW+1vwDyunfdgqwa/4BIC30OBi96r4il//BAoVVjZX7BECsSJhrhBukTVu1nJ5vMudm\nLrtWkPFhtdOMHxI1L9wJ9bRwXhpEazKPPt0yjNnvIyIWIDe7npQXpdbwatlJEaCohtGI59le+RLQ\n4TKaVFZJr4FUTwCDLMAPgib28wosyiTSHwpei0iViWXLlmH48OH43ve+h+HDh+Ojjz7CI488gi1b\ntuDXv/41EokEdF3HokWLsHv3blx22WWoqKjA448/jptvvhmLFi3C8OHDrfP94Q9/wFtvvYWZM2ei\npqYGL7zwAm655RbccsstqKuri7LrGUFQC0nYWEmR4OvXusI6p4hOUZYBxh42IWPNYz0D/b1NnjbW\nPn4/+CDUbkE8GrLgVumUpA62Q114H7Qrv+tQwIzOKlDvXALt97dwmVy4Y45jXSWVxUu+fgYAIGnW\nG7Ded29Pusq1CW12EzB1uldJPXcmEsc2QGN4QuzP3LcXTbRdUMVUaWwG3n/H+k2+OWK11595JL3t\nBUpCqhtFRUC/K0G9qkac7MjDti3A562e5Fy07wYmnxzpfMUlCiD9Xfky1IX3Ba44DgAHlz9n/LAL\nIbQcjoRqCO/b3/UmNo+fDOUrXw3OShUAJMxJ+fZFzErOROixjysR040wT4emiMh4HsbUA6aAZWF6\nI9Tm2UKyDVaunuz86Xd80NYkmbWMNo5l49658yNFeNUf+gMzZJMYxkT5BiK46Yilwl1Xv+IlRVj/\nusHIxbrOsiX0b67zgLeNgR5GjR4WWYDf8GNRYn8gLyPVi5ZbFL9hEKkyMW/ePAwePNj6+7jjjkNF\nRQXuuusuvPPOOzj++OOxdu1avPvuu/jlL3+J448/HgBQX1+Pa665Bk8++SRmzTImkB07dmDFihX4\n4Q9/iDPOOMM6309+8hMsXboU8+bNi7LrGQMvPjlo8SRRgSDLmkFCFroOWIImSZYmoFlXaNo6jT1I\nZsK3BCqJkCMFCMX0JEwSAyzrpQXKB08NrQpI7Sbr0fDt/uRU6ZShDqZer/oIYLdLcCgtZ0+YG9ek\nrbLmGLKzOVnKTWk5lNO/7WT7Mq2rpLK4ttUUrolAVDWCv6h1GDUVHGOc0IbS4sw/2ia2rlcMBjpd\nnpIjaq3xJkONSWMzIt9c39X/CgBQfzof2s4Pqf2RDsEpHwS4azOUD4Ly9TP4z+29TVzPhDL1LCSm\nTcfBXxuehMQvbpdiT/Eb2y30hgFAxx7mt4riYqCvz7l/WbnnetqnO4zr2RvNHA5H+NyeXexxvnUD\ndBpzkgSURjb1qagehGhcEaGn70eGF0n96fw0McHyp4GuTudJyyuMOXLJH6n9wc4P2WODxtRjv8eS\nUq8yMbRKOB/ry5YAg4b4yn/TuzvF37KgMB/g8hT6UF78gLeWWQoNw3MnYuYjioRsMrAUI1N9A9XL\nYHnKOvbCQ4qw+E4olcP5cxfNo5rsNa4tUdHekd8ToddfNrE/Ci+jcb3covgNg0iVCbsiQXDMMYZw\n09Fh8F+vXbsWw4YNsxQJACgrK8OJJ56ItWvXWsrE2rVroaoqpk6dau2nqiqmTZuGJ554An19fSgu\nzr84M19FVwBhLLjUh0KYl0ymG3ssoQyrAK8+BS1pjRZnq29e57SEUkKOPJCw5omUKXXuAmi3/tSb\nZD7mWCjHnyg1AfVtWgfAFVoVkNpN1qPh2/0ZskqnTA0SAEB3J73dhDtkzc7mZAkEpnWVyvZFEuKf\newwAoM5bKCW8ajdcaSje5D3XNwDrX4fGKpL2+U7hfehvv+lVJkrSlaZpjDQJUwAiYD1XWrJ4KFCK\nvOHj7dA/3k5nH7Ixb2k//b53++ChjgVda/0YgCmEs8gUwPd4kG3WO3MJPerC+5jnFYZNfL4TWLvS\nueG4yXI0v2ZImr5safp8YZjHONCXLaHXDjLrQWgrXqIm4LvnNz/jSlt8B6BSLMF9/da1PXh9OXCQ\nbV1Gxx46XfMx4x3hsw6a8w2roRw3SUyBO/ar3vAhd/4buR9Nk0rclinMp7+z3trfTTRih2gcu2FP\nbA4q+CamTQemTacr3C6PhGwysAwdsTJuAtcAZlckrG0iRYIHhiIRti6WLGSF+8Dea4lQcNlxlWuI\nPQH7nXcMl+uRRxoMAa2trZ7cCACora3Fq6++ip6eHgwcOBCtra0YMWIEBgwY4Nhv9OjR6O/vx65d\nu1BbW+u7P3aPxsKFC1FV5Y/KLSz2ffw+1BmzMHRmOpGrd82rYDDoW0iMOgqpzz5xtJWcfBqGzfqx\n9XdRkfE6yT113Hi1EcPogj0+tbhhMirn3+051o4D5jmqzvwOMOvH6CwrM0JTWKBVMnUL8y4kqmuM\nxGAKihsmU+8DMITyCpsAR7uPA1/7Orpd1y/72tcxaOZV1HspbpiMPkG8bPmMWaioqnJe77vOSlm0\nvtDev/bx+xjiOtbxzF2gvivz+PYt6331x+rX2tfT15N5xwy4J0oa2xd5do57JH01/28zaw7Y70M7\n50K0u97JQF3DQfJe9uxKb5BIyHaEWbnaK5qvQPsPL4IGOIqZqb291vM7+PYa7Ec6eVr/oh0Vn+1A\n6VfTRdS+XLncusfOsjJwxLPA4H07AOjsQ2Y9hOKGydCLijyhY0gmAycLy36v3U8Z77KqqRmdS+7l\njjd92RLqecm3SsXalZ5Edypad0C3JX/LCkLqqFponzmV0pKTT4PedYB5/8UNk6FrKfS3t6VrMqRS\nwLNLjXoQNHzRbr0rEk5i/3akxlXKeMPljca31LVsKdDfh6qqKuzdsg79AIrGHAsA6P/ofRQdfQxS\ne9uY4ypRXYPi+gb0muObYMDIIzC0qgodxcUomTELimZYoMtnzEJy41pUCuYWx3xu5mtUVY1A58ED\n6LKPPS1t2SYU2H7Hh/7oYmiPLrbW0bYL/xkAUPN/H8AXCz5FUkAIUFZWhoqqKvS+8rzRz9O/Rd1v\n/8P3Gtuvvs5omPVjfNH2KZJvvObYj7ees9ZyAJbHjoyPvabiVXXuxZ5d962Tn+fJuzi4/BnsBzDo\ne8Z7OfCXez3znGNtkTjvwdde8sgziVFHoXrhH6nnlBnjpfXH4aBr3h9+zoVQbeudew1kzjvutb75\nCu/6OevH2K+nDEXZ3o9zLsLgWT+Gtvtzz3pV9dNf4eDyZ6TGv2hc5RpkSIECo6OjAw8//DAmTJhg\neSg6OztRXl7u2beiosLaTv4nbbz98g0lx5/gEH4rmq9AWeMMS7Bhwf3hAQb1Wdt5U9G55F7qMZXz\n77YSo2iwL0489G5+C73EomH2mdZf3rUICKOQ+7jqex6nnrN8xixUzr/bonuzo+Tk0xzP0o3OJfei\n7byp6P7rnzzbuv/6J7SdNxUpd0VhAEN+/Avu+7B/8P1tn6FfJoaY9Jny/kuOP8GzX8/GN9CzUexG\nJfdI/mm7P4e2+3NHG2t82NH79lr0vr3WOmcQRQIwno3ss7OPqz1XnufoM4G9rf2nBp1t0ZhjLcGn\neEy98Hq8sVNKUdZSXQeMZ0l49JO9FouStqvVeqbJzetRPmMW9P1fQt//pSEwETe5ieQ7G5A0q1Kz\nvhsC2rchA64iwUNRMfq2bEDKnQgJMBPs7VCGDvOesu4rqJx/t1XM0g617iuOsd+7+hX0mp4r0bMp\nnzELQ378C0+76FsN+kxl4FYkAGNOZgp9MBJU+98184NSKUvIx0H+83bP1fZvR+bZKYOHonzGLBSP\nHYfiseOMtkFGJIFi7qN3dULv6jS2AQZhAwn9skNVkdrT5lEkAKB35XK0nTcVyuAhhiC6cjkOrlxu\nfGvfaLT6Syhh7UiMPNI5Pja9hd5Nb3n2Y6FoTD2l7VjhcWQdRX8f0N+HtvOmegR9N+zzWHLTOiRN\n7zX1/JveQtJ1H4Mv9zIm0doI/Kzl/Ts+QP8OOsGG+5lWNF+BgZQ5cOCZ30nfnznPDW65EoNbrqTO\nc3Z03Hg1d/3oWno/lBKvtX3INew8PpkxXmy+/0Hfu8JSfJKbnc/dvV7LnJcnY6jDq5lt5Nru/rCu\n6b6WaFzlGmLzTPT09GDRokVQVRVXX3211a67EzoZ7az9wmLhwoWOv9vbKQtpnJg4BV3t7RiWMuJ7\nv0gUp0NZ3l4HbHUlP1fVAO18YaG7uxs97e2Wxuy4pzm/AhhMN6k5v7L2tfeH5VK0C3m0UCZt5jVQ\nBElr+vhJgItKrv87M9De3g598tcB1yR0cPLX0dPeDq3P67tJ9vV73p/jGZx1LpTubm5/eijsG+13\nL+JWgO5aej+6u7uRaGpBuUmL2UUJR6K+D/P9O7ZNnIID9/9O+MyJ+9P3PZ51rmd8SL1jBpTGZqP6\nMSVfoOesc419aH2qb0DX0vs9C43MNQEAQyuhnPFtR+jU/sW/p1ciNtG19H6jlooL/ed933gOq14F\nAEfCd0/VKG5YDbkP6rUA9Pz9Wc82mXsMrBSIqHhfec77jAYNgXL6vwQPRwCgF5V42vpLK5j3qu3Y\nbgiaxI3/ntFn3ngkoI0bAGj/wQXGj5ojgbZPnRsnTYHyo5/T5z8CUew/LQG7tJwfAhQGJQO89L+D\nh6J/7Fepz1VmXHUtvd/6NvtMqlrt6p9Df3eLdXwfUXCQHsdt5011hPVZKKvgjh2lsRlaUwva29uh\nmYIbmee6zLkwNbDUc1xqYKljrix6w/g2Dxx/kq+53O5xFH7LItDW3/GTrDkVAHRT4OtlyBLa7vQz\n4I1zMpap8zxgrOU0UgwYSqp7LPDGhmPbEd5K5j379qGPvKujj0Vi2nT0mwQPPWedi9TKlx3vyrG2\njP0qwFGoAUDTDCW66NjxAID+97fii2cfg2qrceG5/7POBdr3UPN7es461wqtLbvIeP+dnZ04sHaV\nY22mrteMsWVfO6n9AaCtMBRq+5jrXLEcB089G6m1q5j94ck6BKJxlSmMGjVKar9YlIlkMomFCxei\nra0NN998s4OhqaKiAl1d3omYtBHPQ0VFBVXQJx4JmtcinyAdoy5QJGRA44JWzp3J7I8U/zollMlK\nIOMUwtMW32n8TdgcUpqVKMulvTMTvUlugd7eZrXZ4Y4zTDS1QNuwBtj5kXPHkoHs+xNVf7aBV0SQ\nVwSI1s+gSWSyiYJ+OPaVxmboDB553nMjCdhB2L6EzDJmOIqvQngANcTOGqsJg8YwZUv41l96kl/z\nIGiVaoBLqxsIiYSRF8Rh19L6+oHnH3Vum/YN8XfOEbRJcra++A6gyLQy9vcBHRS6VBeiZjoCABx/\ngkeZIPkr6twF0K6f7Z1Lq2qgLvgjtAduN3IE7Jh6pvHsfv0f3msVl4iViYGlQM9BcZsbR40Ftm91\ntk07K3RNDFKYLrnFGOcqAPVHPxeSW+ivPO+sE2BsMZ6Nj3oQUu94x3ZHAi6xficgWWCQQscs/JZF\nqB5JXYP9UiMzj3FBGOM/6mjAJBGwUDEE6GQbVETX018xjB9Udj3EVK15726gvgFlZxvGp/3KE8A2\nMfsit+q4RB4jiz0pTP0OpbHZSSZC1n2J2leO3Nkl99JljwhqUWUCkSsT/f39+O1vf4vt27fjF7/4\nhSc/YvTo0Xj77bc9x7W2tqKqqgoDBxpu1draWrzxxhvo7e115E20traiqKgII0eOjLrrGYV9UAsX\nCkmqRVbCjm66o1WT6Ufb8UGa6YbSHyBcoR9uIbxtm5z3U9+QnkRIIpx5bkfSLqHMfPGp9DYKz3v3\n34wiSPjWhfwFiFdtFxBaLK3kbE4RQV4RIFqCdRj2EJnJUFqBpfTBgiikS6YQGuXZpmtQ/AYw43ot\nnDgVyqijQlnRmZh4IhJNLeh/3pbwLah5IFR6eGApEkoC0D2ZC2KkODUoTOWOiucfhfb8o8Z3dOJU\n6jNXr7qOLoSXlgPbtqSTN/ttLEo+Qv4iBc+4wUK7mUh+BCX37qP32c9uPyXZ3Q2a0iBQJCxWsu1b\nHRZb7DcEReY6ITEeuQocp84CFQe+ZD8bmfcvUqhdDHuyQjiNjlnb+WFgZcJO2ewQtJNJMSGAgCUN\nZeXU2iZCQbHuWODTHc66DsMqAysT+rIlhqfJrYSZ7Hp2yLJEyVCyo6jYUBhNwhDCAiXEBoMYgVZ1\n3A+ZjDvBOgjVO5Bm5qORiXD7Q2ODFNA457IiAUScM5FKpfC73/0Omzdvxs9+9jPU13tjGE866SR0\ndHRYidmA8SDXrVuHk046ybGfpmlYtWqV1Ub+njhxYl4yOdmR3PyWI54v0dQCDKNo3cOqDHaD8ZO8\n28xqqwQ9ry9PMznYYQrwxV8Zj+KvjHcK8Iz+AHTNXCH8/JNP9l7HnAxJUS57/Lz+9FJjUdizyznJ\nbtti0TDqq5Y7maKWLYG+armxbc0rxnV7uoEeI8QIZvViOw6++iIOvmow+CSaWizlhwalsdl4Fm7U\nNxjsGJS4XlQMMhbm2U1Gvw7sh35gv/W3NrsJqaceAmAUAepjxPwmN693xJ2mnnrIOB9lIiHXI+e1\ngxxHE5xS1892HOe+ZigMGuJtG11nVSnm4oRTPE3WWGunKHDte5jvUmls5r5jEZTascaPvqTxD+Ar\nEkA4z0R9gxGS40aZN48sbliLE+OZA6DHzFdW8Z95pTeOWGpbtkAE4LLy9Hvo3B9qXHFRWuZtI3O5\nOVcPnHomBk490zNXU62lH7MLUIqgNDYjcRklVp98F0EwkHJ/BPUNUO9cYlTKdqNmlHi+Hjbcu2HY\ncCSaWqjCm3rNjeJzCtZWIvRVNF9hnYs3HyWaWow1m7W2zF2AxAzvfE1r88AcH5Xz7zZyJOobgJ6D\n/LFK64etv8pFl3vYs5RvX+TZlyYjENhlD+a92/qj/tbIY+xdvxq9JnMajRmK1Wf3+2CBrI/W2rxn\nF1KmvOFYr23CvXVeCaWIjLnetSvRa7LJSXltxk3wPHPSxhtXuYxIPRP33XcfVq9ejfPPPx8DBgzA\ntm1pnunhw4dj+PDhOOmkk1BfX48777wTM2fORHl5OZ544gnouo6mprQFoq6uDlOnTsWDDz4ITdMw\nYsQI/O1vf8Pu3btx7bVygy6XIFVNcvwkLy2gqnL4zjc63MLMku71E6Beei2Sv/ihccpf/ze0X/17\nIFetJbTSqpTaJkOPpWPFi2IXP/fCKaq1yl2sR7cx+khZ3xnFzgAAo+uAXa447K9OhHJErZT1wF0E\nyA63hSQwZaCP4/wUcxLCXSANJtVir8DjA1geJaoV6FMjHM1BKWm28Wq0aLS8AFr8uQuBQm5kPBPl\ngwB3cn/NKMMC93ODyYskB6f2tBk1Ic78TjzeFxo1bO3Y9Hgyn6+9JghpI8c5LKF7dvH72bGHHdLD\nqyMRFLxwLJmwMsJ5by+0dWBfPO8CAIoHeBOuCeWyOVf3LPgZAEC9fhG0xXfy5zKZb44BlhfRE5fu\nBz3dwNBK4MsOZ/uYYy3aT+WSaz2htzhGXAgQQ7xJ/xg8lHmM1BpAO6f5PojlmVje7ZZnYTFYTnhx\nushourK8VCVrc3yQ/hCLvlFgcas353L8JKg/+TW0u+b78oTQBGJegbVel+yhzl0A7QfnpgkG7HDX\ndoJcEVoAUnWxHPchuT4S+nHHcb6o1dMheTLgeS3ChFxlE5EqExs2GEk3jz32GB577DHHtn/913/F\nRRddhEQigeuuuw5/+tOfcO+996Kvrw/19fW46aabPJSVV199Nf7yl79gyZIl6O7uxtFHH40bbrgB\nY8eGsJpkCVLCm3siAHzlTPS/bwg4bkZxovGnTAucCkD95R38iVYQWkWqlNImQ+YHL7L4BsHAMm8x\nK8i7xkmxM7uwZIWAfWEshg7B9oP3oLv57G3wHS/smkQxus7rlrfVBAAYeSGc/AYepzVV6K0YJK5G\nWn+8Ed+pmlOI1g9UDEHi7PPF/NumO5da8K+0Asrp33IU39NNijyuK5omUFEUHl9gCOHq3AVCIcXz\nTAGg7TNjLKgqUFmNikuM73L//XcA+zqM9/j0UmqCZVCQ/BVPhd+rbUKO+cztie3kmQPwVrjd069X\nLgAAIABJREFUsV184TCGAxYSCa9wUjHYqE9x6396i6SNqYdy/NdCcd6H8kJRoDQ2GyEk++Eo+EmU\nCzJX939ieBtUW5swx2nZUkCODDd+VNV4lYmvpj0AJPTWkW+jqOJkaWo9lQ+sXC23ECqVG2aG9BDW\nt+Qbr1nvw11IFROnWG1phSC9PtgVAp3CdmWtLZwiozyQsWDvj2XRp9UAIm2MKu9+wKvB0LfDCGtW\nIVbgeBBZ4MmzpxZtZB0jET4sEyJlv6ZM3Yug3gQyrggjnmYv+JrDUPS4aJPyBJ99ltkYX1ZlWCCE\nlTgMePHrjKRO9Uc/R+r+24GqERhkToYHdnwAtO9GYlbaZS7DHiQDpbEZ+sqXqcV6UDUimJXYvA/t\nhiuBYcNRY/Jbt82bDXyxF+qt90D7zTwox01CeaXhWu/q2Av9nY1G4aWI35VlIXnhMW9xpX+9FImz\nz0/fjhmfaWek0NvbvALjgj9yE56Zxb5olZ+jRM2RUOf/t3N8kMJ05v+JuwzWmdQ1P09vM8fcCJOL\nfff9vzOKi4VJsGRAaWyG3voxsH6Vc8MJX4dqCuLatTOoSbbKN77Lf+ZbNxkVyOebyb03/j9oC6+H\nOm8BtGsu9HpTEiq9YqwdVNaZyVB/8iukVr4MffEdSJjhVam2T6Fc+u9yyn3tWKi/vN3B9iascBuD\nEI76BuDTT7zfcXFJOjyNdyzPMIJgeQgsGO94ozeReliV1/Ps97wMo0HWclVyBHbBzT6viCpuA7C+\n2QHmnNdbMRj6OxuhXpdmfiw2Y/v7WtL1gVhroL7671IGAfWPTznO67aOU9kAOf3Rfv4DYPfnGHDm\nt437WP4sMOIIqLf8j5GPsPJFZ67BtG/AHlrkvl6YNZsH1jfnFr55909bAwkcTJkmWOujW0D3c00R\neQFtPLpB20bGlWIad/RTTvfIVpmELJtTrHUmCvDCdx4CLbbUD3gxyvUNSMy9ldofwFvN19FmxveR\nOHxaDgOpeQBI5i+UDPBuKBlgnPvCy7zbLrwMCiVvwgNafD8pGFU/wVFx04j3NM6pnPZNJJpa0LPm\nNfSsec24B7MNkyiVqSdNEd6jMB6ywuiro15DhbP/9twHUc6EvmwJNX7VuqblfbnQ8jKhmEIHKQte\nrCy5NrEAvrPRYrwiiyj5v2/LW+nkdrLA0uJMq0YE72tlNfC1r3vby428GI8iAQDrVxmxtrddn/bK\n2KFpwrAKZbwxvrTWHdBMRUidZ47BIZUAAKV8EJTyQUZbQmKa3k9JwCSCt2kFTe3bi9S+vVY8sMw3\nqf7ydgDOeGn10muNZ0/LKSouiV6RgPldmtWWEyOPTNcpoIVRuKCMmwCUUHI/SgYaz4AWQlA7JnBf\n9WVLvIoEEEqRsM7rViSA/FMkBPH0QaAvW0LNKZNaH75qrmVvv4nk229a87wd1HwBcz7qevYxdD37\nWHoNlLH4m3NHcv1qJM2cAT8MSdT+DBpqrDGJhPGvsRkYZISAKSnNkxOgCAwUovlBCOpzUJihdVYu\n4m3sehMEvPw/2rMh1n6lrCLtEaSwR/LQs+Il9Kx4CYBc+BxtPPJg5U2auaMkv8OeO+r3nJlE7BWw\nCzDAG3zcPIQ5Nwem07Q8DDTrzMAyYNsWphAKIJ0oqpiOTV233G1W4pFZWC3RchWTOcHOEKW98JiX\najCR4LAu9RquQ1MpcsSZv/wMMF5isSDFx+wwhUIyWdpdmMRaQ+6n3wyfUG1tVAFv05vQObSy+rIl\nVKHGIczQWB5c7m8/uQ+sMBdHbK/L3a6/+CTzHoTYtkWaCevg358xGmdcKe029pyvdix0moAlg449\nloDqQJF4WlTGTYD+yYcAAPXIo42+fvoxoKpQviXJyKFRFvPBQ6Gc8s/Qn3nY2l9/ZyOgJvhCOo2d\n7JMPqCF2pG/aquWWgiuyEpLkQke89JFjvDlFVTXA595ibmFB7kNpbEbKZGxTGpuhP/dX4bH6e5vo\n77mvlx2CSGGHciCM94X2fcThzYkTAtpg0ZrE8tAnmlqg/WoOsPND54G1Y6FMPlnqu+KxDlGx4Q3n\nOLDn47lBmY+0j94DkF4fUjIeP62fek33vRBI5VyaVNAkNzLx6/9O5xOYNKWO3A+JnIAwrI76Z63A\nuhXODRWDpL3evPfIC7my53a4n5tuCwXTF98BbfEd0uFIdlrlMFTudvila8/lJOyCMpEhyAwUkoeg\nmIKz3rEH+nubjQn2mUfooQ6cBYgaL08gw3VPeNttLlvyAbpBixWkJmwNGgLsdS1CSgJGRDAHX7QD\nldUYMuf/GH/+9pfAzg+QmLcgVCIxobGToU0V5mFIWEkxrBJo+8wRn+uIh6RwU2t3zRcnyzOKdtkX\nDVqugUVtZ7rNEy1XITW8BvrrL7HHFi+plSdkwLTYmPfVa9boSMy40tdE6puGkAdaDDatjdIXbfU/\noHzju9CWP22166v/webgLy6RyqnRP0hbteMMfST3obd7hR8vrbAk538MioQdbrY3GSjjJkD/0Myn\nUE1rqaYBahGUf7mALxCtXQF87jJGVAwKJ/ifMMWjrIhqsDBDPQnUIrrCFBd4ifTbtnBrDQGCJNOq\nGq8yUVXDzg1zJRLb6cHD0K6LFLw46qVEQbSRIsnZ8Hp8LbbHU8+W9oRwiS+6O72K95hj2c9bQpEg\n3iQpKnOq3OHP2MYS0KXkAFqIoWs8yihFMpT8uaxIAIUwp4xBxi2mv/SksWh82QH9yw7j90tPGoNX\nFDPNgoAWjhpGVR68IKCbPYh8LMSF51EkAPEiWN8AHHMc1IX34eDfn8HBvz9jsDfVGtb0RFMLnY5w\nYJmYos4McYqUNpWHg93Gc0+oVsE0OwVdUHrDhKlk2WGFpJnejpKJ/4SSif/kuKaVYGi6+AFAf/B3\nfGGJp4h27OGG1jnYrvZ9Ad0U3LXbrhd+H8T9bachlApjiBjEHY89nxt97uoEujqN33s+N7bRPFSi\n2H4eeHSbAelWyTOluftTS+510CfqB/ZBP7CPSV9sh9LYbBT7cqNI0lJMA+/+BdCXLUnXwtC0tEeo\nv0/M9ONWJAAxOQEHSmMzFHPeQkmJVWGaGI1YNKUA+AJ8JhUJGVAKReLlp61QDWvMFZdYYZVWmxmG\n4gj1JAVKJRKJu//+HLr//lx6c1MLnbFpyDCocxcwQ09524Qhu4J1R4b+0x4mTO6Del2ztg/5VpFK\nWeyH1j9z7ux65hF0PfMItVvu6xFY70UtssKzrLAhCp29cuI0fniUQCZJyw/pNVkm5MqiUHfRvwpD\nfRmQCvOihRianq40JTuHUnfFy+hZ4fQQsULhcx2FBOwMJmCLEnZI0R2i+ap/fMqwKgcsoGVZUP/9\ne07aQwAoK4d6x1+Qen+Ll6KPxijkRoZd9ZYV1XThKr/+bwCSSWJUmsJ6qDf8X+tP/brLjfP+5j7h\neZmJkOUVhmDJQ80oqPP/AG2OMYmptz/kSbwDGIlZnNAAkmRr93gol/67I6m5eOk9AIC+GVfSk+uj\nQNQVnu0gyp9Jm4nrF4VPEqS9M5mE1qAWTVF4COA9VuDtAUD/ZqeeaSRlB/EwhcWQSmCf65tTlEiZ\nqqSQb+FDg4d5i+IddQzUX/y/wOx7eYfRdVBOOAXDzRyuvU8/IkW0YK13tvWTQJtzcbDk/enneC3v\nAwaK6XjDfFvmPOeHaCP1wO3c+YgYzWjPhqB0xQsAgIMmvavU3EpIBUh9lu4uuUrvPJhU7/oNVwIA\nlFvvsTYFIbBhMtoJErClkvcZsCsq6n3/BQDQLv+JZz/tRwYTmPr7h602spbb2ZzIWp4NyCZgF5SJ\nDCkTcTEjCDGwDGj4mjd28YjRdKtbjAjDRmItFFd+FwCg3mPE9Us912PGAx9sFbe5MabeSzc5vRFq\n8+xgk7oAIgYIHiMFYYCwM5K4GSDIsYkFBnNV1sYkC7wF2L4g/tBgtlL/+7Hw90B5xyIWrNCCG41d\nbORoqL++m6rcKz9bAGzdmFvvigODWpbU/SBRzbocK1VQ0Ghji0ug3v1ocCFcRonLJCqrgS/3yoVT\nxg1FMaiT3SGBqkrPBbJDkFNFjGp2YVpoVMslDCyDeucSdtiTwBBB5rmi+w0htP//b+/M46Oqzv//\nubNk9ixksidsYQurbAKCFMVd0KpUEZBFbX+1tC61SrEiFgXU1gXFflUUcWvpt1Vqrdaq9FtQ0DYR\nKbIGAkJCEpIQyDb7zP39MTkP987cLExIMsDzfr18Ge4z995zzj3b85znPOf2U5NQMdFUuqyKiabm\n5LcddViMO9LTDwMA5PsfD78r3sYHQFO5ozGwHcY2rXJTctoRKB1J0ecb5faGfunz9M/Qz8Nni+ie\nebt9z2xWprElvNkbEy/jaE7MKTocGSFWPK5oRQLoNEWirYhFmqd8Wtt2qyLXElkGZFm9hNmGW43y\n4C1xngTqT7a9VD1qYrQgOdzZiGVe44ixMI4YS9eUkaE0n9mKrC2fSOU5C3SyuFiCHhgdkSQyupZ8\nvAry8Sr6d2unbbZaV1tzOdGKcNReWrPkNR90FPzhdeHzIwKBdrnctEmksghFFKxW0hIr0vSZQLLG\nKb7CfaA5Nr0uIxu6ZhdE+Q+vdN6gHmNkHWn6zBajz8kfrFcMsDLo7INOUiSk6TO1XaiaA0forpul\nGdwCffq3/i3jSZEAwqfLx4npT//K+0D/wdGCkeNbr1Pt2FOF4+EQx5GnCrcYRU9EPTvTtFX/taLe\nGRPCJ3wDVP/02XnQZ+ed+k1rda54F7kkabreNrus6vP6Qp/XV+WySoetKhnUfjfQwKFiCjYCtGPO\nEmPkrVhRuggaCobDUDAcgMLlqjX3oBhOuG5XBMorbowWOJIiXETrITfUt3+8Kvsu7PZeWxP+7yyJ\n5sQrE118zkRLkSpQXdnyZKq1zXWxxFA/E7SikbdqQRcx75sj4ISOHoY0/57YN/w2W3KCzzwC7Nmu\nljXH2Q/+/DYgK1d9lkRFGfTPvNV6VJEnFgEle9SxufMLoP/lk7QSoN+/GwAQ7D+YrAexbsRTKhRa\nsbLFO093+b/Laa2+dvVG0c6ilTrZofMArLZwO2p2EwiaLeHvG+mmeCZozwpLCxsM9QsfRvB/VgLb\nIsLntufAw65mwBBg2IXAu6+rrxeM0D4otLtoj8vqr34MVJU3B60AIIcAQwIQ6MB+nDiD8nr3rQAA\n/fN/IJnW2KK5gnym0Kr/4oyix38OHI44wLFXP+gfbnZraR53zM1jnefoYaB4NzqkETa/W3pyEQBA\nXvRk2GW2LffaGJGmzwxHQIp09Zo6DfqZLUTh62gfr9G30gpD8xioKwwbSENjJ9G429LcQunqqxxX\ntdyLVefpdMDNqTPork3YvDIRr2idYuj3tW6Vba1hthFtotMsmrkacdjHXRJ+b2sW9GYLgVx/AnL9\nifDfv3+p7Q2/WhbvAUMgDRwWnrxHKhIAsGd7WJacGn2WRHKPFi0FtApSWx1tzRA0rwQIa47uulmQ\nXU1nJKIHoN5kHPlOsaFLd90sSJdd37r1qLXNuTFu3G0XLVntXnqvxfNEutrS1SFaaa+xngcgTZ8J\njLworByXHkKw9FC4rnZg47I0veXzW1raYNpmmkUozUhFAog/RQII9y2RigTQtiLRWe3DqPE9ba1H\niBKb5aX8QeFvJocAORS21o6d1PYKUwc2sJ9J2lz1hKL/dTcB7iaVlTf0xzXRNyRqbK6OpM+A9l2L\nZOioqEsU2KJW49wQ5bXhF0L/wEpaLW5t1bo9SNNnQv/TXwEAAodLEDjcfEr6c7/v1L5cc5N18zXN\njcGOxI69UI5246P3NI+BYrO4agVenKdTW41Q89gdFWhEsRlaaw+C5rjb1cSwWTwe4JWJLlqZ6JCW\n25oFsbVNjZ204ZHC2EaEiCUrQBeeVAygXRY94PRO+FT67qo2ZXXWxmUA0BvCk20A0hPhTcbyL5+K\n+pnmaaxaeWmP5VlrhSnBFH0K82kgTZ8J+eP3ojc3avm1n610ZJO12x29OpmYTO54ZxwtS2FqBqSL\nLond2BBv+wlaotngEHe+36fLubKiB4TbQENdtKttVi6kMZPi61tdcGFcnTiutck85r0NkgRp2i3x\nt6m/eZxQeQSMGNehcZfcrF9cDgAILfyV9u+ag3vI7Q3u0cLKrZTXt1PKtTuUCl6ZiDM6FMKytYrX\nmrIgy22egN0S0vSZQGKPaIFOH17x0DhrQl63KmztP7gvbEFvPuG4PRZ0afpM8jFVC6Q279M/sDIc\nVSKSFGebod+Q2ztakNs73PFoWC6knrGfjNsqCkUCAAKH9iOgFV4R2iF3NTuuNjosafpMSFfeoJmW\nmBkwJJwWrSgpHVUkLLboa1orfZ1NW4NBbbX2agCk8H2RigTQeYoEoD0Jbe9m1sTk6GuOpLhSJKTp\nM1tsx2eTItFqP3fNjC5MSTtJ1hgfpk5ve4WxeJf2nr2Ksvj7VrGcON5C6NczAa3aNNNuP3ytsTU5\nteX9RFOnt2/l8kyjN0AaNyXaI+Dg3pgfqZyA+/f+F/69La9IBg4fQKDZda0joWHlD9afOvRXSRsr\nkK0R76sTvDIRR6FhO6Uj1RuA9Mzozrs9YfESzNEn62qFLoxAVHplKNJ2afla1jezBfoX/thqeFsA\nCP3jPch/XqdOx4z50F15aoOUZrjVGO6LyygXraFl9c7rC/0jz5GfqYputLzFPZIE/Svvt92W/QHg\n4z+rBR0NmXimac/qQsEF0S6EzoxwyNl4QtJFu0cMGx0O4RnL4K03QLpmRte18w6uBp5VdGTFXGuv\nUkdXn+yJ0YeptWcVVWu8UkRjijWMacz7rVpFQtReDZ0e+pc3IPjxhmg3wJsWQPK5u2ecMxqh/927\n6shKWza2vq+yM9Fqm+2JTKcVtW/kBKCpvvWw4q6maC+OiAhRXQmvTMQZwUV3tH1Aktbh8I7k2P0h\nJSnc2WlZgdpzgFakIgG0W5EAAO/2Qni3hw9Ca5eWr2VB9bjDlhidRlUtGHnqb3sSgIiDjpqvCdxb\n/gn3lgh/yObfJFx4MZ3REHlfJK1Gh4lHtKzepQfDVi2NFSZWJFqhOZIYACBPY5Uqr0+4fkS6jgHA\n6Emdu0/ldGnHAYPSuO9FX5/WxqFc3YLG5LR4d+yPCwa6diLV2YqE1gpeN6zqSdNnUlhvTdqqV1p7\nlYp3dexbaQU3yO3T5n4z6f7Hoi5L35/T6mpxW/sYY91v1aaXAeTmFWcp/J/eAISC4b5Maz/Ru693\nn8HMEV4NVR6ip5s4NaykaY2xJnPs72pPP6bVNtsTmc7vj75mtYfzoTWfESvXWu7gZd/FfTQnVia6\nCKldB45oDIgNJ2N3KdAK3dde2uqctDZeXTRVtQwn1xyDrLBgthjab8S4tk8MnXF7dDqGjT71j3aE\nfmv60zo0/Wmd+iHN9+lsDuhsDs37hLuWimQNt6rO3uSoVUZxN6lD55WDpNFdpTi7fileuHKVHoqW\nlR4KD9BbPo2Wbfk0rtyDALQZprMld8ZW93B1B1qWbq87nM442XzcraRlRV+LPMizC4h004miOyzP\nWhPDIyWtt9XaashPPRR1Wd7yz/aFFI0VrTC47di8D6DZWNccqjme99840wG0cHL0oOHRv7/02tjH\nwc6sb1rG2C2fhuu/1liW3KPD4eO7E1YmzlUkqX37NKwafuhpmWHtWSuO/AUXhiu0V0Nb3/EfVeQN\ngeraAQ1r4bGyNmNvt7ZHI7jojtajPIh0lB1CsCxiAtiO8xlcH/0Jro/+BODUHoWYosN0lGYrjQot\n63gkWn7vOn3H09MSnXWqskaUD5yo6TwLmtbekZTU7vEljkccGit48eY1O2BIOO6/1lk2Vvv58x21\not0FNCynzGmgUdfFZBHQ3seTmNyx/irSbaala2eK7lhJPRIeozXP2SjaCiDizKiireH5SkuKVlvn\nnnQHWsrcyeNtrlrxygQDecvGLn6h3L4D3dI1LFY+X7hD1FpuFWEhvw3LpBTnqdBxktS2hUCr46s8\n2vo9beFqos3SytBuURuog0E6nZWWopsVktDxaoSOV6uvNTdc37ffwPdtRKfWHYhY4jr9KWVAcQhd\ni2i5OXXWScTnElod/onj4Tqy71vtyYLWBPtcxd1JSuOZpHgXgj9rITxuMHh27X1izhrkT/8KafyU\naEHzGQhnBQOGQP/ka9pzhPSszlPEm41RmisTHhcwYAh6PP479Hj8d+H5hjBe9RkY/SxXU9vBMloL\nONKRYCRnmHhfmeAN2GdDaNiO0CMtHOmoZI/6evPha8Gf3xbt3+1IgjTl6tY3if17E6Rx34P88bvh\na1fdBHnj3wBX5xye0ypmK506qgypGlx0R8xuJcqGqwzDB5yFG7BbortC/zHxQSeFjmaYFjlbQgoD\nrR+W2lqo3ubxSDO4RTxiMEavUjkzIE3oQOjozqI5cIgqXPvCH3Rsz9GAIUBleZv7QU/7mc4MIPLM\niosuDQev6MAxAR09r+R04Q3YcUaHQsN2BGc6cFLjcJ2D+8KTZK2Nog11bW8Sy84LT7b9fsDvD//d\n0cNqzgDKw3yE72WraK3c9Egj314tdy35g/XaYRE7shEM6Lz9D1rWckdS5ykSrfioS9Nnavtvd+Bg\ntq5Gmj7z7PHDb6VO6V95v/Vl/rMlj8zZw9miSACtumvqX3pPu783mYGefVoObhGPaK1Sm7RCW3cv\n0vSZ0D/yXNR1/Yt/ar0fa211QUzODWd4BaJ4F7CjKPr6of1nlSJxOvDKxLm+MhErrVllmDMOHUb0\no+ujrcXxerhQC0jTZ0Iu2gJUlKoFWiEYGYZpH+fSwXVMHKERNtaRBP0zb3XPKrzGimmki49WmPfg\nwps1Nj1r5O0shg+tY84+OqJIdFdElzNJF0dJoignWrq92P/SErEqEp3kDyp/sD58jkkkmuHwGIZp\nF6xIMJ2CrB7vFH/rrpsVPmsmks4MK6wxTkha0SOboaAoWtGTWJHoMnh07yK6xc2puzYPnQuLXcW7\nWnYPOlfozMnJkZLoa515yjPTpUjTZwJ2jegpDMPEjDR95pl3uWkPSoNU8S6goe6Um69WJLDOPLSy\nOUgKJIkMk/K+nZ33vrME+YP1YQ+XOIWViS6iy6M5AeePJauzlCa7xh4QrWvM+UU8nu3RxcgfrAca\nOzEkJXPuci6sXLdFjHmUP1gPBOJo3O7Ovk6WyTAZGWFReaDdecURjbON4gRWJroI/ZOvdU9M487Y\nRBlvk6nOUpqaTx21TLsZlmk3h69pnSbOnF/E0f4UhjmrOF8iiHXkwNh4oqYqHA0yErOl69OiQBUC\nvo1DAs8ptM4FixNYmegiOhKmtEO0UvliboDny2TKbIE0fSaMffrD2Kd/uLziuDEzzGkRRzHUmfOE\n80GRAMKT8HOB2mrghEY0SI+769OioOmj99D00XsAzqFQ7e3B1dTdKWgRVia6irozGMP4dGhFgTlv\nGmCsOBKhu24WPF98Bs8Xn4U3Pw04RyxODHO+uEEyTFdzNoXAPYuQ930b/n91BeTqsJuT/NGfuzNJ\nXUvPPt2dghZhZYI5++mRpn3GhSOxY8uf/vBBPr6d38C3s/kEbK1TwRmGYRiG6VyKd0Wd/cRGkfiA\n17m7Cq7wnYczHfD6gIaIMwyamjq2+lJbHd1xMQzDMAzDdDVx7D7HygRz9tPSHg6tkz3bS480Xqpm\nGIZhGCY+6C53+XbAbk4MowUrEgzDMAzDxAsdMZB2MqxMMOcGGRpHvmdknz8h4xiGYRiGOXcxdW9I\n3tZgZaKr6I4zJs4nms+EiLzGEasYhmEYhjnr8bi6OwUtwspEV8FuMwzDMAzDMMw5BisTXQUfEMUw\nDMMwDMOcY7Ay0VVwaFiGYRiGYRjmHIOVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZh\nYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZh\nGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZhGIZhGIZhYoKVCYZh\nGIZhGIZhYoKVCYZhGIZhGIZhYsLQ3Qloi5qaGrzxxhvYsWMHAGDYsGGYP38+nE5nN6eMYRiGYRiG\nYc5v4nplwuv1YtmyZSgvL8fChQvx05/+FBUVFfj1r38Nj8fT3cljGIZhGIZhmPOauF6Z2LhxI44d\nO4ZVq1YhMzMTANCrVy/cfffd+OyzzzBt2rRuTiHDMAzDMAzDnL/E9cpEUVERBgwYQIoEAKSnp2Pg\nwIEoLCzsxpSdPvo1f+3uJDAMwzAMwzBnIfE8j4xrZaK0tBR5eXlR1/Py8lBWVhbTMxctWkT/AYDT\n6eyy/zI2bG0xXZ0l6453drUs3tITqyze0hOrLN7S0xmyeEtPrLJ4S09nyOItPbHK4i09scriLT2d\nIYu39MQqi7f0dIYs3tLTmqwr56viv/YS18pEY2MjbDZb1HW73Y6mpqZuSBHDMAzDMAzDMIK43jMB\nAJIkRV2TZTnm5z355JOqf9fU1MT8rDNJa+noDFl3vJPzyPmIp/RwHs/9fJwPeYy39Jzv+Tgf8hhv\n6Tnf89iZZGdnt+t3cb0yYbfb0djYGHW9qalJc8XibKK15bXOkHXHOzmPnI94Sg/n8dzPx/mQx3hL\nz/mej/Mhj/GWnvM9j/FIXCsTubm5KC0tjbpeVlaG3NzcbkjRmUG5iSZyQ01nyLrjnZxHzkc8pYfz\neO7n43zIY7yl53zPx/mQx3hLz/mex3hFkjviM9TJfPjhh3jrrbewatUqZGRkAACqqqpwzz33YNas\nWZg+fXqH31FeXt7hZ8SC2NiitXTVGbLueCfnkfMRT+nhPJ77+Tgf8hhv6Tnf83E+5DHe0nO+57Er\nOSfcnKZOnYq0tDQ89dRTKCwsRFFREX7zm98gNTUVl19+eXcnj2EYhmEYhmHOa+J6A7aOOGxMAAAg\nAElEQVTZbMbSpUuxbt06rF69GrIsY+jQoZg/fz7MZnN3J49hGIZhGIZhzmviWpkAwks9v/jFL7o7\nGQzDMAzDMAzDRBDXbk4MwzAMwzAMw8QvrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMw\nDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMw\nDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTrEwwDMMwDMMwDBMTkizL\ncncngmEYhmEYhmGYsw9emegmFi1ahEWLFnWZrDveyXnkfMRTejiP534+zoc8xlt6zvd8nA95jLf0\nnO95jEdYmWAYhmEYhmEYJiZYmWAYhmEYhmEYJiZ4zwTDMAzDMAzDMDHBKxMMwzAMwzAMw8QEKxMM\nwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QE\nKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAMw8QEKxMMwzAMwzAM\nw8SEobsTcDZSU1ODN954Azt27AAADBs2DPPnz4fT6VTJ/H4/LBYLfD4fvF4v7r33Xnz55Zeasosu\nughFRUXw+XwAALPZDFmWNWVKWpMNHToUxcXFmrIBAwaguroaJ0+ehCzLkCQJkiQhFAoBAFJSUnDi\nxAnN/JtMpqhnyrIMAEhKSkJdXV2LZWc0GuH3+6Ou22w2eL1eBAIBAIAkSfRMp9OJuro6+P3+qHSm\npqbC7/ejvr4+6pn9+/dHZWUlGhoaNNNisVjgdrs1Zb169UJ5eTn8fj90Oh0sFguampoAALm5uTh2\n7JhmPgBAp9MhFApBkiQYjUYqK0mSAJwqq5ZQ5h0AEhIS6N+BQCDqfoPBAEmSNGWpqamora1VXRfP\nT01NxYkTJ6gslSQmJqKxsVFTZrVa4fF4KI8WiwV+v5/KQ+RfC71eD0mSEAwGVWnS6XSQZbnVstHr\n9QiFQlG/sdvtaGpq0iwXvV4Pn8+nKXM4HGhoaKA6J7DZbEhOTsaxY8c0ZU6nE9XV1XC5XCqZ0WiE\n0+lEfX091RUl4jsq67hOp0MgEIBOp0NWVhaOHj2qmfcePXqgvr5es320Vt4tIUkSEhISIEkSPB5P\nlMxut8Pv98Pj8UQ9X6/XAwCCwaAqHaIuGAwGNDQ0RJW52WyGyWRCY2MjgsFg1DtNJhP8fj+CwSB0\nOh2MRiMCgQD9Vq/XR90nEP1KZFrb2+a08mgwGDTrjtFohF6vh9frjZJJkgSDwdBiOzUYDJr3ifxq\nvU/0IyJ9ogyUvxPvbAmj0UhtTvm9Ip/THkR6AoFAVL3T6/Uwm83weDxR38piscBqtaKhoSFq/DAY\nDLDb7XC73fB6vVHvNBgM1PZFugEgFApBr9fD4XDg5MmTmul1OBxwuVyUnsg6G0v+DYbw1ElrDLBY\nLAgEAvD7/VF1VtnPRaajpfYIhL+f0WiE2+1usX74/X56rsFgQDAYpO/TWj6FLLI+tLdsWup/RB2J\nrKeSJGmWW2syvV4PnU6nKZMkCXq9XrP+C5moJ5HpiZxPaCHazun2sS2h0+kgSRKsVit0Oh2NpWJs\nkCQJDoeDvncwGES/fv3gdDqxfft2NDQ0wGQyIRgMwufzYenSpRgyZAgA4G9/+xt27tyJgwcP4uTJ\nk5gxYwZuvvnmM5LuNvPVJW85h/B6vVi2bBnKy8uxcOFC/PSnP0VFRQV+/etfo66ujmRz5syhiYzg\nzTffbFG2detWWCwWXHvttaoKppSlpqZGpWfr1q0wGAwwm83UGYjBfufOnTAYDMjKyoqSFRcXo7Gx\nESkpKZoTuUAgAKPRiISEBADhhq7ThauLJElITU2F0WiELMs0EAJAXV0d7HY7LrroIvq96HiB8EQ0\nKyuL/i3S09TUpOp09Xo9PbOmpoaeJdIoZMePH1dN6mw2G+W1vr6eOp+MjAzqkAVClpOTQzKRniNH\njlDeJUlSKR0VFRUkE2kRz9XpdDCbzZRWn89H+Rcdm/id8j5lumRZRmJiIt0nOsHICaRAp9OpFA7l\nt6qvr6eBOhKXywWHw0HpVT63vr4eDocDGRkZUTKXywWTyYTLL7+c/q3s5FNTU5GcnBx136hRo+Bw\nOGCz2aLyMXDgQCQkJCApKQkpKSlReRRlp8yjKJ+mpib6W3wXUWZerxeSJMFms9GzTCYTzGYzTpw4\ngWAwSAOVwGAw4OjRozQAKWVGoxGHDx+G2+2mQVw5+a6oqIDL5UJCQgKlRQwaPp+PFGKLxYJQKKSa\nkAtFIiEhASaTie7NzMxEbW0tQqEQfQ9ZlmGxWJCZmUn9hNPpjLpPmW6B1WqFJEnwer00cZEkCUOH\nDkVSUhJkWUZDQwPJQqEQkpKS4HA4AIQns6Kt6nQ6qltGoxEejwf19fX0nRITE2G322EwGODxeFBX\nV0fptdlscDgcsNvtkGVZNQHt0aMHrFarqk8S9+n1eiQmJsLhcECn05FCJrDZbEhMTKRyEs/o0aMH\nsrKySBY5KU1JSYHD4aCJmHLSL+qAmNB4PB6aECjLOCEhgdqCaOtKuZgoK+uVqLuirppMJpUsISEB\nPp+P6oper496digUokmKqJc6nQ6DBw8m5V1MWKxWK4xGI7VDnU6HQYMGafZFQHhyrCxbu90On8+H\nUChE9Q0I1yu9Xk99uVCWBQkJCTh58iR8Ph/1WXq9Hna7HYFAACdPnoTX60VCQgJ9W9Gfi+8ryzLV\nQ2HMkCSJFImUlBQaC202G3r06IGGhgYEg0H079+fnmG1WpGSkkLft3///qr7RB8k0i3qisVioTog\nvrPBYKC+DQDcbjfJgsEgsrOzqY0oJ6RmsxlOp5PeEQqFVIpEdnY2HA4H9Ho9/H6/SpFIT09HYmIi\ntRGlEpqfnw+Hw6FqO+Jvi8WCpKQk2Gw2mrwr67heryeDgHLczs3NxcSJE5GUlIRIdDod8vLyaNwR\n31woN0LZE99RqdQp62ykTElSUhKVndlspvuEcUrUj6SkJJXMZDKR0ivG98jxTjxXp9NRvvV6PUaO\nHEllL+qa6MtSUlIojb169aJ0iHYpZKItSZIEs9mMjIwM6vf9fj+NnSNGjKA89OvXD3a7HXV1dfB4\nPLj11ltRWlqKTZs2YcqUKUhISGjRKLtx40bU19dj7NixUbLOhpWJ02Tjxo04duwYHnjgAVx44YUY\nO3YsFi1ahOrqarzyyisku+yyy/DWW2/h6aefJqXhxIkTLcoA4LHHHsPcuXOxfv16DBgwQFVZHnvs\nMaxevRr/+7//iwEDBqjS9Mgjj2Dt2rX44x//iAEDBqgm5VlZWXjuueeiZDabDX6/H0uXLsX69euR\nnp6uemZjYyN69eqF3r17Y+jQoUhNTaVG5/V6MWDAADgcDhQUFCAjI0M1mK9YsQL33HMP5UMpW7Jk\nCSorK/H9739flR5lpzZmzBg899xzqvvExMzpdOL5559XNTzx94wZM/D666/ThCs5OZkG70mTJuGd\nd97Bs88+Sw1d3Dd+/HiSiTzKsoxf/vKXAIDRo0erlJRQKITHH38cQLhje/bZZ1WD3fLlywGEB5ll\ny5apLIm/+c1vAAA33nij6j7lIJOXl4dXX30V8+bNAwBkZmbSgHHdddfBZrNhxIgRlB5RhyZMmACb\nzYaRI0dSWfr9fqxcuRKvvfYa1q1bR+kAAI/Hg8WLF2Pt2rVYt24dTXAFoqy1ZMFgEHfeeSdef/11\n1aADAA899BCysrLQr18/1X1paWmoq6vD4MGDabLYp08fyr/P58Njjz2GZ555JiqPsizD7/fjmmuu\noTwqy9Xv92PBggV4++230bt3bwSDQRr4QqEQXC4XZsyYgd69e8Pr9aKxsZHunT17Nt555x2kpaUB\nAK2shUKhKNnJkycxdOhQyLKM73//+3jzzTeRmZlJdU7U45tuugkvvPACsrKykJWVhVAoRHU4FArh\npptugtVqpQmdcuDPycnBunXrkJaWBpfLhcrKSrpPp9Ohb9++SEtLg9vtJhkAXHvttZr3iXpw5513\nkmz27NkYPHgwya+77jo88sgjWL58OU30BH379sWaNWuwcuVK6HQ6lVFj/PjxWLt2LZKSkuDz+TBq\n1CjVRGDOnDl44oknVBNPkdd58+Zh5cqVcLlcGDNmjKoO3XzzzZg4cSJCoRApReK+tLQ0rFixAk1N\nTZg9ezauuOIKklmtVrz44otYsWIFdDodBg4cSM+8/vrrsWrVKpJFlvnLL7+MlStXIhQK0cQTACZO\nnIgXXniB0qCsV263G1dffTWAcH8yfPhwVb168cUX6TligirLMubMmUPPDAaDGDFiBD1z5syZKtkt\nt9xCSt60adOwatUqBAIB9OjRA8CpFTuz2Uzvv/jiiwGAlOhQKITk5GRIkoTJkycjGAyioKCArLJT\npkzBc889R3UFODXZc7vduPXWWwGEFXflSq/X68WiRYtgsVjgcrlU49kVV1yBtWvX0uStrq4OU6ZM\noTJ95ZVXYDab0bNnT9W3HzduHF544QVYLBb06tWLJqRCuR8zZgwkSUJWVpbKmivK+KWXXoLZbEZT\nUxNqa2tpkldeXo6RI0dSWk+cOEGy/Px81X1iVV6SJPh8Pvz85z+HxWKBx+PB7NmzUVBQQOm96667\n8Mtf/pLqlVLRmzx5Mp577jlqO8pJ/tChQ+k7+3w+XHDBBSoF7Ic//CFWrlxJ+ReKn3jnihUr4PF4\noiaOs2fPxqBBg2j1GTilDOfn52P58uVwu90YOXKkSpaRkYHf/va38Hq9mDVrlsqifccdd+Cee+7B\n448/rlIQAeCqq67C008/jcceewxAePwSDBo0CMFgENOnT1cZUQBgyJAheOSRR6g8TCYTfeMrrrgC\njz76KD0nMTGRZFOmTKH7QqEQhg4dSt9qwoQJKtmMGTNI4R89ejQefvhheDwe9O7dGwBUisWkSZMQ\nCARw3XXXQZZlWg0DwnMJWZZx3XXXIRQKoV+/fgDCba+goABLliyBLMtRq1B+vx+33XYbeZlceeWV\nuOaaawCEx9/ly5dDkiTs3bsXP/jBDyBJEiorK1FeXo77778fOp0OxcXFqK+vR0JCAjweD9588028\n/PLLKoVX8PTTT2PFihW4/fbbo2SdDSsTp0lRUREGDBigsvqlp6dj4MCB2LVrF8lE55aenk6NKzc3\nN0omBoTMzEyVbOrUqfR8IROdVKSsb9++NKFTysxmMxobGzWfCYBcKyorK6NcYfr3708WmYsvvhjH\njh0jWXZ2NoqKijB16lRMmTIF5eXlNMC2lg+n04m0tDSyDCllYrImnqEst+TkZBrAevbsqZJpLQmL\nsjh8+DBNCrS+h6AlWXZ2NgDgv//9L+bNm0eNV+RRoPWNxd+DBg3SlEXeJyxYyvSLFZzy8nK6Z9y4\nccjKyoLf71eltampCVOnTkVWVhbcbrdKVl9fT1ZUpUU0NzeX6o7VakVOTg5N+CwWC9WdSJndbieL\nfH19PYLBoMqaKb6/Xq9X3VdcXIx+/frh66+/xmWXXYbs7GzqfA8fPkxtx2azUR6FFTk9PR2yLCM5\nOVkzj0BYmQLCFmiz2axaTZJlGRdffDF69OhB7Ugo0MePH4der6cJGAAaLDweT5RMKIDC0jVx4kQc\nOnRIlZZgMIiUlBSVLBAIUFkcPXoUHo8Hbrebrol3itWS733ve1HpqaiowDXXXKMpc7vdUfcpsdls\nJCssLFS5I4q6J/oypeIoltCFTChiAFRW+KSkJOzevZvSIxD3idVOLdnu3btVVs+6ujp8+umn6NWr\nF44fPx6VF3FfUVERjhw5QtcLCgpgtVpJXlpaSjJhYU9PT6dJlmhjyj4g8r7U1FSkpaUhKSkJiYmJ\nUfXqqquuItmuXbtIsTx+/DjS0tKoDldXV6u+lfKZ+/bto3oQKSssLKR2YjQaqTwGDBigstgCYZee\nxMREyLJMY1KvXr0AhFf/EhMT4fV6SSbSU1tbS+8UimTfvn3pe6Wnp5NMTOSB8BgyatQojB07lizM\nop8MBAKwWq2k4Oh0OlRWVkKn05FVffTo0SgpKVEZJLRkoVAIdrsdCQkJOH78OE3yRVsW/bxOp4PV\nalWlp3///rRi8v3vfz9KJlZIlPcB6tXxlJQUjB07Fnq9HoWFhaitraX0ijE8IyNDpbwCoLwLmdKF\nS6l0pKamYteuXejfv7+qLER9FCuRWrJdu3apjF1HjhxBUVER8vPzcfLkSXLZ07pPlA0AXHjhhcjO\nzsbAgQNRWFiI7du3R60QZGRkRK3siXqbn5+PgQMHqowYYtIeCAQwcOBAlYE0NzeX6p/T6aR6bDQa\nYTAYVP1IeXm56lsp7ztw4ACNa5Gyr7/+mtpOYmIiqqqqAAAXXHABrFYr3G43jYei3hoMBiqfnJwc\nAKDvKvIhyi4yPaK+9OvXj8q8b9++lJ7CwkLq52w2G3r27EkGqBtuuAFGoxENDQ1ISUnByJEjYTQa\nUVNTA71ej7Fjx+Lrr7+muiP6ZaXRNVLR60pYmThNSktLoyYxQFgb15rgAKcmysoJs0BUODGYK58n\niFUWDAapMciyrPptU1MTNfRXX32VtPvI53z33XdYu3atSmaz2eDz+ZCcnIzNmzcDAC0zi2VbrfT0\n7NkTFosFkydPxt///neVFaupqYlcY0SDEOUlOi8A1BCF7MSJEy02II/H0+r3UFolgsEgPB4PdYyp\nqalk0XA4HJg8eTItY4vBAwhb5u644w6a0ASDQeqwbDYbXnrpJZSVlVFav/vuO820SJJEHdaRI0dQ\nWFhIA4/X66V64vP5UFpaipycnKj61KNHD02ZeL/wVRcdnvI3jY2NKC0tpbIOBAJUdxoaGlBaWkpL\n9S6XC9deey0A4KWXXoIkSSq3JsGhQ4dw4MABykd5eTkSExPh8/lgNptx8OBBHD58GED4W4m6I9KS\nk5NDymJubi4mT56MDz/8EIcPH0ZmZialR5SNx+PBZ599hv/+97/o16+fqn7pdDrs3LkT//3vf8la\nK9IsyiM3N5d+L54trillYqKplAnLu1DIc3Nzqc2FQiGaEImJ5vbt2zF27FiVJUu8s7KyEnPmzMF7\n770XJQPCq3T//ve/6d9icqmVVtFecnJy8O6771JeDx8+rFJSlZOGvLw81QClrO95eXmqclVax2w2\nG9xut8qlTJCTk4NgMKhymRGWvMzMTLhcLpXFe9OmTRg3blzUBAQIuz3OmzcPe/fuxb59+3DgwAGS\nWa1WPP3005g9ezb27dsXta9FIPa0iDpQVVWFnTt3wuPxICUlBR6Ph+pVbm4uysrKUFdXh4yMDFX+\nhTudkLndbipzo9GIsrIy2s/l9/upbCKfqXSNyc3NRWlpKerq6uB0OnHo0CFS4LKysmi1o1+/fiqX\nQYPBQEripk2bUF5eDpfLpXIPEbL9+/eTu2JkekQ6vF4vrYStX78edXV1NPGprq5WufmJ+q7cEyQm\nUUrXvP3790OSJDIk5ebmwuv1Ur3Q6XSYOnUqQqEQcnJyqO+TZZn2ER44cABjxozB8ePHKe/iO5eX\nl2PWrFn44osvKD3KMvD7/di5cyfJjh49ClmWVelRugTpdDo4nU688847cDgcCAQCOHTokMq4BoRX\nfgKBAJxOp6pNi7Yj8qOsO6NGjaK/HQ4H3G53VH0NhUJIT0+nVUmBWI1NT0+Hy+VCU1MTKQUffPAB\npk+fjj59+kTtTSsuLsbs2bOxd+9euFwu1NTUqFyHly1bhr1792LPnj3Ys2dPlCugy+Wi+ixkmzZt\nQklJCbnNer1eSosYf3NycpCTk6PqV6644grs3r0bQNgAJeqd+B5CBoTHPVE2SpnoO8Q+q6lTp2Ln\nzp0AwnORkpISqsfjx4+n1flevXrRPiSRj3/84x8AgHfffReVlZWqvArjw5/+9CccOHAALpeL2qQy\nPSIPQklxOp00f0pPT8d3332Hd999l9zpVq9ejUAggLlz55LbGRDuV8V9wjjSu3dv1NTU0Jgoxm+l\nYtudsDJxmjQ2NmoOlsLvV0smJq3KgTSSSJly8hCrzO/34/rrrwcAbNu2DUuWLCGZJEkYNmwYNm/e\njJKSEvzgBz9QPcdms6GgoADz5s3Dj370I5VMTF7eeustmpyIdBQXF6ssd9XV1VFp/clPfoKxY8fi\ntddeU6Vnzpw5AE41DlFuyucJq5lSFmntjMxHJELxER1dXV0dbr31VsydOxf79+8HELZE7N27F8Cp\nTl+8W+knPGLECNx3333kMlJeXo433ngDALBv3z5UVVVh0KBBAMKd8bJly1RpUQ4QQlkZNGgQfvvb\n35JLFAByA3n77bchyzKuvfZalTIEAH/+8581ZaLTE52TKBNl3Vm7di1kWSaXA2Xdefrpp+H1eum7\nZ2VlYcaMGdi8eTP27dsHg8EQ5apQUFCAnj17wmAwID8/H0B4clJRUQEAeOeddwAA06ZNo3u2bdsG\nt9tNaRk8eDApIhaLBT/5yU9os/dnn31GHbiY0N5999147bXXMH/+/ChXwFAohLVr12L+/PlUNkLJ\nEkq2su0cOHAAOTk55EKglDU1NcFut0fJxEApLJz/+Mc/8Pzzz6tktbW1tB/mkksuobSJd9psNtxx\nxx341a9+hRtuuIHeuWfPHlL0/ud//kfllnjixAkYjUaMHj06Kq1erxc5OTl4/PHHkZ2djT/84Q8A\nQP7Xok4rByRl/Y78t+jnBMJNDQBN+g4cOKDaawOAlG2/30+yw4cP49Zbb8X//d//AQhPzIWsqqoK\nt912myovQjZx4kQsWrQI48aNI/c4MXnZunUrdDodFi1ahAsuuABaFBcX08REKLP9+vXDsmXLMHfu\nXGzdupXSA4Tby5o1a5CYmKiqV0ajEVarFa+++irJZFmmepWZmUn3CUS9GjVqlOqZwi0jIyMDo0aN\norZfUlJCkwe73Y6ysjKqQ5deein1H3a7HRdeeCFSU1Nhs9nw4x//mPpFsRdn8+bNMBqNuP/++zFs\n2DAAwO7du1XpUbp/VVdXY/HixViyZAmVl2iPwWAQV155JbxeL8rKyvDll19SHkV/I/qXb775BsCp\nPWQTJkwghVfcJxTJCy+8ELm5uXj77bepror8NzQ0YPDgwcjNzSWLv2g71dXVGD58OH70ox9hyZIl\nKle8Y8eOUV1QurICp4xqkekReX3ooYfI1ejDDz9UpUe8w+Vy4dlnn8WsWbPw+eefQ4mYHD777LP4\n7LPPVDKlMefQoUOkpM2cOZOuP/vss9i0aROAcNsRssLCQsyePZtker0es2bNom9z5MgRep9wnQOA\na665Bv3796dvXFtbS0aj999/H1VVVSRTumaKtMyfP5/KT7h6OZ1OLF68GPPnz8d//vMfVR7ff/99\n5OTkYMuWLar8p6SkIDU1FevWrYPZbMa3335LstmzZ5NM2feI+tjSfffffz9SU1PJdWzPnj2kvF10\n0UX49NNPEQgEYDabsWrVKlqZnTBhAiZMmACLxYK0tDT06tWL3NxEvfz73/8Oh8OhMm5UVFSo0qNc\n/amursZDDz2EpUuXkuK5a9cuMhrIsoyqqip8+eWXGD58OC655BKEQiFqXwcPHsS///1vLF68mFbl\nRF8o2pfW/Kg7YWUiBiKX/gB1BIQzQWtRFNory8vLowZfUFCA+++/X/W7nTt34q233sKtt96quUH3\nlltuwWWXXabyDwROKRPp6emYP38+gFPWXZ/Pp+pQlRZUwfr16/H555/TZBUIT7b+9re/AQgPPsXF\nxTQBq6mpaTG/p8uuXbtw8OBBAKc6c7vdjpUrV+LRRx8ll4Di4mK8+uqrAKDazAmov3G/fv0wdOhQ\nmtBmZWVh27ZtAMLltWjRIpINHjw4KoqJWMVQPnPPnj246aabcNttt9G1HTt2IC0tDSUlJbj88svh\ncDii3D+2bt2K22+/XeVKJdiwYQO++OIL2ninJbv99tup8xR1Z8OGDdi9ezduuukmWlY9evQo3njj\nDaxZswbBYBC33357lGKTkJCAAwcO4M4771SlRygTwWAQP/rRj8hXHAh3ii+88AKlRTlQAMCyZctQ\nWVmJCRMm4NFHHyXXhqamJjidTqSkpGD06NFYu3YtCgsL6b709HTYbDZMnToVa9eujZoYRW7uF2m5\n9957qc5Htjnh8qBEWCTHjRsHvV6Piy66iHxXlZvFXS4X7rzzzqjv4Ha78eijj+KSSy5BQUGByi9b\nRHUDwvVI+OmL9AuLayR+vx/33nsvXn31Vezfv191H3BqQNq4cSOOHDmCEydO0ORei8iIJpHWUpEP\n0S8AwBdffIHi4mIA4f5ByLKysrBy5UrKl9J1b/z48VEuPHPnzgUQdrsaPHiwql4pXRnuvfdeDBky\npMXIWP/617/ob1EH9u/fj9tuuw2PPvooGQ+8Xi/S0tKwevVq7Nu3D3feeScpCkBYufF6vSguLsbP\nfvYz1UZqAPj8889RXFyMhQsX0j2iXq1bt47uKykpobL8xS9+gXXr1qG6uhoLFiwg1z0g3F9t2LAB\nwWAQP/vZz2CxWKhvFIrtsWPHcO+992LKlCmYOHGiqmxkWcaDDz6IsWPHUj8XCoVw1VVXUXpEMAog\nbNx44okncP/996OhoQHf+973VJPLTz75BDabDS+++KLKvUepjABQKXWSJOGrr76iPI8bN071bYqK\nilBSUoJrr72WlGmRHr1ej23btuHKK69UPU+kVSixBQUFKhdaWZaxfft2AOG9JFdddRXJrFYr9u7d\nS0qEMj1JSUl44oknsHLlStTV1WH8+PGqtIr+d8OGDbjyyiuxePHiKCVaMGfOHNovIlCuDIrv4XA4\n8Mc//pGuK40GiYmJJBsyZAgeeOABUqqCwSAZaK655hrMnTuX3idJEt03YsQI3K4vAisAABPUSURB\nVHXXXSQzGAykUPfu3RtLlizBlClTqFyVbsRz5sxRuWEJY1tZWRl++MMf4u677yZlW/QLVVVVuOWW\nW3DDDTeoDA8nT57EihUrUFtbS/VV8M4775BM2Y+I+qi8T7jbJSUl4fnnn8eKFSsQCAQwb948UpiB\n8Ni4efNmSJKEe++9FytWrCDj3VdffYXa2lq43W48+OCDuO+++zBp0iTVt5IkCY888ggWLlxIaZVl\nGStWrKD0JCUl0TxKpPXBBx+E3+/HtGnTVO1AbBIPBoMoLi7GN998g9dff53kvXr1Ql5eHp544gka\nr1qb98UDrEycJna7XeUzLGhqaoIkSZoy0SBb0yAjZcrQkqcjKyoqor+Vlg+r1aqy1hiNRpSUlFDk\npcgJu4gkoHxfZKi/YcOGUUVXRjsRPuJ+v58m1iKtpaWl+Mtf/oJ58+apOu78/HxyATIYDHj44YdJ\nERGWE+DUZmORNmGpbgllWR04cABPPfUUdSLiGWID3uDBg8nVpqamhlYw/H4/mpqaaJLQ1NQUVe7i\nWUq/79TUVJhMJlVUHGVeAJB/qcViofcNGDAAN998Mw0w4vnV1dVIS0vDhx9+iAULFtAETXD99dfj\n0ksvVaUHCHf2f/jDHzBz5kwKKQeEv8cnn3xCssTERHpmWlqaSnbLLbfQxDMzMxMffvghvF4vbrjh\nBowbN05V7z/66CO679JLL6W0KN2ABg4cSGkV6HQ6FBUVYebMmbj44ovx5ZdfUnmWlJRg9+7dGDt2\nLK0ECSXP5XJh0qRJSExMxH/+8x/IsqzyeRfuBXfccQcuvvhiSqtQkiMtPkB40FWWv1Jms9lUisSW\nLVtU+RCyxMRESqNyY//QoUPRv39/moyI7zFkyBBVpLPIkMai/Q4fPlwlEy6Kot0pfZZHjBiB48eP\nY8uWLfjpT3+qsqybzWZaxvd4PPjFL36B//f//l9UHybqfSgUwldffQUtZFmmPmTEiBFUzw8dOoTV\nq1eTlVopM5lM6NOnDyn3mZmZ1H/169cPDQ0NKkux0qVKmRal9VK4qbz44os0mVUqPH6/H1u3bqV7\nhKvnxIkTMX36dAwaNEg1yUtOTobb7UYoFMIzzzxD4cDFs3w+H3784x9j2LBhlB5Rr3bt2oW77rpL\n5UI6YsQIbNmyBRs3bsRdd92FyspK7NmzB0B40q2UXXnllfRNDQYDDh06hFAohDvuuAN9+/bFqlWr\nqG0dOHCA7hsxYgRCoVCUIefiiy8mmfI7bt26FRs3bsTcuXNx9OhRiv43fPhwhEIh1NbW4vrrr8fC\nhQupDxDPEeGVP/74Y3qemDQKK7VybHA4HEhISMD69esBgAwGwudfyCLdTIFwf2W1WrFx40bVBmkA\n1B+I5yrbZzAYpO89fPhwlUwYSN59910AUIWXHT58OAwGAw4ePIjFixfTCmtkBLyKigosW7aMggko\nEf1dRkaGShEDgH/+85/0t4gGNHz4cFo5LiwsxAcffIB+/fpFyWw2G8aOHUv70zIzM6kfyc3Nhd1u\nV0UUUkYdE2mRJEnVx/Xt25dkdrudyl2QkJCAkpISZGdnqzaK33DDDbj88ssxadIkak/KSFfPPPMM\nFi9erOqXZFlGcXExHnjgAYwaNSqqbIRMfB8gPN8IhUIk27t3LxmnRo0aRbIHH3wQ1157LbKysiga\nluh/7777bgwcOBBZWVkqQ5F4Zq9evZCRkUHlpYyeJWTKtAplYNasWTh+/DhycnLI60NE4PrJT36C\nuXPnIj09HQ6HA7fffju5V4n5wcqVK7Fnzx6qVyaTCQ8++CCSkpJQXV2NxsZGmsuI8Uo5B4oH+JyJ\n00T4s0ZSVlYGi8WiKRODmtLlRyCs76JRKJ8naK/s22+/xe9///t23ZeYmIjjx4/j6NGjmjv/N23a\nhKamJjz44IOq+yKJlMnyqXjVRUVFqkltRUUFuTvk5+erNq3W19fDarXC5XLh8ssvx5QpU7B8+XJU\nVFSoJu5i8iPKMiUlRTOmPxBukCJ9dXV1WL58OXr37o3GxkbU19drfg/lNfHc999/H++//z5d37t3\nryrShPI+5dkcohxa+v7K/QRpaWmkTCl91IFTMeTT0tLw4osvora2Fo2NjVi1apWqviktMcp3bd26\nFdOmTcONN96ITz75hL7Jd999h2+++QbTpk1D//798cQTT9A3UMpuvPFG1TOV5b1hwwZs2LBBld63\n3noLGRkZUfclJCRQfpXRcpR57NmzJ2688UZ8+eWXaGpqIoVYDETKCCPiuUKJ/81vfoOqqir89re/\nxXfffUfPHDJkCL755hscO3ZMNaCJiYNwcfjggw9IFjmhVsp69uxJ5b5582aVJd9qtaq+iah/IhJJ\nMBjE9u3bsWDBAvqNGOh27NiB3//+92SN++tf/6pKg3DHkiRJJRPfQwzuYmIk8qFsc7/+9a8BhBWs\nxMREmsjOmTMH27dvx7///W9kZ2erzlERLlBr1qxBZWWlZlhCl8sFr9cLk8mkqh+ffPIJnE4nqqqq\nomTKZ5pMJtUkb+3atVF7tR566KGo+8S3U/ZDa9aswdatW5GdnU0WR0FRURFN+EwmEyllwgKsTI/P\n58P+/fuxYMECjB49Gm+//Ta++uorqlfCfWHgwIGq+8SKyIwZMzB58mS89NJLAMITxiNHjmDbtm30\n/YWrp8lkUskmT56Ml19+mfJ48uRJqpNr1qzBmjVrVGVTWlqKwYMHY/Lkyap8KKPkiTYnZCLk7J49\ne7BgwQIKgWkwGGCxWLBjxw7qK8Qqgdi4LL5jY2MjXnvtNTz99NO0GigCN4iyVba5vLw8HD58GJWV\nlXjvvfewa9cuOntD7FEQvy8rK4PJZKLw1oFAAC6XCzt37qRVONF2Nm/ejF69eqnuVeY9MzMTJSUl\nkCSJZMCplWHh8iKURUmScPToUZjNZjQ0NCAzMxNlZWUwGAzIzc2Fz+ej1ZWf//znSElJgcFgwEsv\nvaRaVVeOB2VlZVTmgNq1sL6+HhaLBUePHkV6ejpqa2vx0UcfYdq0aTh48GCUTPnMyEh7Tz75pKpu\n+Hy+qBVxcZ/yfCZRJmVlZaSUi9UHILzSpqwf4pnKvZhlZWXUz6WmpsJgMOChhx5CKBTCK6+8ggMH\nDlDfkZiYSGNWWVkZ9SterxfJyckYNmwYfc+EhARs27YNHo8HycnJ2L9/P/7yl78gMzMT9fX1Kpny\nmbm5uTh48CApWkrXYYHYy6UcPyPrT6RxTpyl4vf7kZycTPkWG+W3bdtG+RRBL0R6xCqe0q0JgCqQ\nRHFxMXbs2IGePXti586dqKqqwuHDh+F0OknhEONf5Hyhu+CVidNkzJgx2L9/v2oDVlVVFfbt24fB\ngwdrysRAV1ZWFiUTnU1FRYVKtnHjRvq7PbLi4mI89dRTKitda/eJzm7BggVYunQp+vTpo3J1kiSJ\nzhH4/PPPo0LH5ubmYseOHdi8eTOysrKoU5JlmX67adMmlVWwoqKCGqewpAnKysqirDoi7cePH1dt\nTlaWaaR7jTJvvXr1oj0QmzdvRkZGBm6//XYa7CMVIeVzAeCmm24CEO4M7rnnHupsJUnCjBkzNO9T\nlndNTQ1KS0tV31/peqG05g0cOJB+JwYLMdkV+RGWMRGtSJlWm82GL774QjMf48ePJxeRYDBIE4Ha\n2lpMmDAB48ePx1NPPYX+/ftTpylk4j7lM8Uk4b777sPSpUtVZQOErYQPPPBAi/eJuiPLclQexfLy\npk2bYLPZolbMxGZb5XOVvv8ff/wxKWWiLQwePBgGgwGrV69WreYcO3YMWVlZSE9Px7p161Sb45Xt\nOFLWo0cP7N+/H5988glWr14N4NQqYHp6Ot0bDAZV31ikZ+HChViyZElU3ZUkifbGrFu3LipCVE5O\nDpKTk/HBBx+oZMKyn5+fj3Xr1qkmMGLDKwD87ne/o/suvfRS1UbtrVu34quvvsKcOXNQUVGh+p7F\nxcV48803sXHjRuh0uqhY82+++SYNisOGDcP+/fvJT95qtaKqqgo6nY5k4psKK7yQKVcERF2XJIlW\nZMSeKuV9l1xyiSqt+/btwz//+U/KR8+ePVXuAcIVRLxTDMhVVVWqPIozCAYMGICrr74aH3/8Mb76\n6itVHP3LL7+c6pW4Lz09HY2NjXA4HLj55pvx5ptvkgVa1Odp06YhNTWV6o64T8iuvvpqVVoyMzNJ\nkVi4cCG524j+WZST6JPEvcrJkMPhwPbt21XPFZbn8ePH4+qrr8amTZuQk5ODw4cPIzU1FTU1NeRi\ndODAAVRVVdFhhKIdpqSkwO12qzY1V1dXw2g0IisrCy6XiybooVAIWVlZaGxshCzLWL9+PRISEih8\neE5ODp195HK58PXXXyM/Px+BQIAUTYfDgT59+sBisZCLj8j/yZMnaUNuYWEh5V2WZYoAVVRURDJZ\nlsnFR4xhog4Ka7U4M+XQoUMUVWvMmDGqQCM6nQ65ubnQ6XTYu3evamIv8i7mCMrxWTkJPH78OM0f\nRF8zatQoXHXVVaq5hXL8UM47hFIEhEMu33PPPRQ1C1Bbr8V9BQUFKqOT+Mb79u3DmDFjoiapyvoh\norMBp/bkVFVVYc+ePTRx93g8yMzMRFZWFoxGI4qLi1XeDSJt4p3K82fEiolYvRXBA2w2G6xWK9av\nX49p06ahuroaDoeDZOI+8Uzg1Mpqz549sXTpUhqzlCgVIpEPZb8h6p94rvi3xWJBXl4etm7disGD\nB6OkpITSI9pOZWWlqlyFgpadnY2HHnoISUlJyM/Px3333acKZhIIBHDw4EEKXlFYWEjjQzAYpD2D\nke6y3YUkx7sjVpzh8XjwwAMPICEhATNnziR/RLfbjeXLl+Phhx8m2aFDh/Cvf/0LbrcbTU1NsNls\nMJvN5JOolAHhRjR69GiUlZXh4MGDMJlM1BBsNhsGDRqE0tJSVccBhLV2EWtbROsRWrHRaKTBXwwO\nynCOdrsdPXr0wKFDh8gqLZ4pOv/S0lKKsS1iXptMJvqt1WpFIBAgS4UIB6h0QxD5EAdmuVwuyLJM\n6VEOfCkpKfB6vfD5fNRh2Ww2KifRSJVLqSLuenJyMm1m69u3L8rKyuiQpP79+6O0tJTSKu5LTk5G\nZmYmDh8+DL/fTycS22w2NDQ0ICcnB/X19XC73QgEAnRoTFNTE5KSkugAI2WZK8tY+LQrz0zIycnB\n0aNH6dCcCy+8EDt37qQyzcrKQk1NjapsTCYTLrjgAni9Xuzdu1flPiTiwYtQmuLkTBE1QoSgEx2Q\nWEESp1kbDAY6VEu802QywW63w+l0orS0VHVCeUJCAi666CJIkoRvvvlGFWPeZrPBZDIhLS0NZWVl\nUfclJCSgsbERqampqtNwJUlCYmIipk6divfee09VjmazmX7Xu3dvVFVVwePxqJ5rt9tJERMWVHHq\nslJxSUhIgMViQV1dHSwWC9LT02lSAZxyZbRYLJR3gYi6Iknqk1qTkpKofgoXAKPRiMbGRjrITNR3\n4dJx4sQJWCwWBINBOljJYDDA6XSSRU55gq4ImSlkVqsVdrsdVVVV5KonlCXhQrRv3z7Y7XbVCcBW\nqxXDhg3DN998o7JYpqen02F1NpsNwWBQZdUXkXt8Ph9ZWBMTE8mQYDAYMG7cOBQWFqqeK8InTpo0\niRRe8VwRXjcnJwclJSWqb24ymWAwGNCnTx/s3LlTdWK9OJRN1DHlfcLCLSLZiAO9MjIycOzYMQoD\neumll+LTTz9VGTHEc0V/ZTab0aNHD5SXl6tO9DUYDDQZqq2tjbrPbrcjLS0Nhw4dUlmj9Xo9EhIS\nKB9iciki4BiNRphMJtTV1ZGCLO4Vqzculwt9+/ZFdXU1GhoaqJ6LyFhHjhxRnYgtykPUT9EefD4f\nTV5MJhMqKyvp4DZRx8XZBqI+R65M2Gw2Om/DbDarDvpLTU2Fy+WivCrHEFEWon9qaGigyaTItwgt\nKxQp0VeazWb4/X4kJSXRioKo2+JsIRFNRzmxV7ooCzcW8e0zMzNRV1cHt9sNi8UCu92ummiLsrBa\nrcjLy6OJKhBWCkQ/IcIzKyfNTqeT+kfl4aPi0Eax2jdo0CBSypT3CdfIr7/+mvIjxshQKKQKeQqA\nxivxDqWBKzk5mQ5AFBN0JaI99unTh1YtgXDfUFVVBYvFQuOGMAqIs2dOnjypOZYnJiaiurqagj4I\nuXDPEmOrcP2RZZks8GIsU34DUe8kSVKNVyK/KSkpdACiso82mUzIz89Hamoqvv76a7hcLrovISGB\nDjwsLy+n+5R1yOl0wuPxqMYdkccTJ07AarXSYZfKPIqx1efzIS0tDZWVlZBlGfn5+WhqakJ1dTWy\ns7NRXV0Nv9+PgoIC1NTUUP279dZb8de//hX19fWYPXs2QqEQ7S0KhUL4wQ9+gLy8PJhMJirrUCiE\n5557DuPHj6c9aSNHjowKrnEmYWUiBmpqarBu3Tp8++231NDnz59P1iUhU3acSpSTSoZhGIY5WxCK\nhXCLUmI2m2G1WnHy5MkoWUcQBjPlmCkURuF2E7nHCAhPnvV6PU6cOKEZPEAoekqZOOE4FApp3ne+\no5zUM6ePMDYJt6hQKESBUcSqstPpJFfBYDCIfv36ITU1FTt27IhSAAVpaWkYPHgwRfiKZPXq1VEe\nJmc0X6xMMAzDMAzDMAwTC7xngmEYhmEYhmGYmGBlgmEYhmEYhmGYmGBlgmEYhmEYhmGYmGBlgmEY\nhmEYhmGYmGBlgmEYhmEYhmGYmGBlgmEYhmEYhmGYmGBlgmEYhmEYhmGYmGBlgmEYhmEYhmGYmPj/\njkUyi+1glfoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19b9d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df_merge['timing'], df_merge['duration_min'], '*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The darkness on the bottom is hard to read. Let's try to get a density plot to have a better idea."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a379229d0>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7jRs35sUXX9zlVrCpU6fmH/7hH/LSSy+lubk548ePr2zr169fRo0alRUrViRJ\nVq5cmeTtyffb1dbWZtiwYZU6AABA19XhkZLO8Oqrr6ZcLqdPnz759re/nWeffTbV1dU566yzcsEF\nF+T1119PkgwePLjN6wYNGpTly5cnSerr63PkkUemqqpqlzr19fUH50AAAIB3rNBQsnHjxiTJzTff\nnNNOOy3nnntunn/++SxYsCC9e/dOuVxOr1690rNn22ZWV1ensbExSdLU1JTq6upd9l1VVZV169bt\nc5tmzJjRbvnMmTP3eV8AAMDeFRpKWlpakiRjx47NJZdckuTf55QsWLAg5513XruvK5fLlQns5XI5\npVKp3Xq7KwcAALqOQkPJ9luuTjnllDblJ598cn72s5+lpqYmLS0taWlpaTNa0tzcXFl5q6amJk1N\nTbvse8c6+8KICAAAHFyFTnTfPldk+4jJdtu2bUuS9OzZM+VyOWvXrm2zfc2aNRkyZEiStye1b9iw\nIVu2bNltHQAAoOsqNJQMGzYs/fv3z+OPP96m/KmnnspRRx2VSZMmpVevXlm2bFllW0NDQ1544YWM\nHj06ydu3e7W2tlYmvidvT35ftWpVpQ4AANB1HfYP//AP/1DUm5dKpRx++OG5//77s2HDhvTu3TsP\nP/xwfvazn+Xiiy/OiSeemKampixcuDC9e/fOpk2bcuutt6alpSVf+tKX0qtXr/Tr1y9/+MMf8sAD\nD+Twww/P2rVr8/3vfz8DBgzI5z73uQM7r6Rp44HbFwAAvBvUvGe/d9HhJ7p3pv/7f/9vFi5cmNWr\nV2fAgAH5xCc+UXl2ybZt2zJv3rw8+uijaW5uzsiRI1NXV5ehQ4dWXt/c3JzZs2fn8ccfT7lczpgx\nY1JXV5f+/fsf2IZ6ojsAALR1AJ7o3iVCSbchlAAAQFsHIJQUOqcEAABAKAEAAAollAAAAIUSSgAA\ngEIJJQAAQKGEEgAAoFBCCQAAUCihBAAAKJRQAgAAFEooAQAACiWUAAAAhRJKAACAQgklAABAoYQS\nAACgUEIJAABQKKEEAAAolFACAAAUSigBAAAKJZQAAACFEkoAAIBCCSUAAEChhBIAAKBQQgkAAFAo\noQQAACiUUAIAABRKKAEAAAollAAAAIUSSgAAgEIJJQAAQKGEEgAAoFBCCQAAUCihBAAAKJRQAgAA\nFEooAQAACiWUAAAAhRJKAACAQgklAABAoYQSAACgUEIJAABQKKEEAAAolFACAAAUSigBAAAKJZQA\nAACFEkoAAIBCCSUAAEChhBIAAKBQQgkAAFAooQQAACiUUAIAABRKKAEAAAollAAAAIUSSgAAgEIJ\nJQAAQKGEEgAAoFA9i27AdltUuTQ5AAAaKElEQVS3bs3VV1+dESNGZPr06UmScrmchQsX5qGHHsqm\nTZsycuTIXHbZZRk6dGib182ZMydLly7N5s2bM3bs2NTV1aV///5FHQoAALAPusxIyb333pvXXnut\nTdl9992X+fPn5+Mf/3iuuuqqNDY25pvf/GYaGxsrdW677bY89thjmTp1aq644oq88soruf7669Pa\n2nqwDwEAAHgHukQo+f3vf5+f/OQnOfzwwytlTU1NWbx4cS688MKcc845GT9+fL7+9a+nubk5jzzy\nSJJk9erVWbJkSb7whS/kjDPOyMSJE/PVr341r776apYtW1bU4QAAAPug8FCybdu2fP/7388nPvGJ\nNrdcvfTSS2lubs748eMrZf369cuoUaOyYsWKJMnKlSuTJOPGjavUqa2tzbBhwyp1AACArq3wUHL/\n/fenpaUl559/fpvy119/PUkyePDgNuWDBg1KfX19kqS+vj5HHnlkqqqqdlsHAADo2gqd6P7aa69l\nwYIF+fu///v07Nm2KU1NTenVq9cu5dXV1ZU5JU1NTamurt5lv1VVVVm3bt07atOMGTPaLZ85c+Y7\n2h8AALBnhY2UtLa25gc/+EE++tGP5gMf+MAu28vlcruvK5fL6dGjR+XfpVKp3Xq7KwcAALqWwkZK\nfvrTn+aNN97IjBkzsm3btkp5uVzOtm3bUlNTk5aWlrS0tLQZLWlubk5NTU2SpKamJk1NTbvse8c6\n+8qICAAAHFyFhZInnngi69evz2WXXdam/JVXXsljjz2WL37xiymXy1m7dm2GDBlS2b5mzZrKz7W1\ntdmwYUO2bNmS3r17t6kzatSog3MgAADAfikslHzxi1/cZZTjpptuSm1tbS688MLU1tbmhz/8YZYt\nW5ZPfvKTSZKGhoa88MILueCCC5Iko0ePTmtra5YvX55JkyYleXvy+6pVq3LhhRce3AMCAADekcJC\nyY6jH9v17t07hx9+eI4//vgkyeTJkzNv3ryUSqUMGTIkCxYsSHV1dc4888wkb6/MNXHixNxyyy1p\nbGxMv379Mnfu3AwfPjynnnrqQT0eAADgnSl09a29mTJlSkqlUhYvXpzm5uaMHDky06dPbzNf5Ior\nrsjs2bMzZ86clMvljBkzJnV1dZXJ8AAAQNdWKu9umSt2tW5V0S0AAICuZcCw/d6F4QQAAKBQQgkA\nAFAooQQAACiUUAIAABRKKAEAAAollAAAAIUSSgAAgEIJJQAAQKGEEgAAoFBCCQAAUCihBAAAKJRQ\nAgAAFEooAQAACiWUAAAAhRJKAACAQgklAABAoYQSAACgUEIJAABQKKEEAAAolFACAAAUSigBAAAK\nJZQAAACFEkoAAIBCCSUAAEChhBIAAKBQQgkAAFAooQQAACiUUAIAABRKKAEAAAollAAAAIUSSgAA\ngEIJJQAAQKGEEgAAoFBCCQAAUCihBAAAKJRQAgAAFEooAQAACiWUAAAAhRJKAACAQgklAABAoYQS\nAACgUEIJAABQKKEEAAAolFACAAAUSigBAAAKJZQAAACFEkoAAIBCCSUAAEChhBIAAKBQQgkAAFAo\noQQAACiUUAIAABRKKAEAAArVs+gGtLa25sEHH8zDDz+cN998M0cffXTOPvvsnH322SmVSimXy1m4\ncGEeeuihbNq0KSNHjsxll12WoUOHVvaxdevWzJkzJ0uXLs3mzZszduzY1NXVpX///gUeGQAA0BGl\ncrlcLrIB99xzT+6///58+tOfzgknnJAXXnghCxYsyJQpU/LJT34y9957bxYtWpSpU6dm4MCBmT9/\nftavX58bb7wxNTU1SZJZs2Zl+fLlmTZtWqqqqjJ37tz06dMnM2fOTI8eB3AwaN2qA7cvAAB4Nxgw\nbL93UehISWtrax544IF8/OMfz6c+9akkyZgxY7Jx48YsXrw4Z511VhYvXpwLL7ww55xzTpLkxBNP\nzPTp0/PII4/k3HPPzerVq7NkyZJceeWVmTRpUpLkmGOOyVVXXZVly5ZlwoQJhR0fAACwd4XOKWls\nbMxHPvKRXYLDkCFDsnHjxqxcuTLNzc0ZP358ZVu/fv0yatSorFixIkmycuXKJMm4ceMqdWprazNs\n2LBKHQAAoOsqdKSkX79++fznP79L+ZNPPpkBAwZk3bp1SZLBgwe32T5o0KAsX748SVJfX58jjzwy\nVVVVu9Spr6/vpJYDAAAHSuET3Xf28MMP59lnn01dXV2amprSq1ev9OzZtpnV1dVpbGxMkjQ1NaW6\nunqX/VRVVVVCzb6YMWNGu+UzZ87c530BAAB716WWBP7FL36R2267LRMnTszHPvax7G4Ofrlcrkxg\nL5fLKZVK7dbbXTkAANB1dJmRkh//+Me566678sEPfjB/8zd/k1KplJqamrS0tKSlpaXNaElzc3Nl\n5a2ampo0NTXtsr8d6+wLIyIAAHBwdYmRkrlz5+bOO+/Mn//5n+fLX/5yJYDU1tamXC5n7dq1beqv\nWbMmQ4YMqdTZsGFDtmzZsts6AABA11V4KHnwwQezaNGinHPOOZk+fXoOO+ywyraRI0emV69eWbZs\nWaWsoaEhL7zwQkaPHp0kGT16dFpbWysT35O3J7+vWrWqUgcAAOi6Cr1966233sqcOXMyfPjwTJo0\nKS+99FKb7ccff3wmT56cefPmpVQqZciQIVmwYEGqq6tz5plnJnl7Za6JEyfmlltuSWNjY/r165e5\nc+dm+PDhOfXUU4s4LAAAYB8U+kT3Rx99NLNmzdrt9ttvvz19+/bNvHnz8uijj6a5uTkjR45MXV1d\nhg4dWqnX3Nyc2bNn5/HHH0+5XM6YMWNSV1eX/v37H9gGe6I7AAC0dQCe6F5oKOl2hBIAAGjrAISS\nwueUAAAAhzahBAAAKJRQAgAAFEooAQAACiWUAAAAhRJKAACAQgklAABAoYQSAACgUEIJAABQKKEE\nAAAolFACAAAUSigBAAAKJZQAAACFEkoAAIBCCSUAAEChhBIAAKBQQgkAAFAooQQAACiUUAIAABRK\nKAEAAAollAAAAIUSSgAAgEIJJQAAQKGEEgAAoFBCCQAAUCihBAAAKJRQAgAAFEooAQAACiWUAAAA\nhRJKAACAQgklAABAoYQSAACgUEIJAABQKKEEAAAolFACAAAUSigBAAAKJZQAAACFEkoAAIBCCSUA\nAEChhBIAAKBQQgkAAFAooQQAACiUUAIAABRKKAEAAAollAAAAIUSSgAAgEIJJQAAQKGEEoCDoHX1\n79O6+vdFNwMAuqSeRTeA7m/7F60eg99fcEug87Wu/n2Hr/X2Qoj+AgC7KpXL5XLRjeg21q0qugWd\nal++LO3uL76+aNFV7HyN7s91fTDoOwB0WwOG7fcuhJJ98S4IJQfiS1f55ZXtlpeOG53ElyuKty/X\n+e6u5yJs70MHkv5Id9GRfut6hi5KKDnIumko6cgv+g5/Mfvtb3a/bcSJggmF23697/Wa3tO13EHl\n3/2u3fLS8ce3LRhxYsfff6e67QWVPR3b7oKNPklXtqd+29417XqGLkYoOci6UShpL4js81+Ed/rS\ntLsvYNuVjj9eMKFQewwkO1zPe7qW//jcgennfU96+xf0LgFlB+21o1K/vSCzsx376B7q65N0Za2r\nf//vfbYD1/SOIcU1DV3EAQglJrq/i+xuRKTNF7R9+Ovwjl+YOvJFrW+SUpLtKbc1PjA4eNoNJLsJ\nIru7nv/w//71Hb33yxub2/x83Huqkv/3r3nfsUckz62qBJSdtdeOvv/2/9L2gh2/mLXTf7cfV2mX\nLf/+2vLLK1M6bvQ+TdKHg2HnQLJjP21zTe/QD7bX335NJz5r4N3gXRNK/umf/in/+3//76xbty7H\nHntsLr300nzgAx8oulkHxcEOI7v74va+/Hsw2f4B4ksQB0O7feDfrvndXc/tXcc7h4uO+Oc/bt79\nxv/39v/e928/9j1p2F7bUKn7b/9vL2y0N8LS5svc9tGW7f1+xImCCV1Oe4Fke//oe9Kwfw/cxx/f\n7gjK9mt6+76S9sOJ4ALdw7vi9q0lS5Zk1qxZueCCC3L88cfnpz/9aV588cV85zvfycCBAw/cG3Xh\n27eKGiXZ8UvV+449In1PGtbmNi4fAhwsu4yUdHCU5J2MjuwuvPzzHzdnbN8+Sf5ttCRv94skbUZL\ndteGneu2uZWrg7dT7mk+i9u46Era9Nl2/oiQdOx63s51DQUypyQpl8v567/+65xyyim5/PLLkyQt\nLS256qqrMm7cuFx22WUH7s26cCjZURHzSZJ/n1OSRCChEDte+3sK5O1dz/s7l2TngJ7seV7JnvpU\nh+aVdHCifCKM0LXtdk5JssfreTvXNXQB5pQkq1evzhtvvJHx48dXynr27Jlx48bln//5nwtsWXF2\n/AW9/Uvaviw1Wn555a4rAHXkhUZHKNj2a6919e8r13xHr+d+e5iQ3p6dQ8WJO4yEtAkhu5us287o\nx871d9dv2zumNvu2WhHdSI/B70/rv/27vb+SWlEODg3dPpTU19cnSQYPHtymfODAgVm9enVaW1vT\no0ePIprWJbQXUPam3Q+ADixLKpDQVfQY/P49B/IdA8s7VOrA6lh7/WPATu3o6B8P9uWPDPok3UHl\nDwodrAe8+3T7UNLY2Jgkqa6ublNeXV2dcrmc5ubm1NTUdHh/M2bMaLd85syZ77yRXcQB/2Xuw4Eu\nrEPXe1e5hrtKO6BgQgccurp9KNmd7VNlDugoyQG4Xw4AAGir24eS7aMgzc1tV8Npbm5OqVRKnz59\n9ml/74YREfZu+4iY8/3u49y+ezm3707O67uXc/vu1RnntttPttg+l2TNmjVtyteuXZshQ4akVOrQ\nFG0AAKAg3T6U1NbWZsCAAVm2bFmlrKWlJU899VTGjBlTYMsAAICO6Pa3b5VKpZx33nm544470rdv\n34wcOTI/+9nPsmnTpvzVX/1V0c0DAAD2otuHkiQ5++yzs2XLljz44IN54IEHcuyxx+brX/96Bg0a\nVHTTAACAvej2T3QHAAC6t24/pwQAAOjehBIAAKBQQgkAAFAooQQAACiUUAIAABRKKAEAAAollAAA\nAIUSSgAAgEIJJQAAQKGEEgAAoFA9i24AdJbly5fnpptuyp133lkp+93vfpevfvWru9Q999xzM23a\ntCTJ1q1bM2fOnCxdujSbN2/O2LFjU1dXl/79+x+0ttNWa2trHnzwwTz88MN58803c/TRR+fss8/O\n2WefnVKplHK5nIULF+ahhx7Kpk2bMnLkyFx22WUZOnRoZR/Oa9e0t3Orz3ZfLS0tue+++/LYY49l\n06ZNGTFiRC655JIcd9xxSaLfdlN7O6/67LvD1q1bc/XVV2fEiBGZPn16ks7vs6VyuVzutCOCgrz4\n4ov51re+ldbW1tx1112V8kceeSQ//OEP81/+y39pU79///45+uijkySzZs3K8uXLM23atFRVVWXu\n3Lnp06dPZs6cmR49DC4W4Z577sn999+fT3/60znhhBPywgsvZMGCBZkyZUo++clP5t57782iRYsy\nderUDBw4MPPnz8/69etz4403pqamJonz2lXt7dzqs93X7bffnl/84heZOnVqBg0alJ/85Cd54YUX\n8t3vfjfvfe979dtuam/nVZ99d5g7d24WLVqU008/vRJKOr3PluFdZMuWLeVFixaVp0yZUv7c5z5X\nvvjii9ts/+EPf1j+2te+ttvX19fXly+66KLy0qVLK2Wvv/56+aKLLio//vjjndZudm/btm3ladOm\nlX/0ox+1Kb/tttvKn//858uNjY3lSy65pLxw4cLKtk2bNpWnTZtWXrx4cblcdl67qr2d23JZn+2u\n/vjHP5Y/85nPVPpguVwub968uTx16tTyfffdp992U3s7r+WyPvtu8PLLL5cvvvji8mWXXVa++eab\ny+Vy+aD0WXGUd5Wnn346ixYtysUXX5zJkyfvsv2VV17J8OHDd/v6lStXJknGjRtXKautrc2wYcOy\nYsWKA99g9qqxsTEf+chHMmHChDblQ4YMycaNG7Ny5co0Nzdn/PjxlW39+vXLqFGjKufMee2a9nZu\nm5ub9dluqk+fPvnWt76VM844o1J22GGHpVQqZevWrXnppZf0225ob+c18Tnb3W3bti3f//7384lP\nfKLNLVcHo8+aU8K7yogRI3LzzTenb9++ueeee3bZ/uqrr6ZXr175u7/7u6xatSpHH310Pv3pT1d+\nwdbX1+fII49MVVVVm9cNGjQo9fX1B+MQ2Em/fv3y+c9/fpfyJ598MgMGDMi6deuSJIMHD26zfdCg\nQVm+fHkS57Wr2tu5raqq0me7qcMOOyzvf//7k7w9b+jNN9+s/E7+yEc+kmeeeSaJftvd7O28Jj5n\nu7v7778/LS0tOf/88/PEE09Uyl9//fUkndtnhRLeVfY0kWr9+vXZtGlT6uvr89nPfjZ9+/bN0qVL\nM2vWrJRKpZx++ulpampKdXX1Lq+tqqqqfPmleA8//HCeffbZ1NXVpampKb169UrPnm1/nVVXV6ex\nsTFJnNduZMdzq8++O8yfPz/33ntvkuSiiy7KkCFD8utf/1q/7ebaO6/6bPf22muvZcGCBfn7v//7\nXfrmwfisFUo4ZPTt2zdf+9rXcswxx+Soo45Kkpx88sl56623ct999+X0009PuVxOqVRq9/W7K+fg\n+sUvfpHbbrstEydOzMc+9rEsXLiw3Xrlcrkyqc557R52PrdbtmzRZ98FTj311Jx00klZuXJl5s+f\nn5aWlvTu3bvduvpt99HeeT3//PP12W6qtbU1P/jBD/LRj340H/jAB3bZXt7NulgHss8KJRwy+vTp\nk1NOOWWX8lNOOSUrVqxIc3Nzampq0tTUtEud7dso1o9//OPcdddd+eAHP5i/+Zu/SalUSk1NTVpa\nWtLS0tLmLzg7njPntetr79zqs+8OxxxzTJLkT/7kT9Lc3JzFixdn6tSp+m031955veCCC/TZbuqn\nP/1p3njjjcyYMSPbtm2rlJfL5Wzbtu2gfNaa6M4h4/XXX8//+T//pzIZb7stW7akd+/e6dOnT2pr\na7Nhw4Zs2bKlTZ01a9ZkyJAhB7O57GTu3Lm588478+d//uf58pe/XPmlWFtbm3K5nLVr17apv+M5\nc167tt2dW322+9qwYUN+/vOf7/IF5dhjj83WrVvTt29f/bYb2tt5/Zd/+Rd9tpt64oknsn79+lx2\n2WWZMmVKpkyZkldeeSWPPfZYpkyZkp49e3Z6nxVKOGSsX78+t99+e55++ulKWblczq9//euMGjUq\npVIpo0ePTmtra2XSVvL2xK1Vq1Zl9OjRRTSbJA8++GAWLVqUc845J9OnT89hhx1W2TZy5Mj06tUr\ny5Ytq5Q1NDTkhRdeqJwz57Xr2tO51We7rz/+8Y/5/ve/n8cff7xN+TPPPJMjjjgiH/rQh/Tbbmhv\n53Xbtm36bDf1xS9+Mddff32b/2prazNu3Lhcf/31mTRpUqf3Wbdvccj4kz/5k5x44om57bbb0tDQ\nkKOOOioPPfRQXn311Xzzm99M8vaqEhMnTswtt9ySxsbG9OvXL3Pnzs3w4cNz6qmnFnwEh6a33nor\nc+bMyfDhwzNp0qS89NJLbbYff/zxmTx5cubNm5dSqZQhQ4ZkwYIFqa6uzplnnpnEee2q9nZuR44c\nqc92U0OHDs2ECRNy5513pqWlJYMGDcqvf/3rPPbYY/nSl76Umpoa/bYb2tt5Pemkk/TZbqq9kYze\nvXvn8MMPz/HHH58knd5nPdGdd6177rknixcvbvNE94aGhsydOzdPPfVUNm3alOOOOy6f/exnM2rU\nqEqd5ubmzJ49O48//njK5XLGjBmTurq6Pa7sRed59NFHM2vWrN1uv/3229O3b9/Mmzcvjz76aJqb\nmzNy5MjU1dVl6NChlXrOa9fTkXPbo0cPfbab2rx5c+6999786le/yltvvZVhw4blU5/6VCZOnJjk\n7ech6Lfdz97Oq8/Zd4+/+7u/y7HHHlt5ontn91mhBAAAKJQ5JQAAQKGEEgAAoFBCCQAAUCihBAAA\nKJRQAgAAFEooAQAACiWUAFCI//pf/2s2btyY66+/PqtWrerw6373u9/lhhtu6MSWAXCweU4JAIW4\n6KKLcvvtt+c973lP0U0BoGBCCQAH3axZs/Loo4/mfe97X1atWpVvfetbaW5uzty5c3P00Ufn9ddf\nT1VVVT75yU/mJz/5SV5//fVMmDAhn/vc5/Lcc8/ljjvuyA033JD/8T/+R6qrq/OHP/whb775Zo45\n5pj89V//daqqqvLUU09lzpw56dGjR4499tg8++yz+eY3v5mBAwcWffgA7MTtWwAcdFdccUWS5Bvf\n+EaOPvroSvnvfve7nHfeefnOd76T6urqLFq0KF/96lczc+bM/OxnP8v69et32dfvf//7fO1rX8uN\nN96YN954I7/61a+yadOm3HzzzflP/+k/5Tvf+U5OOumkdl8LQNcglADQZQz8/+3cPYrCQBjG8Scz\nbwwGsdTGgFoJKWwtvYTnEEs7Sw9g4yU8R25gJ1h5gSg2QbuF3S2WRd0J7v9XZYr5ahIe3sx0OhoM\nBpKkbrerPM9lZmq320rTVGVZfuszHo8Vx7HMTFmWqSxL7fd79Xo99ft9SdJ0OlWz2fzLrQAAfoFQ\nAgCojTiOP7W99z/2aTQaH89RFOl2u8k5p69/JzvHJw8A6oo3NAAgCOecqqp6ydij0Uin00nH41GS\nVBSFzuezoih6yXwAgMdY6AUAAP6nyWSi1Wql6/X69LFbrZbm87k2m42ccxoOh/LeK0mSp88FAHgc\nt28BAN7O5XLRbrfTbDZTkiQ6HA5ar9fabrdUSwCghqiUAADeTpqmMjMtl0t572VmWiwWBBIAqCkq\nJQAAAACC4qA7AAAAgKAIJQAAAACCIpQAAAAACIpQAgAAACAoQgkAAACAoAglAAAAAIK6A/JCaawk\n12npAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3792cf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#density plot\n",
    "sb.kdeplot(df_merge.timing, df_merge.duration_min, cmap=\"Reds\", shade=True, bw=.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok so there is a clear decrease in March.\n",
    "Can we visualize this differently?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oOUoH+nGVUUhSCANscY5SXERqjHeYMMK7dHId9EKE8Q5l5YnUgvOhwYaVpnRo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b5d76d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12,12]) # make the whole figure bigger to show all \n",
    "plt.scatter(data = df_merge , x = 'start_month' , y = 'duration_min' , alpha = 0.1 )\n",
    "plt.title('The Trip Duration Trend over Each Month ')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Trip Duration(min)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see a clear dropout of trip durations during COVID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#from https://stackoverflow.com/questions/29545704/fast-haversine-approximation-python-pandas/29546836#29546836\n",
    "def haversine_np(lon1, lat1, lon2, lat2):\n",
    "    \"\"\"\n",
    "    Calculate the great circle distance between two points\n",
    "    on the earth (specified in decimal degrees)\n",
    "\n",
    "    All args must be of equal length.    \n",
    "\n",
    "    \"\"\"\n",
    "    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n",
    "\n",
    "    dlon = lon2 - lon1\n",
    "    dlat = lat2 - lat1\n",
    "\n",
