Data Speeds.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pandas.read_csv(\"march18_myspeed.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f795c7ffba10fa3300fc0a02b4b68ad7a66f8939"
   },
   "source": [
    "Setting 'na' values for Signal Strength to None. Some of the values for Signal Strength are 'na' which means not available since the device was not able to capture signal level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "a7a3a44f105638b165ea84c299dea755327d3a6c"
   },
   "outputs": [],
   "source": [
    "# set na to None\n",
    "df.loc[df['Signal_strength'] == 'na', 'Signal_strength'] = None\n",
    "df.loc[df.isnull()['Signal_strength']]\n",
    "\n",
    "# convert 'Signal_strength' to float\n",
    "df['Signal_strength'] = pandas.to_numeric(df.loc[:,'Signal_strength'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "d65839fb7208cb2ed650ef1329b5a018cb153739"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Service Provider Technology Test_type  Data Speed(Mbps)  Signal_strength  \\\n",
      "0              JIO         4G    Upload              7.30            -63.0   \n",
      "1              JIO         4G    Upload              7.31            -65.0   \n",
      "2              JIO         4G    Upload              6.91            -65.0   \n",
      "3         VODAFONE         4G  Download              7.73            -90.0   \n",
      "4         VODAFONE         4G  Download              6.92            -90.0   \n",
      "\n",
      "              LSA  \n",
      "0  Andhra Pradesh  \n",
      "1  Andhra Pradesh  \n",
      "2  Andhra Pradesh  \n",
      "3     Maharashtra  \n",
      "4     Maharashtra  \n"
     ]
    }
   ],
   "source": [
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "269a942071b177a68ca6d0ceca8075319c774896"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1322972 entries, 0 to 1322971\n",
      "Data columns (total 6 columns):\n",
      "Service Provider    1322972 non-null object\n",
      "Technology          1322972 non-null object\n",
      "Test_type           1322972 non-null object\n",
      "Data Speed(Mbps)    1322972 non-null float64\n",
      "Signal_strength     1220981 non-null float64\n",
      "LSA                 1322972 non-null object\n",
      "dtypes: float64(2), object(4)\n",
      "memory usage: 60.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "c6209f005a37e178d1d71651eb0f8fc7db0299c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       Data Speed(Mbps)  Signal_strength\n",
      "count      1.322972e+06     1.220981e+06\n",