    "    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n",
    "\n",
    "    c = 2 * np.arcsin(np.sqrt(a))\n",
    "    km = 6367 * c\n",
    "    return km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#use this to calculate distance between start and end points. \n",
    "df_merge['distance'] = haversine_np(df_merge['start_station_longitude'],df_merge['start_station_latitude'],df_merge['end_station_longitude'],df_merge['end_station_latitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.000000    13351\n",
       "1.010196      967\n",
       "2.170170      793\n",
       "1.364479      789\n",
       "0.709665      771\n",
       "0.729813      762\n",
       "1.526942      746\n",
       "1.358898      646\n",
       "1.547389      633\n",
       "1.231893      624\n",
       "2.074046      599\n",
       "1.309522      581\n",
       "0.958359      580\n",
       "0.673529      573\n",
       "0.653611      547\n",
       "1.541740      546\n",
       "1.463261      533\n",
       "1.941393      530\n",
       "1.369875      525\n",
       "1.003059      516\n",
       "0.845648      498\n",
       "1.086754      494\n",
       "0.897499      484\n",
       "0.393059      482\n",
       "1.496210      480\n",
       "1.127422      480\n",
       "1.239000      474\n",
       "1.060577      471\n",
       "1.858680      470\n",
       "1.152149      466\n",
       "            ...  \n",
       "1.974493        1\n",
       "0.616972        1\n",
       "1.383936        1\n",
       "0.330898        1\n",
       "3.045516        1\n",
       "0.047813        1\n",
       "0.602748        1\n",
       "2.069375        1\n",
       "1.981163        1\n",
       "0.693813        1\n",
       "5.118452        1\n",
       "1.254901        1\n",
       "3.769042        1\n",
       "1.988946        1\n",
       "1.926342        1\n",
       "4.093908        1\n",
       "0.502258        1\n",
       "2.060925        1\n",
       "1.180693        1\n",
       "1.406844        1\n",
       "1.742038        1\n",
       "1.252803        1\n",
       "2.258501        1\n",
       "0.935706        1\n",
       "3.189951        1\n",
       "0.649869        1\n",
       "1.070863        1\n",
       "6.526547        1\n",
       "1.721266        1\n",
       "2.299091        1\n",
       "Name: distance, Length: 574893, dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge['distance'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+fHl69uyZMWPG5Mtf/vIuvxZbHgf9UWvPxV3N55+0y9ssmk7T5la6C1RYR1lACbsj4wva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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abc764910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data = df_merge , x = 'distance' , bins = bin_edges);\n",
    "plt.xticks(ticks,labels);\n",
    "plt.xlabel('Distance in km');\n",
    "plt.xscale('log');\n",
    "plt.title('Trip Length')\n",
    "plt.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(-4.869,0.5,u'Distance in km')"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tk5ND69atL5hp8eLFLFmypMbmKCJSmTp7kSs9PZ0RI0YwaNAg7rjjDsrLy7GddQk/Yww2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1abc76d250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_context(\"notebook\", font_scale=1.1)\n",
    "sb.set_style(\"ticks\")\n",
    "\n",
    "\n",
    "sb.lmplot('duration_min', \n",
    "           'distance', \n",
    "           data=df_merge, \n",
    "           fit_reg=False) \n",
    "\n",
    "# Set title\n",
    "plt.title('Scatterplot of Distance')\n",
    "\n",
    "# Set x-axis label\n",
    "plt.xlabel('Time in min')\n",
    "\n",
    "# Set y-axis label\n",
    "plt.ylabel('Distance in km')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>timing</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56309</th>\n",
       "      <td>411</td>\n",
       "      <td>2020-01-15 08:15:21.920</td>\n",
       "      <td>2020-01-15 08:22:13.8300</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Grand Ave at Webster St</td>\n",
       "      <td>37.811377</td>\n",
       "      <td>-122.265192</td>\n",
       "      <td>338.0</td>\n",
       "      <td>13th St at Franklin St</td>\n",
       "      <td>37.803189</td>\n",
       "      <td>-122.270579</td>\n",
       "      <td>780</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.850000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0115</td>\n",
       "      <td>1.025441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56309</th>\n",
       "      <td>13378</td>\n",
       "      <td>2020-02-16 16:50:51.408</td>\n",
       "      <td>2020-02-16 20:33:49.4480</td>\n",
       "      <td>485.0</td>\n",
       "      <td>Funston Ave at Fulton St</td>\n",
       "      <td>37.773263</td>\n",
       "      <td>-122.471230</td>\n",
       "      <td>449.0</td>\n",
       "      <td>16th Depot Bike Fleet Station</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9612</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>222.966667</td>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0216</td>\n",
       "      <td>12791.691538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56309</th>\n",
       "      <td>1117</td>\n",
       "      <td>2020-03-02 17:44:47.553</td>\n",
       "      <td>2020-03-02 18:03:25.1260</td>\n",
       "      <td>126.0</td>\n",
       "      <td>Esprit Park</td>\n",
       "      <td>37.761634</td>\n",
       "      <td>-122.390648</td>\n",
       "      <td>23.0</td>\n",
       "      <td>The Embarcadero at Steuart St</td>\n",
       "      <td>37.791464</td>\n",
       "      <td>-122.391034</td>\n",
       "      <td>1828</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.616667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0302</td>\n",
       "      <td>3.315002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       duration_sec              start_time                  end_time  \\\n",
       "56309           411 2020-01-15 08:15:21.920  2020-01-15 08:22:13.8300   \n",
       "56309         13378 2020-02-16 16:50:51.408  2020-02-16 20:33:49.4480   \n",
       "56309          1117 2020-03-02 17:44:47.553  2020-03-02 18:03:25.1260   \n",
       "\n",
       "       start_station_id        start_station_name  start_station_latitude  \\\n",
       "56309             181.0   Grand Ave at Webster St               37.811377   \n",
       "56309             485.0  Funston Ave at Fulton St               37.773263   \n",
       "56309             126.0               Esprit Park               37.761634   \n",
       "\n",
       "       start_station_longitude  end_station_id               end_station_name  \\\n",
       "56309              -122.265192           338.0         13th St at Franklin St   \n",
       "56309              -122.471230           449.0  16th Depot Bike Fleet Station   \n",
       "56309              -122.390648            23.0  The Embarcadero at Steuart St   \n",
       "\n",
       "       end_station_latitude  end_station_longitude  bike_id user_type  \\\n",
       "56309             37.803189            -122.270579      780  Customer   \n",
       "56309              0.000000               0.000000     9612  Customer   \n",
       "56309             37.791464            -122.391034     1828  Customer   \n",
       "\n",
       "      rental_access_method  duration_min  start_year  start_month  start_day  \\\n",
       "56309                  NaN      6.850000        2020            1         15   \n",
       "56309                  NaN    222.966667        2020            2         16   \n",
       "56309                  NaN     18.616667        2020            3          2   \n",
       "\n",
       "      timing      distance  \n",
       "56309   0115      1.025441  \n",
       "56309   0216  12791.691538  \n",
       "56309   0302      3.315002  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.loc[df_merge['distance'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merge = df_merge[df_merge.distance != 12791.691538]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>start_time</th>\n",
       "      <th>end_time</th>\n",
       "      <th>start_station_id</th>\n",
       "      <th>start_station_name</th>\n",
       "      <th>start_station_latitude</th>\n",
       "      <th>start_station_longitude</th>\n",
       "      <th>end_station_id</th>\n",
       "      <th>end_station_name</th>\n",
       "      <th>end_station_latitude</th>\n",
       "      <th>end_station_longitude</th>\n",
       "      <th>bike_id</th>\n",
       "      <th>user_type</th>\n",
       "      <th>rental_access_method</th>\n",
       "      <th>duration_min</th>\n",
       "      <th>start_year</th>\n",
       "      <th>start_month</th>\n",
       "      <th>start_day</th>\n",
       "      <th>timing</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>83118</td>\n",
       "      <td>2020-01-31 15:23:47.733</td>\n",
       "      <td>2020-02-01 14:29:06.2630</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Buchanan St at North Point St</td>\n",
       "      <td>37.804272</td>\n",
       "      <td>-122.433537</td>\n",
       "      <td>13052</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1385.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68239</td>\n",
       "      <td>2020-01-31 15:40:31.616</td>\n",
       "      <td>2020-02-01 10:37:51.0000</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Folsom St at 15th St</td>\n",
       "      <td>37.767037</td>\n",
       "      <td>-122.415443</td>\n",
       "      <td>12235</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1137.316667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55407</td>\n",
       "      <td>2020-01-31 17:48:16.635</td>\n",
       "      <td>2020-02-01 09:11:44.3170</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>12822</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>923.450000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54929</td>\n",
       "      <td>2020-01-31 17:53:03.413</td>\n",
       "      <td>2020-02-01 09:08:32.6340</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>197.0</td>\n",
       "      <td>El Embarcadero at Grand Ave</td>\n",
       "      <td>37.808848</td>\n",
       "      <td>-122.249680</td>\n",
       "      <td>11705</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>915.483333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55700</td>\n",
       "      <td>2020-01-31 17:12:33.460</td>\n",
       "      <td>2020-02-01 08:40:53.6460</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>371.0</td>\n",
       "      <td>Lombard St at Columbus Ave</td>\n",
       "      <td>37.802746</td>\n",
       "      <td>-122.413579</td>\n",
       "      <td>3673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>928.333333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.811157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11333</td>\n",
       "      <td>2020-01-31 22:48:22.639</td>\n",
       "      <td>2020-02-01 01:57:15.7160</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>12137</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188.883333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.038713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11341</td>\n",
       "      <td>2020-01-31 22:48:12.989</td>\n",
       "      <td>2020-02-01 01:57:14.1650</td>\n",
       "      <td>60.0</td>\n",
       "      <td>8th St at Ringold St</td>\n",
       "      <td>37.774520</td>\n",
       "      <td>-122.409449</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Market St at Franklin St</td>\n",
       "      <td>37.773793</td>\n",
       "      <td>-122.421239</td>\n",
       "      <td>12321</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>189.016667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.038713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4038</td>\n",
       "      <td>2020-01-31 23:32:03.907</td>\n",
       "      <td>2020-02-01 00:39:22.0210</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>1589</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4059</td>\n",
       "      <td>2020-01-31 23:31:01.161</td>\n",
       "      <td>2020-02-01 00:38:40.8570</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>450.0</td>\n",
       "      <td>Funston Ave at Irving St</td>\n",
       "      <td>37.763934</td>\n",
       "      <td>-122.470651</td>\n",
       "      <td>12884</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>67.650000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1980</td>\n",
       "      <td>2020-01-31 23:49:09.230</td>\n",
       "      <td>2020-02-01 00:22:09.7540</td>\n",
       "      <td>238.0</td>\n",
       "      <td>MLK Jr Way at University Ave</td>\n",
       "      <td>37.871719</td>\n",
       "      <td>-122.273068</td>\n",
       "      <td>198.0</td>\n",
       "      <td>Snow Park</td>\n",
       "      <td>37.807813</td>\n",
       "      <td>-122.264496</td>\n",
       "      <td>10464</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>7.141293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1969</td>\n",
       "      <td>2020-01-31 23:49:03.972</td>\n",
       "      <td>2020-02-01 00:21:53.5730</td>\n",
       "      <td>238.0</td>\n",
       "      <td>MLK Jr Way at University Ave</td>\n",
       "      <td>37.871719</td>\n",
       "      <td>-122.273068</td>\n",
       "      <td>198.0</td>\n",
       "      <td>Snow Park</td>\n",
       "      <td>37.807813</td>\n",
       "      <td>-122.264496</td>\n",
       "      <td>12120</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.816667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>7.141293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1611</td>\n",
       "      <td>2020-01-31 23:49:10.931</td>\n",
       "      <td>2020-02-01 00:16:02.4310</td>\n",
       "      <td>174.0</td>\n",
       "      <td>Shattuck Ave at 51st St</td>\n",
       "      <td>37.836801</td>\n",
       "      <td>-122.264004</td>\n",
       "      <td>259.0</td>\n",
       "      <td>Addison St at Fourth St</td>\n",
       "      <td>37.866249</td>\n",
       "      <td>-122.299371</td>\n",
       "      <td>838</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.850000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>4.509856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1133</td>\n",
       "      <td>2020-01-31 23:56:49.475</td>\n",
       "      <td>2020-02-01 00:15:42.9960</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>13068</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.883333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>3.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1119</td>\n",
       "      <td>2020-01-31 23:57:02.284</td>\n",
       "      <td>2020-02-01 00:15:42.0630</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>12801</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.650000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>3.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1110</td>\n",
       "      <td>2020-01-31 23:57:09.019</td>\n",
       "      <td>2020-02-01 00:15:39.4330</td>\n",
       "      <td>127.0</td>\n",
       "      <td>Valencia St at 21st St</td>\n",
       "      <td>37.756708</td>\n",
       "      <td>-122.421025</td>\n",
       "      <td>324.0</td>\n",
       "      <td>Union Square (Powell St at Post St)</td>\n",
       "      <td>37.788300</td>\n",
       "      <td>-122.408531</td>\n",
       "      <td>9916</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>3.678177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1257</td>\n",
       "      <td>2020-01-31 23:51:43.435</td>\n",
       "      <td>2020-02-01 00:12:40.5780</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Telegraph Ave at Ashby Ave</td>\n",
       "      <td>37.855956</td>\n",
       "      <td>-122.259795</td>\n",
       "      <td>248.0</td>\n",
       "      <td>Telegraph Ave at Ashby Ave</td>\n",
       "      <td>37.855956</td>\n",
       "      <td>-122.259795</td>\n",
       "      <td>12914</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.950000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1247</td>\n",
       "      <td>2020-01-31 23:50:06.665</td>\n",
       "      <td>2020-02-01 00:10:53.7830</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Clay St at Battery St</td>\n",
       "      <td>37.795001</td>\n",
       "      <td>-122.399970</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>9672</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.783333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>5.066992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>803</td>\n",
       "      <td>2020-01-31 23:56:51.379</td>\n",
       "      <td>2020-02-01 00:10:15.3390</td>\n",
       "      <td>241.0</td>\n",
       "      <td>Ashby BART Station</td>\n",
       "      <td>37.852477</td>\n",
       "      <td>-122.270213</td>\n",
       "      <td>252.0</td>\n",
       "      <td>Channing Way at Shattuck Ave</td>\n",
       "      <td>37.865847</td>\n",
       "      <td>-122.267443</td>\n",
       "      <td>3582</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.383333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.505489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1147</td>\n",
       "      <td>2020-01-31 23:46:05.956</td>\n",
       "      <td>2020-02-01 00:05:13.3540</td>\n",
       "      <td>482.0</td>\n",
       "      <td>Brannan St at 7th St</td>\n",
       "      <td>37.772658</td>\n",
       "      <td>-122.403681</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Commercial St at Montgomery St</td>\n",
       "      <td>37.794231</td>\n",
       "      <td>-122.402923</td>\n",
       "      <td>12711</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.116667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.398241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>777</td>\n",
       "      <td>2020-01-31 23:47:40.692</td>\n",
       "      <td>2020-02-01 00:00:38.5070</td>\n",
       "      <td>368.0</td>\n",
       "      <td>Myrtle St at Polk St</td>\n",
       "      <td>37.785434</td>\n",
       "      <td>-122.419622</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Folsom St at 19th St</td>\n",
       "      <td>37.760594</td>\n",
       "      <td>-122.414817</td>\n",
       "      <td>2104</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.950000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.792447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>435</td>\n",
       "      <td>2020-01-31 23:51:06.263</td>\n",
       "      <td>2020-01-31 23:58:22.2370</td>\n",
       "      <td>220.0</td>\n",
       "      <td>San Pablo Ave at MLK Jr Way</td>\n",
       "      <td>37.811351</td>\n",
       "      <td>-122.273422</td>\n",
       "      <td>230.0</td>\n",
       "      <td>14th St at Mandela Pkwy</td>\n",
       "      <td>37.810743</td>\n",
       "      <td>-122.291415</td>\n",
       "      <td>3645</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.581156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>535</td>\n",
       "      <td>2020-01-31 23:47:01.549</td>\n",
       "      <td>2020-01-31 23:55:57.5240</td>\n",
       "      <td>340.0</td>\n",
       "      <td>Harmon St at Adeline St</td>\n",
       "      <td>37.849735</td>\n",
       "      <td>-122.270582</td>\n",
       "      <td>269.0</td>\n",
       "      <td>Telegraph Ave at Carleton St</td>\n",
       "      <td>37.862320</td>\n",
       "      <td>-122.258801</td>\n",
       "      <td>11447</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.916667</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.739036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>377</td>\n",
       "      <td>2020-01-31 23:48:30.312</td>\n",
       "      <td>2020-01-31 23:54:48.1000</td>\n",
       "      <td>134.0</td>\n",
       "      <td>Valencia St at 24th St</td>\n",
       "      <td>37.752428</td>\n",
       "      <td>-122.420628</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Mission Dolores Park</td>\n",
       "      <td>37.761420</td>\n",
       "      <td>-122.426435</td>\n",
       "      <td>9963</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.283333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.122036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>873</td>\n",
       "      <td>2020-01-31 23:40:11.939</td>\n",
       "      <td>2020-01-31 23:54:45.6910</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Laguna St at Hayes St</td>\n",
       "      <td>37.776435</td>\n",
       "      <td>-122.426244</td>\n",
       "      <td>321.0</td>\n",
       "      <td>Folsom St at 5th St</td>\n",
       "      <td>37.780645</td>\n",
       "      <td>-122.402866</td>\n",
       "      <td>2911</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.550000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.105967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>554</td>\n",
       "      <td>2020-01-31 23:43:48.432</td>\n",
       "      <td>2020-01-31 23:53:03.2580</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>459.0</td>\n",
       "      <td>Utah St at 24th St</td>\n",
       "      <td>37.753123</td>\n",
       "      <td>-122.405393</td>\n",
       "      <td>10133</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.233333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.388645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>525</td>\n",
       "      <td>2020-01-31 23:43:50.111</td>\n",
       "      <td>2020-01-31 23:52:35.6630</td>\n",
       "      <td>133.0</td>\n",
       "      <td>Valencia St at 22nd St</td>\n",
       "      <td>37.755213</td>\n",
       "      <td>-122.420975</td>\n",
       "      <td>459.0</td>\n",
       "      <td>Utah St at 24th St</td>\n",
       "      <td>37.753123</td>\n",
       "      <td>-122.405393</td>\n",
       "      <td>239</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>1.388645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>180</td>\n",
       "      <td>2020-01-31 23:49:07.194</td>\n",
       "      <td>2020-01-31 23:52:07.8770</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Haste St at Telegraph Ave</td>\n",
       "      <td>37.866043</td>\n",
       "      <td>-122.258804</td>\n",
       "      <td>252.0</td>\n",
       "      <td>Channing Way at Shattuck Ave</td>\n",
       "      <td>37.865847</td>\n",
       "      <td>-122.267443</td>\n",
       "      <td>12276</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>0.758167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>761</td>\n",
       "      <td>2020-01-31 23:39:05.307</td>\n",
       "      <td>2020-01-31 23:51:46.5050</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>351.0</td>\n",
       "      <td>10th St at University Ave</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.293400</td>\n",
       "      <td>3050</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.683333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.244729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>749</td>\n",
       "      <td>2020-01-31 23:39:11.393</td>\n",
       "      <td>2020-01-31 23:51:41.2990</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>351.0</td>\n",
       "      <td>10th St at University Ave</td>\n",
       "      <td>37.869060</td>\n",
       "      <td>-122.293400</td>\n",
       "      <td>3256</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.483333</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.244729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>624</td>\n",
       "      <td>2020-01-31 23:38:52.841</td>\n",
       "      <td>2020-01-31 23:49:17.8300</td>\n",
       "      <td>244.0</td>\n",
       "      <td>Shattuck Ave at Hearst Ave</td>\n",
       "      <td>37.873676</td>\n",
       "      <td>-122.268487</td>\n",
       "      <td>257.0</td>\n",
       "      <td>Fifth St at Delaware St</td>\n",
       "      <td>37.870407</td>\n",
       "      <td>-122.299676</td>\n",
       "      <td>1947</td>\n",
       "      <td>Customer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>0131</td>\n",
       "      <td>2.759899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176769</th>\n",
       "      <td>1090</td>\n",
       "      <td>2020-03-04 19:05:51.000</td>\n",
       "      <td>2020-03-04 19:24:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809134</td>\n",
       "      <td>-122.415192</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784234</td>\n",
       "      <td>-122.388862</td>\n",
       "      <td>557816</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>18.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>3.605894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176770</th>\n",
       "      <td>1183</td>\n",
       "      <td>2020-03-01 12:26:56.000</td>\n",
       "      <td>2020-03-01 12:46:40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809138</td>\n",
       "      <td>-122.415689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806195</td>\n",
       "      <td>-122.405260</td>\n",
       "      <td>866334</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>19.716667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0301</td>\n",
       "      <td>0.972302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176771</th>\n",
       "      <td>1025</td>\n",
       "      <td>2020-03-04 19:06:07.000</td>\n",
       "      <td>2020-03-04 19:23:13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809140</td>\n",
       "      <td>-122.415247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.784262</td>\n",
       "      <td>-122.388924</td>\n",
       "      <td>886830</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>17.083333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>3.603477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176772</th>\n",
       "      <td>443</td>\n",
       "      <td>2020-03-02 22:07:56.000</td>\n",
       "      <td>2020-03-02 22:15:20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809149</td>\n",
       "      <td>-122.415776</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.796818</td>\n",
       "      <td>-122.405370</td>\n",
       "      <td>240222</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>7.383333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0302</td>\n",
       "      <td>1.646967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176773</th>\n",
       "      <td>720</td>\n",
       "      <td>2020-03-21 19:18:05.000</td>\n",
       "      <td>2020-03-21 19:30:06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809152</td>\n",
       "      <td>-122.418323</td>\n",
       "      <td>478.0</td>\n",
       "      <td>Greenwich St at Franklin St</td>\n",
       "      <td>37.800254</td>\n",
       "      <td>-122.425919</td>\n",
       "      <td>438673</td>\n",
       "      <td>Customer</td>\n",
       "      <td>clipper</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0321</td>\n",
       "      <td>1.192720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176774</th>\n",
       "      <td>317</td>\n",
       "      <td>2020-03-16 16:02:59.000</td>\n",
       "      <td>2020-03-16 16:08:17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809153</td>\n",
       "      <td>-122.415734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806566</td>\n",
       "      <td>-122.420714</td>\n",
       "      <td>721149</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.283333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0316</td>\n",
       "      <td>0.523272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176775</th>\n",
       "      <td>787</td>\n",
       "      <td>2020-03-21 19:16:56.000</td>\n",
       "      <td>2020-03-21 19:30:03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809165</td>\n",
       "      <td>-122.418325</td>\n",
       "      <td>478.0</td>\n",
       "      <td>Greenwich St at Franklin St</td>\n",
       "      <td>37.800254</td>\n",
       "      <td>-122.425919</td>\n",
       "      <td>585226</td>\n",
       "      <td>Customer</td>\n",
       "      <td>clipper</td>\n",
       "      <td>13.116667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>21</td>\n",
       "      <td>0321</td>\n",
       "      <td>1.193788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176776</th>\n",
       "      <td>1663</td>\n",
       "      <td>2020-03-04 14:55:02.000</td>\n",
       "      <td>2020-03-04 15:22:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809172</td>\n",
       "      <td>-122.415235</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.766544</td>\n",
       "      <td>-122.398083</td>\n",
       "      <td>451085</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>27.716667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0304</td>\n",
       "      <td>4.970790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176777</th>\n",
       "      <td>1018</td>\n",
       "      <td>2020-03-01 12:29:54.000</td>\n",
       "      <td>2020-03-01 12:46:53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809176</td>\n",
       "      <td>-122.415754</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.806158</td>\n",
       "      <td>-122.405295</td>\n",
       "      <td>572370</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>16.966667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0301</td>\n",
       "      <td>0.977588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176778</th>\n",
       "      <td>877</td>\n",
       "      <td>2020-03-05 10:17:10.000</td>\n",
       "      <td>2020-03-05 10:31:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809177</td>\n",
       "      <td>-122.415263</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.808295</td>\n",
       "      <td>-122.415003</td>\n",
       "      <td>454861</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>14.616667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0305</td>\n",
       "      <td>0.100614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176779</th>\n",
       "      <td>492</td>\n",
       "      <td>2020-03-08 10:13:01.000</td>\n",
       "      <td>2020-03-08 10:21:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809197</td>\n",
       "      <td>-122.412139</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>517726</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0308</td>\n",
       "      <td>2.097861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176780</th>\n",
       "      <td>1100</td>\n",
       "      <td>2020-03-13 13:15:28.000</td>\n",
       "      <td>2020-03-13 13:33:48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809579</td>\n",
       "      <td>-122.410255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.805522</td>\n",
       "      <td>-122.422260</td>\n",
       "      <td>331159</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0313</td>\n",
       "      <td>1.146360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176781</th>\n",
       "      <td>1115</td>\n",
       "      <td>2020-03-02 20:29:04.000</td>\n",
       "      <td>2020-03-02 20:47:39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809675</td>\n",
       "      <td>-122.409954</td>\n",
       "      <td>81.0</td>\n",
       "      <td>Berry St at 4th St</td>\n",
       "      <td>37.775880</td>\n",
       "      <td>-122.393170</td>\n",
       "      <td>888753</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>18.583333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0302</td>\n",
       "      <td>4.034363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176782</th>\n",
       "      <td>476</td>\n",
       "      <td>2020-03-08 16:17:59.000</td>\n",
       "      <td>2020-03-08 16:25:56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809718</td>\n",
       "      <td>-122.409923</td>\n",
       "      <td>487.0</td>\n",
       "      <td>Powell St at Columbus Ave</td>\n",
       "      <td>37.800496</td>\n",
       "      <td>-122.410887</td>\n",
       "      <td>420959</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>7.933333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0308</td>\n",
       "      <td>1.028281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176783</th>\n",
       "      <td>5650</td>\n",
       "      <td>2020-03-22 17:09:35.000</td>\n",
       "      <td>2020-03-22 18:43:46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.809927</td>\n",
       "      <td>-122.476421</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Pier 1/2 at The Embarcadero</td>\n",
       "      <td>37.796389</td>\n",
       "      <td>-122.394586</td>\n",
       "      <td>539344</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>94.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0322</td>\n",
       "      <td>7.341157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176784</th>\n",
       "      <td>1429</td>\n",
       "      <td>2020-03-28 16:52:56.000</td>\n",
       "      <td>2020-03-28 17:16:45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810162</td>\n",