      "mean       8.468365e+00    -8.922951e+01\n",
      "std        1.482456e+01     1.475934e+01\n",
      "min        0.000000e+00    -1.340000e+02\n",
      "25%        9.000000e-01    -1.010000e+02\n",
      "50%        3.170000e+00    -9.100000e+01\n",
      "75%        8.440000e+00    -7.700000e+01\n",
      "max        1.376500e+02    -5.500000e+01\n"
     ]
    }
   ],
   "source": [
    "print(df.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "275a3ff5bd8703c49518faaa047c932b7c674f04"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Service Provider         0\n",
       "Technology               0\n",
       "Test_type                0\n",
       "Data Speed(Mbps)         0\n",
       "Signal_strength     101991\n",
       "LSA                      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "e36960563edf96d33dbae321ed8c1fe472c86573"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Technology\n",
      "3G     136888\n",
      "4G    1186084\n",
      "Name: Technology, dtype: int64\n",
      "1322972\n"
     ]
    },
    {
     "data": {
      "image/png": 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tatUGykIryg+YmV0CbA0hLMssbqK+XLU9F6+pBD9lfjSEMAj4HD+FbUxb6vejgEvxywJd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef2607470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test_type\n",
      "Upload      661457\n",
      "Download    661515\n",
      "Name: Test_type, dtype: int64\n",
      "1322972\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef274a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Service Provider\n",
      "DOLPHIN         30\n",
      "AIRCEL          60\n",
      "UNINOR          82\n",
      "CELLONE      11800\n",
      "VODAFONE    115708\n",
      "Name: Service Provider, dtype: int64\n",
      "1322972\n"
     ]
    },
    {
     "data": {
      "image/png": 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zs53AUqBn8sEowt3nmdl8IAf4Q2kXOeWUMB22axtF9wuelEuXlbVr3XQvvFC4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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee7c45cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LSA\n",
      "Chennai              8591\n",
      "North East          12804\n",
      "Himachal Pradesh    17755\n",
      "Assam               22387\n",
      "Jammu & Kashmir     24575\n",
      "Name: LSA, dtype: int64\n",