       "      <td>-122.412339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.787604</td>\n",
       "      <td>-122.393953</td>\n",
       "      <td>473795</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>23.816667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0328</td>\n",
       "      <td>2.981671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176785</th>\n",
       "      <td>1416</td>\n",
       "      <td>2020-03-28 16:53:19.000</td>\n",
       "      <td>2020-03-28 17:16:55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810173</td>\n",
       "      <td>-122.412357</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1st St at Folsom St</td>\n",
       "      <td>37.787290</td>\n",
       "      <td>-122.394380</td>\n",
       "      <td>229168</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>23.600000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>0328</td>\n",
       "      <td>2.993020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176786</th>\n",
       "      <td>804</td>\n",
       "      <td>2020-03-17 15:25:02.000</td>\n",
       "      <td>2020-03-17 15:38:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810488</td>\n",
       "      <td>-122.476507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.804016</td>\n",
       "      <td>-122.448654</td>\n",
       "      <td>942930</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "      <td>2.548967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176787</th>\n",
       "      <td>2547</td>\n",
       "      <td>2020-03-17 15:23:09.000</td>\n",
       "      <td>2020-03-17 16:05:36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810513</td>\n",
       "      <td>-122.476495</td>\n",
       "      <td>512.0</td>\n",
       "      <td>Divisadero St at Clay St</td>\n",
       "      <td>37.789688</td>\n",
       "      <td>-122.440688</td>\n",
       "      <td>401842</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>42.450000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "      <td>3.903874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176788</th>\n",
       "      <td>131</td>\n",
       "      <td>2020-03-17 15:16:10.000</td>\n",
       "      <td>2020-03-17 15:18:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810528</td>\n",
       "      <td>-122.476450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.810528</td>\n",
       "      <td>-122.476488</td>\n",
       "      <td>942930</td>\n",
       "      <td>Customer</td>\n",
       "      <td>app</td>\n",
       "      <td>2.183333</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>0317</td>\n",
       "      <td>0.003278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176789</th>\n",
       "      <td>192</td>\n",
       "      <td>2020-03-23 16:17:02.000</td>\n",
       "      <td>2020-03-23 16:20:15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.840358</td>\n",
       "      <td>-122.267302</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848314</td>\n",
       "      <td>-122.268623</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "      <td>0.891642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176790</th>\n",
       "      <td>63</td>\n",
       "      <td>2020-03-23 11:31:18.000</td>\n",
       "      <td>2020-03-23 11:32:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848441</td>\n",
       "      <td>-122.265775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848441</td>\n",
       "      <td>-122.265775</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176791</th>\n",
       "      <td>310</td>\n",
       "      <td>2020-03-23 16:11:17.000</td>\n",
       "      <td>2020-03-23 16:16:27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848457</td>\n",
       "      <td>-122.266072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.840393</td>\n",
       "      <td>-122.267316</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>clipper</td>\n",
       "      <td>5.166667</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "      <td>0.902776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176792</th>\n",
       "      <td>78</td>\n",
       "      <td>2020-04-01 13:27:39.000</td>\n",
       "      <td>2020-04-01 13:28:58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848478</td>\n",
       "      <td>-122.266082</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266023</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "      <td>0.005612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176793</th>\n",
       "      <td>165</td>\n",
       "      <td>2020-04-01 12:53:13.000</td>\n",
       "      <td>2020-04-01 12:55:58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848479</td>\n",
       "      <td>-122.266063</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848602</td>\n",
       "      <td>-122.265745</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "      <td>0.031052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176794</th>\n",
       "      <td>61</td>\n",
       "      <td>2020-04-01 10:42:35.000</td>\n",
       "      <td>2020-04-01 10:43:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848493</td>\n",
       "      <td>-122.265797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848476</td>\n",
       "      <td>-122.265779</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.016667</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "      <td>0.002467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176795</th>\n",
       "      <td>78</td>\n",
       "      <td>2020-03-18 17:10:42.000</td>\n",
       "      <td>2020-03-18 17:12:01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848499</td>\n",
       "      <td>-122.265794</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848477</td>\n",
       "      <td>-122.265815</td>\n",
       "      <td>999960</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>0318</td>\n",
       "      <td>0.003127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176796</th>\n",
       "      <td>122</td>\n",
       "      <td>2020-04-01 13:32:28.000</td>\n",
       "      <td>2020-04-01 13:34:31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848507</td>\n",
       "      <td>-122.266010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848485</td>\n",
       "      <td>-122.266063</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>2.033333</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "      <td>0.005211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176797</th>\n",
       "      <td>340</td>\n",
       "      <td>2020-04-01 13:07:34.000</td>\n",
       "      <td>2020-04-01 13:13:14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848530</td>\n",
       "      <td>-122.266013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848497</td>\n",
       "      <td>-122.266029</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0401</td>\n",
       "      <td>0.003949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176798</th>\n",
       "      <td>333</td>\n",
       "      <td>2020-03-23 16:39:57.000</td>\n",
       "      <td>2020-03-23 16:45:30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848625</td>\n",
       "      <td>-122.266660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.848446</td>\n",
       "      <td>-122.266022</td>\n",
       "      <td>633095</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>app</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>0323</td>\n",
       "      <td>0.059446</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>905006 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        duration_sec              start_time                  end_time  \\\n",
       "0              83118 2020-01-31 15:23:47.733  2020-02-01 14:29:06.2630   \n",
       "1              68239 2020-01-31 15:40:31.616  2020-02-01 10:37:51.0000   \n",
       "2              55407 2020-01-31 17:48:16.635  2020-02-01 09:11:44.3170   \n",
       "3              54929 2020-01-31 17:53:03.413  2020-02-01 09:08:32.6340   \n",
       "4              55700 2020-01-31 17:12:33.460  2020-02-01 08:40:53.6460   \n",
       "5              11333 2020-01-31 22:48:22.639  2020-02-01 01:57:15.7160   \n",
       "6              11341 2020-01-31 22:48:12.989  2020-02-01 01:57:14.1650   \n",
       "7               4038 2020-01-31 23:32:03.907  2020-02-01 00:39:22.0210   \n",
       "8               4059 2020-01-31 23:31:01.161  2020-02-01 00:38:40.8570   \n",
       "9               1980 2020-01-31 23:49:09.230  2020-02-01 00:22:09.7540   \n",
       "10              1969 2020-01-31 23:49:03.972  2020-02-01 00:21:53.5730   \n",
       "11              1611 2020-01-31 23:49:10.931  2020-02-01 00:16:02.4310   \n",
       "12              1133 2020-01-31 23:56:49.475  2020-02-01 00:15:42.9960   \n",
       "13              1119 2020-01-31 23:57:02.284  2020-02-01 00:15:42.0630   \n",
       "14              1110 2020-01-31 23:57:09.019  2020-02-01 00:15:39.4330   \n",
       "15              1257 2020-01-31 23:51:43.435  2020-02-01 00:12:40.5780   \n",
       "16              1247 2020-01-31 23:50:06.665  2020-02-01 00:10:53.7830   \n",
       "17               803 2020-01-31 23:56:51.379  2020-02-01 00:10:15.3390   \n",
       "18              1147 2020-01-31 23:46:05.956  2020-02-01 00:05:13.3540   \n",
       "19               777 2020-01-31 23:47:40.692  2020-02-01 00:00:38.5070   \n",
       "20               435 2020-01-31 23:51:06.263  2020-01-31 23:58:22.2370   \n",
       "21               535 2020-01-31 23:47:01.549  2020-01-31 23:55:57.5240   \n",
       "22               377 2020-01-31 23:48:30.312  2020-01-31 23:54:48.1000   \n",
       "23               873 2020-01-31 23:40:11.939  2020-01-31 23:54:45.6910   \n",
       "24               554 2020-01-31 23:43:48.432  2020-01-31 23:53:03.2580   \n",
       "25               525 2020-01-31 23:43:50.111  2020-01-31 23:52:35.6630   \n",
       "26               180 2020-01-31 23:49:07.194  2020-01-31 23:52:07.8770   \n",
       "27               761 2020-01-31 23:39:05.307  2020-01-31 23:51:46.5050   \n",
       "28               749 2020-01-31 23:39:11.393  2020-01-31 23:51:41.2990   \n",
       "29               624 2020-01-31 23:38:52.841  2020-01-31 23:49:17.8300   \n",
       "...              ...                     ...                       ...   \n",
       "176769          1090 2020-03-04 19:05:51.000       2020-03-04 19:24:01   \n",
       "176770          1183 2020-03-01 12:26:56.000       2020-03-01 12:46:40   \n",
       "176771          1025 2020-03-04 19:06:07.000       2020-03-04 19:23:13   \n",
       "176772           443 2020-03-02 22:07:56.000       2020-03-02 22:15:20   \n",
       "176773           720 2020-03-21 19:18:05.000       2020-03-21 19:30:06   \n",
       "176774           317 2020-03-16 16:02:59.000       2020-03-16 16:08:17   \n",
       "176775           787 2020-03-21 19:16:56.000       2020-03-21 19:30:03   \n",
       "176776          1663 2020-03-04 14:55:02.000       2020-03-04 15:22:46   \n",
       "176777          1018 2020-03-01 12:29:54.000       2020-03-01 12:46:53   \n",
       "176778           877 2020-03-05 10:17:10.000       2020-03-05 10:31:48   \n",
       "176779           492 2020-03-08 10:13:01.000       2020-03-08 10:21:14   \n",
       "176780          1100 2020-03-13 13:15:28.000       2020-03-13 13:33:48   \n",
       "176781          1115 2020-03-02 20:29:04.000       2020-03-02 20:47:39   \n",
       "176782           476 2020-03-08 16:17:59.000       2020-03-08 16:25:56   \n",
       "176783          5650 2020-03-22 17:09:35.000       2020-03-22 18:43:46   \n",
       "176784          1429 2020-03-28 16:52:56.000       2020-03-28 17:16:45   \n",
       "176785          1416 2020-03-28 16:53:19.000       2020-03-28 17:16:55   \n",
       "176786           804 2020-03-17 15:25:02.000       2020-03-17 15:38:27   \n",
       "176787          2547 2020-03-17 15:23:09.000       2020-03-17 16:05:36   \n",
       "176788           131 2020-03-17 15:16:10.000       2020-03-17 15:18:21   \n",
       "176789           192 2020-03-23 16:17:02.000       2020-03-23 16:20:15   \n",
       "176790            63 2020-03-23 11:31:18.000       2020-03-23 11:32:21   \n",
       "176791           310 2020-03-23 16:11:17.000       2020-03-23 16:16:27   \n",
       "176792            78 2020-04-01 13:27:39.000       2020-04-01 13:28:58   \n",
       "176793           165 2020-04-01 12:53:13.000       2020-04-01 12:55:58   \n",
       "176794            61 2020-04-01 10:42:35.000       2020-04-01 10:43:37   \n",
       "176795            78 2020-03-18 17:10:42.000       2020-03-18 17:12:01   \n",
       "176796           122 2020-04-01 13:32:28.000       2020-04-01 13:34:31   \n",
       "176797           340 2020-04-01 13:07:34.000       2020-04-01 13:13:14   \n",
       "176798           333 2020-03-23 16:39:57.000       2020-03-23 16:45:30   \n",
       "\n",
       "        start_station_id             start_station_name  \\\n",
       "0                  400.0  Buchanan St at North Point St   \n",
       "1                   99.0           Folsom St at 15th St   \n",
       "2                  197.0    El Embarcadero at Grand Ave   \n",
       "3                  197.0    El Embarcadero at Grand Ave   \n",
       "4                   12.0    Pier 1/2 at The Embarcadero   \n",
       "5                   60.0           8th St at Ringold St   \n",
       "6                   60.0           8th St at Ringold St   \n",
       "7                  450.0       Funston Ave at Irving St   \n",
       "8                  450.0       Funston Ave at Irving St   \n",
       "9                  238.0   MLK Jr Way at University Ave   \n",
       "10                 238.0   MLK Jr Way at University Ave   \n",
       "11                 174.0        Shattuck Ave at 51st St   \n",
       "12                 127.0         Valencia St at 21st St   \n",
       "13                 127.0         Valencia St at 21st St   \n",
       "14                 127.0         Valencia St at 21st St   \n",
       "15                 248.0     Telegraph Ave at Ashby Ave   \n",
       "16                  14.0          Clay St at Battery St   \n",
       "17                 241.0             Ashby BART Station   \n",
       "18                 482.0           Brannan St at 7th St   \n",
       "19                 368.0           Myrtle St at Polk St   \n",
       "20                 220.0    San Pablo Ave at MLK Jr Way   \n",
       "21                 340.0        Harmon St at Adeline St   \n",
       "22                 134.0         Valencia St at 24th St   \n",
       "23                  74.0          Laguna St at Hayes St   \n",
       "24                 133.0         Valencia St at 22nd St   \n",
       "25                 133.0         Valencia St at 22nd St   \n",
       "26                 240.0      Haste St at Telegraph Ave   \n",
       "27                 244.0     Shattuck Ave at Hearst Ave   \n",
       "28                 244.0     Shattuck Ave at Hearst Ave   \n",
       "29                 244.0     Shattuck Ave at Hearst Ave   \n",
       "...                  ...                            ...   \n",
       "176769               NaN                            NaN   \n",
       "176770               NaN                            NaN   \n",
       "176771               NaN                            NaN   \n",
       "176772               NaN                            NaN   \n",
       "176773               NaN                            NaN   \n",
       "176774               NaN                            NaN   \n",
       "176775               NaN                            NaN   \n",
       "176776               NaN                            NaN   \n",
       "176777               NaN                            NaN   \n",
       "176778               NaN                            NaN   \n",
       "176779               NaN                            NaN   \n",
       "176780               NaN                            NaN   \n",
       "176781               NaN                            NaN   \n",
       "176782               NaN                            NaN   \n",
       "176783               NaN                            NaN   \n",
       "176784               NaN                            NaN   \n",
       "176785               NaN                            NaN   \n",
       "176786               NaN                            NaN   \n",
       "176787               NaN                            NaN   \n",
       "176788               NaN                            NaN   \n",
       "176789               NaN                            NaN   \n",
       "176790               NaN                            NaN   \n",
       "176791               NaN                            NaN   \n",
       "176792               NaN                            NaN   \n",
       "176793               NaN                            NaN   \n",
       "176794               NaN                            NaN   \n",
       "176795               NaN                            NaN   \n",
       "176796               NaN                            NaN   \n",
       "176797               NaN                            NaN   \n",
       "176798               NaN                            NaN   \n",
       "\n",
       "        start_station_latitude  start_station_longitude  end_station_id  \\\n",
       "0                    37.804272              -122.433537           400.0   \n",
       "1                    37.767037              -122.415443            99.0   \n",
       "2                    37.808848              -122.249680           197.0   \n",
       "3                    37.808848              -122.249680           197.0   \n",
       "4                    37.796389              -122.394586           371.0   \n",
       "5                    37.774520              -122.409449            75.0   \n",
       "6                    37.774520              -122.409449            75.0   \n",
       "7                    37.763934              -122.470651           450.0   \n",
       "8                    37.763934              -122.470651           450.0   \n",
       "9                    37.871719              -122.273068           198.0   \n",
       "10                   37.871719              -122.273068           198.0   \n",
       "11                   37.836801              -122.264004           259.0   \n",
       "12                   37.756708              -122.421025           324.0   \n",
       "13                   37.756708              -122.421025           324.0   \n",
       "14                   37.756708              -122.421025           324.0   \n",
       "15                   37.855956              -122.259795           248.0   \n",
       "16                   37.795001              -122.399970           134.0   \n",
       "17                   37.852477              -122.270213           252.0   \n",
       "18                   37.772658              -122.403681            13.0   \n",
       "19                   37.785434              -122.419622           123.0   \n",
       "20                   37.811351              -122.273422           230.0   \n",
       "21                   37.849735              -122.270582           269.0   \n",
       "22                   37.752428              -122.420628           120.0   \n",
       "23                   37.776435              -122.426244           321.0   \n",
       "24                   37.755213              -122.420975           459.0   \n",
       "25                   37.755213              -122.420975           459.0   \n",
       "26                   37.866043              -122.258804           252.0   \n",
       "27                   37.873676              -122.268487           351.0   \n",
       "28                   37.873676              -122.268487           351.0   \n",
       "29                   37.873676              -122.268487           257.0   \n",
       "...                        ...                      ...             ...   \n",
       "176769               37.809134              -122.415192             NaN   \n",
       "176770               37.809138              -122.415689             NaN   \n",
       "176771               37.809140              -122.415247             NaN   \n",
       "176772               37.809149              -122.415776             NaN   \n",
       "176773               37.809152              -122.418323           478.0   \n",
       "176774               37.809153              -122.415734             NaN   \n",
       "176775               37.809165              -122.418325           478.0   \n",
       "176776               37.809172              -122.415235             NaN   \n",
       "176777               37.809176              -122.415754             NaN   \n",
       "176778               37.809177              -122.415263             NaN   \n",
       "176779               37.809197              -122.412139            12.0   \n",
       "176780               37.809579              -122.410255             NaN   \n",
       "176781               37.809675              -122.409954            81.0   \n",
       "176782               37.809718              -122.409923           487.0   \n",
       "176783               37.809927              -122.476421            12.0   \n",
       "176784               37.810162              -122.412339             NaN   \n",
       "176785               37.810173              -122.412357            26.0   \n",
       "176786               37.810488              -122.476507             NaN   \n",
       "176787               37.810513              -122.476495           512.0   \n",
       "176788               37.810528              -122.476450             NaN   \n",
       "176789               37.840358              -122.267302             NaN   \n",
       "176790               37.848441              -122.265775             NaN   \n",
       "176791               37.848457              -122.266072             NaN   \n",
       "176792               37.848478              -122.266082             NaN   \n",
       "176793               37.848479              -122.266063             NaN   \n",
       "176794               37.848493              -122.265797             NaN   \n",
       "176795               37.848499              -122.265794             NaN   \n",
       "176796               37.848507              -122.266010             NaN   \n",
       "176797               37.848530              -122.266013             NaN   \n",
       "176798               37.848625              -122.266660             NaN   \n",
       "\n",
       "                           end_station_name  end_station_latitude  \\\n",
       "0             Buchanan St at North Point St             37.804272   \n",
       "1                      Folsom St at 15th St             37.767037   \n",
       "2               El Embarcadero at Grand Ave             37.808848   \n",
       "3               El Embarcadero at Grand Ave             37.808848   \n",
       "4                Lombard St at Columbus Ave             37.802746   \n",
       "5                  Market St at Franklin St             37.773793   \n",
       "6                  Market St at Franklin St             37.773793   \n",
       "7                  Funston Ave at Irving St             37.763934   \n",
       "8                  Funston Ave at Irving St             37.763934   \n",
       "9                                 Snow Park             37.807813   \n",
       "10                                Snow Park             37.807813   \n",
       "11                  Addison St at Fourth St             37.866249   \n",
       "12      Union Square (Powell St at Post St)             37.788300   \n",
       "13      Union Square (Powell St at Post St)             37.788300   \n",
       "14      Union Square (Powell St at Post St)             37.788300   \n",
       "15               Telegraph Ave at Ashby Ave             37.855956   \n",
       "16                   Valencia St at 24th St             37.752428   \n",
       "17             Channing Way at Shattuck Ave             37.865847   \n",
       "18           Commercial St at Montgomery St             37.794231   \n",
       "19                     Folsom St at 19th St             37.760594   \n",
       "20                  14th St at Mandela Pkwy             37.810743   \n",
       "21             Telegraph Ave at Carleton St             37.862320   \n",
       "22                     Mission Dolores Park             37.761420   \n",
       "23                      Folsom St at 5th St             37.780645   \n",
       "24                       Utah St at 24th St             37.753123   \n",
       "25                       Utah St at 24th St             37.753123   \n",
       "26             Channing Way at Shattuck Ave             37.865847   \n",
       "27                10th St at University Ave             37.869060   \n",
       "28                10th St at University Ave             37.869060   \n",
       "29                  Fifth St at Delaware St             37.870407   \n",
       "...                                     ...                   ...   \n",
       "176769                                  NaN             37.784234   \n",
       "176770                                  NaN             37.806195   \n",
       "176771                                  NaN             37.784262   \n",
       "176772                                  NaN             37.796818   \n",
       "176773          Greenwich St at Franklin St             37.800254   \n",
       "176774                                  NaN             37.806566   \n",
       "176775          Greenwich St at Franklin St             37.800254   \n",
       "176776                                  NaN             37.766544   \n",
       "176777                                  NaN             37.806158   \n",
       "176778                                  NaN             37.808295   \n",
       "176779          Pier 1/2 at The Embarcadero             37.796389   \n",
       "176780                                  NaN             37.805522   \n",
       "176781                   Berry St at 4th St             37.775880   \n",
       "176782            Powell St at Columbus Ave             37.800496   \n",
       "176783          Pier 1/2 at The Embarcadero             37.796389   \n",
       "176784                                  NaN             37.787604   \n",
       "176785                  1st St at Folsom St             37.787290   \n",
       "176786                                  NaN             37.804016   \n",
       "176787             Divisadero St at Clay St             37.789688   \n",
       "176788                                  NaN             37.810528   \n",
       "176789                                  NaN             37.848314   \n",
       "176790                                  NaN             37.848441   \n",
       "176791                                  NaN             37.840393   \n",
       "176792                                  NaN             37.848497   \n",
       "176793                                  NaN             37.848602   \n",
       "176794                                  NaN             37.848476   \n",
       "176795                                  NaN             37.848477   \n",
       "176796                                  NaN             37.848485   \n",
       "176797                                  NaN             37.848497   \n",
       "176798                                  NaN             37.848446   \n",
       "\n",
       "        end_station_longitude  bike_id   user_type rental_access_method  \\\n",
       "0                 -122.433537    13052    Customer                  NaN   \n",
       "1                 -122.415443    12235  Subscriber                  NaN   \n",
       "2                 -122.249680    12822    Customer                  NaN   \n",
       "3                 -122.249680    11705    Customer                  NaN   \n",
       "4                 -122.413579     3673    Customer                  NaN   \n",
       "5                 -122.421239    12137    Customer                  NaN   \n",
       "6                 -122.421239    12321    Customer                  NaN   \n",
       "7                 -122.470651     1589    Customer                  NaN   \n",
       "8                 -122.470651    12884    Customer                  NaN   \n",
       "9                 -122.264496    10464    Customer                  NaN   \n",
       "10                -122.264496    12120    Customer                  NaN   \n",
       "11                -122.299371      838  Subscriber                  NaN   \n",
       "12                -122.408531    13068    Customer                  NaN   \n",
       "13                -122.408531    12801    Customer                  NaN   \n",
       "14                -122.408531     9916    Customer                  NaN   \n",
       "15                -122.259795    12914    Customer                  NaN   \n",
       "16                -122.420628     9672    Customer                  NaN   \n",
       "17                -122.267443     3582    Customer                  NaN   \n",
       "18                -122.402923    12711    Customer                  NaN   \n",
       "19                -122.414817     2104    Customer                  NaN   \n",
       "20                -122.291415     3645    Customer                  NaN   \n",
       "21                -122.258801    11447    Customer                  NaN   \n",
       "22                -122.426435     9963    Customer                  NaN   \n",
       "23                -122.402866     2911    Customer                  NaN   \n",
       "24                -122.405393    10133    Customer                  NaN   \n",
       "25                -122.405393      239    Customer                  NaN   \n",
       "26                -122.267443    12276    Customer                  NaN   \n",
       "27                -122.293400     3050    Customer                  NaN   \n",
       "28                -122.293400     3256    Customer                  NaN   \n",
       "29                -122.299676     1947    Customer                  NaN   \n",
       "...                       ...      ...         ...                  ...   \n",
       "176769            -122.388862   557816    Customer                  app   \n",
       "176770            -122.405260   866334    Customer                  app   \n",
       "176771            -122.388924   886830  Subscriber                  app   \n",
       "176772            -122.405370   240222  Subscriber                  app   \n",
       "176773            -122.425919   438673    Customer              clipper   \n",
       "176774            -122.420714   721149  Subscriber                  app   \n",
       "176775            -122.425919   585226    Customer              clipper   \n",
       "176776            -122.398083   451085  Subscriber                  app   \n",
       "176777            -122.405295   572370    Customer                  app   \n",
       "176778            -122.415003   454861  Subscriber                  app   \n",
       "176779            -122.394586   517726    Customer                  app   \n",
       "176780            -122.422260   331159    Customer                  app   \n",
       "176781            -122.393170   888753  Subscriber                  app   \n",
       "176782            -122.410887   420959    Customer                  app   \n",
       "176783            -122.394586   539344  Subscriber                  app   \n",
       "176784            -122.393953   473795    Customer                  app   \n",
       "176785            -122.394380   229168  Subscriber                  app   \n",
       "176786            -122.448654   942930    Customer                  app   \n",
       "176787            -122.440688   401842    Customer                  app   \n",
       "176788            -122.476488   942930    Customer                  app   \n",
       "176789            -122.268623   633095  Subscriber                  app   \n",
       "176790            -122.265775   999960  Subscriber                  app   \n",
       "176791            -122.267316   633095  Subscriber              clipper   \n",
       "176792            -122.266023   633095  Subscriber                  app   \n",
       "176793            -122.265745   633095  Subscriber                  app   \n",
       "176794            -122.265779   999960  Subscriber                  app   \n",
       "176795            -122.265815   999960  Subscriber                  app   \n",
       "176796            -122.266063   633095  Subscriber                  app   \n",
       "176797            -122.266029   633095  Subscriber                  app   \n",
       "176798            -122.266022   633095  Subscriber                  app   \n",
       "\n",
       "        duration_min  start_year  start_month  start_day timing  distance  \n",
       "0        1385.300000        2020            1         31   0131  0.000000  \n",