      "1322972\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ll8Pw4fDwwzByZJiCrVMnhOu2bUXY0TKgoEeEXYC33f1z4Ecza5lw7kzgFqAxcApwvplV\nBiYDlwEXACfkuN/pQAfgLOBeM8ucXGgITHP3M939a2CYu6cBZwBtzeyMg7TzMiA94fhod2/r7o8Q\nRrPnuPuZwItA9Iqae4H3o/I3gDoAZtYIuAY4PxptZgDXAi2A2u7e1N2bAc8lPK+Cu58V/Tzuza2B\nZtYvCvfFGdu3HKQ7IlLSTJwYQm3AgHDsDjt3wp494XjmzDBtesMNcM01YUS3dGl4F5gZnFdfDVde\nCRdcEN4XfvddKG/YEJo3hxEjoH9/SE8P90tPh9WrYfr0ou5t6VbQQdidEB5E28RXt39z9/Xuvg9Y\nDqQSgu4rd//C3R3I+T/fbHffFb2H2wjUisq/dvePE+pdbWZLgWVAE0LY5uYhM1sO9COMWjO9lLB/\nEjDXzNKB26L7AbTJbJ+7zwb+FZVfBLQCFkX3vogQ9F8Cp5jZ42bWEfh3wjNejbZLop/Dftx9krun\nuXta+ao18+iOiJREu3eHac46daBz51D29dfhXV/XruG4fv2wHTMGnnwSPvooLIA59tiw6CUpCZ57\nLrwnnD8/LLbJvAbCqHDuXBg8OIwmt28PdTduDIt05NAVWBCa2bHAhcAz0UKS24BrMqdAgV0J1TPI\nWqjjB7htXtf8Z+BvZvWA3wEXufsZwGygch73u83dW7h7e3dflVCeOJHwOPBENIrrn+NeubXVgP+N\n7tvC3Ru6+wh3/xfQHHgXuAl4Jpd+JfZJRMqIV1+FTZvCKtByefwpe9NNYTrzk0/C+77TT4eXXw71\nTzwR/vjHcI+bb4ZTT4U33oAKCX9aDB4Mo0eHwOzQAbp0gXvvhRYt4LrriqafZUVB/iF8FWG6sn9m\nQfTe7RcHuGYNUM/M6rv7WrKPIA/VUYQg2xK9z+tECJ/8qglsiPZ7JJQvJEx5jjSzTsAxUfl84E9m\nNs7dN5rZfwE1ojbtdvdZZrYWmHoEbRKRUqRbt/BJlJoapkczVa4MzzxDnq68MnzyMmdO1n6FCuFd\nouRPQU6Ndgdey1E2C/h1Xhe4+07CNOXsaLHM14f7UHdfQZgSXQ1MAT443HvkMAJ42czeAxK/GnEf\n0Caagv0V8E30/E+Bu4G/mNlK4P+AFKA28G40XToVuPMI2yUiIoXA3A80MyklQVJKA0/pMb64myEi\nB6F/j7BkMbMl0ULKA9KvWBMRkVhTEIqISKwpCEVEJNYUhCIiEmsKQhERiTV9mbsUaFa7Jou1Gk1E\npFBoRCgiIrGmIBQRkVhTEIqISKwpCEVEJNa0WKYUSN+whdShs4u7GSJlgn4NmuSkEaGIiMSaglBE\nRGJNQSgiIrGmIBQRkVhTEIqISKwpCEUkVqZOBbP9P+vW7V934kQ46SSoUgWuuAJ++CGUt2u3//Xt\n2oVzjzwCRx8NjRpBenoo27YNTjkFvvqq8Psnh09BKCKx0rYtzJgRPs8/D5UqQa1aULt29nrLlsEN\nN4RAu+8+mD0bBg8O54YPz7rHjTeGspYtYft2GDoUevQI4fngg+HcqFHQrRvUq1d0/ZRDF9vvEZpZ\nLWAccA7wL2A3MMbdXzvANW8Bv3b3nwrg+T2Bv7j7d0d6LxE5dPXqZQXSK6/A7t3QqxdUrJi93tSp\nYTtqFLRuDW++GYJv0iS48MKsetOnh23//rBrF+zdC+edB+vXw9atYaQ5YwasXFnYPZP8imUQmpkB\nrwP/6+6/jsrqApcf6Dp373yYzynv7hl5nO4JrAIUhCLFZOJEKFcO+vXb/1zmNGbmSPGkk0LIffst\nNGgQyr79FubMCcHYsGEo69IljP7Kl4eZM+G22+Dee6F69cLvj+RPXKdGLwR2u/uEzAJ3/9rdHzez\nnmb2RGa5mb1pZu2i/XVmdly0/7qZLTGz1WbWL6H+VjO738w+Ac41s+FmtsjMVpnZJAuuAtKAF8xs\nuZlVKaJ+i0hk7VqYPx86doTU1IPXdw9bs6yyyZNh374whZpp1ixYtCjc/7jj4Ouv4eKL4aKLoG5d\nGDasQLshBSCuQdgEWHqE9+jl7q0IgTbIzI6NyqsBq9z9bHd/H3jC3Vu7e1OgCnCpu78CLAaudfcW\n7r4j583NrJ+ZLTazxRnbtxxhU0Ukp4kTQ7gNGBCO3WHnTtizJxxnTp+uXx+2GzZAhQphZAhhdPjs\ns3DCCWEUmKlcOUhLg5NPDu8UH30UHn88BOi774ap1r//vUi6KIcorkGYjZk9aWYrzGzRYVw2yMxW\nAB8DJwPRZAkZwKyEer80s0/MLJ0wEm1yKDd390nunubuaeWr1jyMZonIwezeHd4B1qkDnaMXHl9/\nHRa4dO0ajq+/PmyHDYMxY+DDD8OUZ+XKofzPf4bvvoPevfd/vwgwZUpYaHPuuZCRAf/v/2W9d9y7\ntzB7J4crrkG4GmiZeeDuNwEXAcnAXrL/XCrnvDiaKr0YONfdmwPLEurtzHwvaGaVgaeAq9y9GTA5\nt/uJSNF69VXYtAn69g0juNy0agVPPgmffhpWiXbqBOPGZZ0/0PvFn38OI7/MVaMDB8Kxx4avVtx4\