       "1        1137.316667        2020            1         31   0131  0.000000  \n",
       "2         923.450000        2020            1         31   0131  0.000000  \n",
       "3         915.483333        2020            1         31   0131  0.000000  \n",
       "4         928.333333        2020            1         31   0131  1.811157  \n",
       "5         188.883333        2020            1         31   0131  1.038713  \n",
       "6         189.016667        2020            1         31   0131  1.038713  \n",
       "7          67.300000        2020            1         31   0131  0.000000  \n",
       "8          67.650000        2020            1         31   0131  0.000000  \n",
       "9          33.000000        2020            1         31   0131  7.141293  \n",
       "10         32.816667        2020            1         31   0131  7.141293  \n",
       "11         26.850000        2020            1         31   0131  4.509856  \n",
       "12         18.883333        2020            1         31   0131  3.678177  \n",
       "13         18.650000        2020            1         31   0131  3.678177  \n",
       "14         18.500000        2020            1         31   0131  3.678177  \n",
       "15         20.950000        2020            1         31   0131  0.000000  \n",
       "16         20.783333        2020            1         31   0131  5.066992  \n",
       "17         13.383333        2020            1         31   0131  1.505489  \n",
       "18         19.116667        2020            1         31   0131  2.398241  \n",
       "19         12.950000        2020            1         31   0131  2.792447  \n",
       "20          7.250000        2020            1         31   0131  1.581156  \n",
       "21          8.916667        2020            1         31   0131  1.739036  \n",
       "22          6.283333        2020            1         31   0131  1.122036  \n",
       "23         14.550000        2020            1         31   0131  2.105967  \n",
       "24          9.233333        2020            1         31   0131  1.388645  \n",
       "25          8.750000        2020            1         31   0131  1.388645  \n",
       "26          3.000000        2020            1         31   0131  0.758167  \n",
       "27         12.683333        2020            1         31   0131  2.244729  \n",
       "28         12.483333        2020            1         31   0131  2.244729  \n",
       "29         10.400000        2020            1         31   0131  2.759899  \n",
       "...              ...         ...          ...        ...    ...       ...  \n",
       "176769     18.166667        2020            3          4   0304  3.605894  \n",
       "176770     19.716667        2020            3          1   0301  0.972302  \n",
       "176771     17.083333        2020            3          4   0304  3.603477  \n",
       "176772      7.383333        2020            3          2   0302  1.646967  \n",
       "176773     12.000000        2020            3         21   0321  1.192720  \n",
       "176774      5.283333        2020            3         16   0316  0.523272  \n",
       "176775     13.116667        2020            3         21   0321  1.193788  \n",
       "176776     27.716667        2020            3          4   0304  4.970790  \n",
       "176777     16.966667        2020            3          1   0301  0.977588  \n",
       "176778     14.616667        2020            3          5   0305  0.100614  \n",
       "176779      8.200000        2020            3          8   0308  2.097861  \n",
       "176780     18.333333        2020            3         13   0313  1.146360  \n",
       "176781     18.583333        2020            3          2   0302  4.034363  \n",
       "176782      7.933333        2020            3          8   0308  1.028281  \n",
       "176783     94.166667        2020            3         22   0322  7.341157  \n",
       "176784     23.816667        2020            3         28   0328  2.981671  \n",
       "176785     23.600000        2020            3         28   0328  2.993020  \n",
       "176786     13.400000        2020            3         17   0317  2.548967  \n",
       "176787     42.450000        2020            3         17   0317  3.903874  \n",
       "176788      2.183333        2020            3         17   0317  0.003278  \n",
       "176789      3.200000        2020            3         23   0323  0.891642  \n",
       "176790      1.050000        2020            3         23   0323  0.000000  \n",
       "176791      5.166667        2020            3         23   0323  0.902776  \n",
       "176792      1.300000        2020            4          1   0401  0.005612  \n",
       "176793      2.750000        2020            4          1   0401  0.031052  \n",
       "176794      1.016667        2020            4          1   0401  0.002467  \n",
       "176795      1.300000        2020            3         18   0318  0.003127  \n",
       "176796      2.033333        2020            4          1   0401  0.005211  \n",
       "176797      5.666667        2020            4          1   0401  0.003949  \n",
       "176798      5.550000        2020            3         23   0323  0.059446  \n",
       "\n",
       "[905006 rows x 20 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge[df_merge.distance != 12791.691538]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "duration_sec                        int64\n",
       "start_time                 datetime64[ns]\n",
       "end_time                           object\n",
       "start_station_id                  float64\n",
       "start_station_name                 object\n",
       "start_station_latitude            float64\n",
       "start_station_longitude           float64\n",
       "end_station_id                    float64\n",
       "end_station_name                   object\n",
       "end_station_latitude              float64\n",
       "end_station_longitude             float64\n",
       "bike_id                             int64\n",
       "user_type                          object\n",
       "rental_access_method               object\n",
       "duration_min                      float64\n",
       "start_year                          int64\n",
       "start_month                         int64\n",
       "start_day                           int64\n",
       "timing                             object\n",
       "distance                          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UdVKhHnr/28cOAAAP3lfXSQUaNwAAnHBIXSdpxjEErI3GCwATrZPWMtWDK2HZbYMVL7ut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\nxhAgSZIkVcYQIEmSJFXm/wGqV+ShR9fsNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a435f65d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [12,12]) # make the whole figure bigger to show all \n",
    "plt.scatter(data = df_merge , x = 'start_month' , y = 'distance', )\n",
    "plt.title('The Trip Distance Trend over Each Month ')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Trip Distance(km)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?\n",
    "\n",
    "> After looking at the distribution of ride duration, I wanted to see how the ride duration changed over the course of the first few months of this year and whether or not COVID have any impact on it. I found that one datapoint was throwing everything off. It was so much longer that it had to be an error (someone forgot to check the bike back in). After taking that off, it looked like the ride duration stayed pretty consistent throughout. By looking at the duration each month, I was able to see that ridership had gone down signifigantly. \n",
    "\n",
    "### Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?\n",
    "\n",
    "> Well, I realizes that many riders might return their bikes to the same station that they got them from (essentially going 0 km). Also, it looks like there was an uptick in bikes midway through january, which is puzzling because typically people try to adopt healthy habits in the first of the year, but maybe these people were getting their exercise at a gym instead. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a4d30dd90>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwOdbVl0EbEPpKRAR21COFrPlSyGBh4GD6qjzNDqSUu9ZdfTXQy+nHDm3+xw8k9ILnjUy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\nCcNAkoRhIEnCMJAkYRhIkoD/B8qDUNz9t3hUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10a8a5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df_merge , x=\"start_station_longitude\", y=\"start_station_latitude\", alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a23cdbed0>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lwOdbVl0EbEPpKRAR21COFrPlSyGBh4GD6qjzNDqSUu9ZdfTXQy+nHDm3+xw8k9ILnjUy\nc6Q5CKpl91F6+ztsnVr13DCwthEEAJm5EfgOPfr7nI09g6l4JfDTMdbdERHPohwtXwqcOpPHzsew\npHr8cfPCzNwYEQ8Ci6tFfwxs26bcbyPi1qZyM15EPAVYDnw1M3/VpshLI+I+YDvgJ8DHM/Oz01nH\nHjiw2p/zgRuAD7fMF7T9HADrq8fFwOreVrG3ImIh8Ke0nz/qh32+BFjZZvl6yue9drOuZzBZEfEO\nYA/grJZVtwAnUo4u96McHR8HrJrWCtZjYfV4b5t19zatH6/cpqb1s8HBwNMp48mtvk05kWAYOJTS\nxf5MRJw8fdWr3VeAY4B9gTcCv6XMhR3eVGas/bupZf1s9gnKpGrrpHC/7POFjP33uaAXG9yqPYOI\neDVlEnMiazJz7ylsZ2/gHyljqBc3r8vM1q70VRFxJ3BeRLw6M6+e7HbHqU+v2t34f6Lb/Y9F8yZR\nrudq+Lc4kjKHcEXrisw8pWXRyoi4HDgpIs7LzPu7rW+dJtP2zDym5T0uB9ZSDnIan+Xx9u9WN9V9\nHhEfAA4D3tY8jAIzf593aVr/Prf2MNF3Kac/TqTd+H9HIuJllKP8a4C3dfiySyhHHC+jnM5Vt161\ne7wjv2c0rR+v3AK2DCdMh0n/W0TE9sCrgU90Mfl/CaU38SLg2k4r2SNT/hxk5uaIuAw4OyK2z8y7\nefz+vbupeGN/b2Lrmso+P4pyZtzJmfnPHW5vJu3zTjX35JstoH2PYcq2ahhUk7xjjeFPWUS8iHIa\n3jrgkEmcQdGTI6setrvxJb6Epg99ROxEGT+9uVp0K/AQW8aWG+W2pZzBcFkP6tbWFP8tDqeMm7cb\nIhrLjDlqrvFz0Nqm5s9Bcxg05oJuZiuabLsj4gjgfOCjmXlGFy+dMfu8C+tp+fusLKZH+69v5wwi\nYhdKV/S/gQO6nAx+Y/V4Xe0V66HqYpQb2VL/hsOBR4CvV+UeBv4diOo0xIZDgScze+ZL3gTclJnr\nunjNYcBvgB/1pkrTq9p/y4Hbm04bvhYYof3nYBPljJRZJSJeR5ks/kxmvq/Ll8/Gfb4K2CMiFjUW\nVAd1r6BHf5/zRkdnU1hCROwO7EQJsi9QjmKzWn1FZj5YnRX0PcrE4hGUC5Ka3ZCZD1XvdwPwr8B/\nUY4c9qFM0F2dmfv3tjWd66TdVbnXAl+lXHV8CeXq4rMoQynHN73fUsqXxtcp113sBJwDfDMze3K2\nQp0i4s+A64H3ZuYTzrmurj04EfgS5Xz0P6DMLwwDJ2bm2dNX23pExBsopxVeAdwBPJtyZe6ewBsy\n89KmskdRjqLPpAx1LqNcWHhMZra7zmbGiohXAt+gHBEfDTSfZv1QZt5QleubfV5dWHYjJcROpnw3\nnQ78PvDiXsx9bO05g8k4mrKDG5az5VSrxmX3i9lyyf5X27xHoxyUEDga2J4y5HArcBrlNg0zSSft\nJjOviIhDKRdgvZlyO4ozKRch/U5mrquuVj6bclXvfZRQ/GDPWlCvI4FHgYvHWH83JThPAwYpPaOb\ngMMy85JpqWH9NlKuQj+HMp78IOVK8/0y88rmgpn5qYgYpdyO4njgduDozDx/eqtci2WUHutLeWKv\n5n8oBzLQR/s8Mx+IiGWU21FcRBnq+ibldhQ9mQSfdT0DSVL9+nbOQJLUOcNAkmQYSJIMA0kShoEk\nCcNAkoRhIEnCMJAkYRhIkoD/B8qDUNz9t3hUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10aadbd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df_merge , x=\"start_station_longitude\", y=\"start_station_latitude\", alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like many of these trips have the same start and end station. Let's try to eliminate those. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_filtered = df_merge[df_merge['start_station_latitude'] == df_merge['end_station_latitude'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_filtered = df_merge[df_merge['start_station_longitude'] == df_merge['end_station_longitude'] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a450ee8d0>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a4d33bd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df_filtered , x=\"start_station_longitude\", y=\"start_station_latitude\", alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a23c3ca50>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a45100d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df_filtered , x=\"start_station_longitude\", y=\"start_station_latitude\", alpha=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_merge[df_merge.distance.isnull()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, so it looks like we have eliminated the trips that just went only to the same station. These are more 1 way trips. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate Exploration\n",
    "\n",
    "> Create plots of three or more variables to investigate your data even\n",
    "further. Make sure that your investigations are justified, and follow from\n",
    "your work in the previous sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JMB8vDdSpe2/inQjz1LXvGNf4lJoUCiFU1UCmb29bPmP2fh2d+GKYuJCusi1T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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a23cc7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sb.FacetGrid(data =  df_merge, hue = 'user_type', aspect=4)\n",
    "g.map(plt.scatter, 'start_month','duration_min', alpha = .3)\n",
    "g.add_legend()\n",
    "plt.title('Trip Duration(min) Against Month and User Type')\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Trip Duration(min)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'sqrt'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-95-f6eba17f2a46>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_filtered\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'user_type'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"user_type\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"duration_min\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"jet\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mcolorbar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m )\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m   3355\u001b[0m                          \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3356\u001b[0m                          \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3357\u001b[0;31m                          edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m   3358\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3359\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1708\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1709\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1710\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m   4094\u001b[0m                 \u001b[0moffsets\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moffsets\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4095\u001b[0m                 \u001b[0mtransOffset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'transform'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4096\u001b[0;31m                 \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4097\u001b[0m                 )\n\u001b[1;32m   4098\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmtransforms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIdentityTransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/collections.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, paths, sizes, **kwargs)\u001b[0m\n\u001b[1;32m    861\u001b[0m         \u001b[0mCollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_paths\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sizes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    864\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/grantglass/anaconda2/lib/python2.7/site-packages/matplotlib/collections.pyc\u001b[0m in \u001b[0;36mset_sizes\u001b[0;34m(self, sizes, dpi)\u001b[0m\n\u001b[1;32m    834\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m             \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sizes\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdpi\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_factor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transforms\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'sqrt'"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a10e34bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data = df_filtered, x=\"start_station_longitude\", y=\"start_station_latitude\",\n",
    "    s=df_filtered['user_type'], label=\"user_type\",\n",
    "    c=\"duration_min\", cmap=plt.get_cmap(\"jet\"),\n",
    "    colorbar=True, alpha=0.4, figsize=(10,7),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?\n",
    "\n",
    "> Your answer here!\n",
    "\n",
    "### Were there any interesting or surprising interactions between features?\n",
    "\n",
    "> Your answer here!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> At the end of your report, make sure that you export the notebook as an\n",
    "html file from the `File > Download as... > HTML` menu. Make sure you keep\n",
    "track of where the exported file goes, so you can put it in the same folder\n",
    "as this notebook for project submission. Also, make sure you remove all of\n",
    "the quote-formatted guide notes like this one before you finish your report!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}