nIzQ5pL8OS1GJ5WIZ4B1glJkNcPeno7Kq0XYdcKOZlQNqA2flcn1N4F/uvt3MTiesPM1NZuhtNrPq\nwFXAK1HZz0CNI+uGiORHt27hkyg1Nes9YKYbb8z6ekROb7+d9/1r1IAvv8w6rlsXlizJV1OlCMQy\nCN3dzawLMM7Mbgc2AduAO4APgK+AdMKqzpzvEh14G7jBzFYCfydMj+b2nJ/MbHJ0r3VA4tTrVGCC\nme0gjCz3e08oIiKFzzznX4EkV2ZWHtgInODue4ry2UkpDTylx/iifKRImaV/jzA+zGyJu6cdrF5c\n3xHmx2rgmaIOQRERKVyxnBrND3c/vbjbICIiBU8jQhERiTUFoYiIxJqCUEREYk3vCEuBZrVrslgr\n3URECoVGhCIiEmsKQhERiTUFoYiIxJqCUEREYk1BKCIisaZVo6VA+oYtpA6dXdzNECnV9DtGJS8a\nEYqISKwpCEVEJNYUhCIiEmsKQhERiTUFoYjExtSpYLb/Z926/etOnAgnnQRVqsAVV8APP4Tyb76B\n88+HpKRw7SuvZF2zYAHUqQPJyfDii1nll18OM2YUZs/kSCgIRSQ22rYNgTRjBjz/PFSqBLVqQe3a\n2estWwY33ACNGsF998Hs2TB4cDi3axeccgq0abP//YcNC9e0bw+33hrK5s0LIdq9e+H2TfLviIPQ\nzNzMnk84rmBmm8zszcO8z7tmlhbtbz3SduVy/55Ru5ab2adm1vcI7zfVzK7Kx3UjzOx3R/JsEcmf\nevWgW7fwqVwZdu+GXr2gYsXs9aZODdtRo+D22+G880J47twJDRqEED3//P3vv21bCMKWLWHrVsjI\ngCFD4NFHC71rcgQK4nuE24CmZlbF3XcA7YENBXDfwvCSuw80s+OB1Wb2hrv/M/OkmVVw973F2D4R\nKSITJ0K5ctCv3/7nvvoqbDNHiiedBHv3wrffhiDMS69ecMstYf+WW2DCBGjVCtLSCrbtUrAKamp0\nDpD5bdXuwH9mw83sLDP70MyWRduGUXkVM3vRzFaa2UtAlcQbmtnvzWyFmX1sZrXMrIaZfWVmFaPz\nR5nZOjOraGZ9zWxRVH+WmVU9UGPdfSOwFqgbjdAmmdlfgGlmlmpm75nZ0uhzXvQ8M7MnotHkbOD4\nhLa2MrO/mtkSM5trZilR+aCo/kozS3hjQONoBPylmQ3K109cRPJt7VqYPx86doTU1IPXdw9bswPX\nu/lmWLMGli+H4cPh4Ydh5Ejo3Tu8O+zaNYwapWQpqCB8EehmZpWBM4BPEs6tAdq4+5nAcGBUVD4A\n2O7uZwC/B1olXFMN+NjdmwMLgb7u/jPwLlmB2w2Y5e57gFfdvXVU/zOg94Eaa2anAKcA/y8qagVc\n4e6/BjYC7d29JXAN8FhUpyvQEGgG9AUyA7Ii8Dhwlbu3AqZE/QEYCpwZ9fGGhCacDnQAzgLuzQz3\nHG3sZ2aLzWxxxvYtB+qOiBymiRNDuA0YEI7dw7Tnnj3huF69sF2/Pmw3bIAKFcLI8GAaNoTmzWHE\nCOjfH9LTYebMsF29GqZPL/DuyBEqkCB095VAKmE0+FaO0zWBl81sFTAOaBKVtwGmJ1y/MuGa3UDm\nO8Yl0b0BngF+E+3/Bngu2m8ajeLSgWsTnpHTNWa2nDBi7e/uP0blb0TTugAVgcnRvV4GGie0d4a7\nZ7j7d8A7UXlDoCnwf9G97wYy/3NZCbxgZtcBiVOus919l7tvJgRvrZwNdfdJ7p7m7mnlq9bMozsi\ncrh27w7vAOvUgc6dQ9nXX4fVoV27huPrrw/bYcNgzBj48MOs94pbt8Izz8DSpaHO/PnhONGaNTB3\nblhgk5EB27fD5MmwcWOYYpWSpSB/1+gbwMNAO+DYhPIHgAXu3tXMUgmjukyex732uGdORpCR2U53\n/yCaumwLlHf3VVGdqUAXd19hZj2jNuTmJXcfmEt54mTFYOCfQHPCXxR2HqS9Bqx293NzOXcJIUAv\nB+4xs8yA3pVQ5z/9E5HC9+qrsGkTPPBAeEeYm1at4Mkn4fe/h/feg06dYNy4cG7zZuibsNRuwoSw\n7dMnq2zwYBg9OnzFokMH6NIF7r0XWreG664rnH5J/hXkH8BTgC3unm5m7RLKa5K1eKZnQvlCwuht\ngZk1JUypHopphBHdAwllNYDvoynGazmyxTo1gfXuvs/MegDlE9rb38ymEd4P/hL4I/B3INnMznX3\nj6I2nEaYoj3Z3ReY2fvAr4HqR9AuESkAmatGE6WmZr0HzHTjjeGTU251c5ozJ2u/QgWYNSs/LZWi\nUmDfI3T39e6e2yLhMcBoM/uArFABeBqobmYrgduBvx3io14AjiFhQQ5wD+G95P8R3kkeiaeAHmb2\nMSHQMkeLrwFfAOlR2/8K4O67gauAP5jZCmA54f1heWB6NMW6DBjn7j8dYdtERKSAmR/srzYlTPTd\nvSvc/f8r7rYUlaSUBp7SY3xxN0OkVNM/wxQ/ZrbE3Q/65ZVS9W7KzB4HOgGdi7stIiJSNpSqIHT3\n3xZ3G0REpGzR7xoVEZFYUxCKiEisKQhFRCTWStU7wrhqVrsmi7XiTUSkUGhEKCIisaYgFBGRWFMQ\niohIrCkIRUQk1hSEIiISa1o1Wgqkb9hC6tDZxd0MkSKl3w0qRUUjQhERiTUFoYiIxJqCUEREYk1B\nKCIisaYgFJES66ef4Prr4eijoXp1aNNm/zqbNkGLFlCtGtSoAW3bwqpV2eusWQNJSWAGr7wSytLT\noVGjcO+xY7PqDhoEo0cXXp+k5FEQikiJ1asXvPAC9O4N48fDqafmXq9TJ3jqKRgwABYuhCFDss65\nQ9++UCHHGvnRo0N4Xn893HEH7NgBn30Gb7+d/Xop+8p8EJpZqpmtylE2wsx+F+1PNbOvzGy5mS01\ns3Nz1D3azH4wM4uOzzUzN7OTouOaZvajmR3Wz9LMWphZ5yPrnUjZ9eWX8Npr0L17CK3f/AamTNm/\nXnIyjBwJnTvDhReGsnIJ/zU+/TSsWwf9+2e/bts2SE2F886DvXth584QgA8+GEaPEh9lPggP0W3u\n3gIYCkxMPOHuPwH/ABpFRecBy6ItwDnAJ+6+7zCf2QJQEIrk4dNPw3bRojByq1YtjNxyk54Oxx8f\nRoa1a4fRI8CGDXDnnSEMjzoq+zU9esDrr4eg7dIFPvwwhOGVVxZen6RkUhBmtxDIbfLlA7KC7zxg\nXI7jDwHMrL6ZvW1mS8zsPTM7PSr/HzNbZWYrzGyhmVUC7geuiUai1xRmp0RKo127wnbbNnjpJTj/\nfBgzBubN27/uqafC3LnwwAPw3XehHsDQoZCWBqefDj/+GMr+8Q/YujUE3tq1IWhnzIDbb4dHH4Vh\nw6BuXbjoIvj++6LpqxQvBWF2lwHpuZR/SFbwnQK8DKRFx+cRghJgEvBbd28F/A54KiofDnRw9+bA\n5e6+Oyp7yd1buPtLOR9oZv3MbLGZLc7YvqUAuiZSuqSmhu0FF4TQuvrqcLx2bRi57d6dVbd6dfjV\nr+Duu+Hkk2HmzFD+7bfwzjvQoAE8/ngo++1vw0gQQuClpYUR4wUXQKVKMGpUeM8I8Nhjhd5NKQHi\n8CvW/BDKHzKzu4FNQO9c6n4ADDWzesA6d99pQXWgFfC3aP884OXodSJAUsL1U81sJvDqITXafRIh\nWElKaZBXH0TKrJYtoVkzmD+jeenkAAAgAElEQVQfJk+G556D8uXDyLBKFWjSJKwOfe45WL48rBxd\nuRK++QZatw73uO++sKoUQji+/DLcemv21aebN4fA+9vfYOPGUDZlSgjcli2Lts9SPOIQhD8Ax+Qo\n+y/gq4Tj29z9lbxu4O5fmNkxhBHjR1HxEuA3wFfuvtXMjgJ+it415rz+BjM7G7gEWG5m+9URkezM\nwpRlnz5hFFenDkybBk2bZq+XnAxvvQUTJoSR4aWXZn0dom3brHqZX6k455xwr0z33BO+MpGcHD43\n3giPPAKnnQYDBxZuH6VkMPeyP9gws8XAHe4+38z+C/gY6OTua81sKvDmgYIwusfrQDOgp7u/Z2bd\ngZHAW+7+26jOh8A4d385WmV6hruvMLP67r42qrOMEKD1CdOkPQ7W/qSUBp7SY3x+uy9SKumXbsuR\nMrMl7p52sHpxeUd4PXC3mS0H3gHuywymw/ABcDKwODr+iPC+8MOEOtcCvc1sBbAauCIqf8jM0qOv\ncSwEVgALgMZaLCMiUrxiMSIs7TQilDjSiFCOlEaEIiIih0BBKCIisaYgFBGRWFMQiohIrCkIRUQk\n1uLwhfpSr1ntmizWCjoRkUKhEaGIiMSaglBERGJNQSgiIrGmIBQRkVhTEIqISKxp1WgpkL5hC6lD\nZxd3M0QOSL8bVEorjQhFRCTWFIQiIhJrCkIREYk1BaGIiMSaglBEClRqKphlfVq0yL3et9/CFVdA\ntWpQsyZce23WuVGj4KSTwrlrroF//zuUL1gAdepAcjK8+GJW/csvhxkzCq1LUsZp1aiIFLg2bWDA\ngLB/zDH7n3eHrl3h00/h9tshJQU++yycmzULhg2DLl0gLQ3uvhtq1YLHHgvljRrBscfCrbdCt24w\nbx788AN07150/ZOypVSMCM3sWDNbHn3+YWYbEo4rHcF9zzazcdF+HzMbn0udPma2z8yaJJStMbOT\nDuM5F5vZ6/ltp0hpU68eXHJJCKoOHfY/v2ABLFkCQ4bA0KHQrx+Mj/7re/fdsP3d70LwnXACTJ0a\nyrZtC0HYsiVs3QoZGeEejz5aFL2SsqpUBKG7/+DuLdy9BTABGJd57O67j+C+n7j74EOouh64K7/P\nEYmbadPgqKPg+OPh2Wf3P//pp2E7axZUrRrqPvZYKDv++LB9911YtAg2b4affw6jvl69QujddlvY\nnzABWrUKI0eR/CoVQXggZvZnM1tiZqvNrE9UVsHMfjKzh8xsqZnNjUZ/fzWzL82sc1TvUEdqrwMt\nzezUXJ4/ycwWR88fnlB+iZn93czeB65IKB9pZrckHB/W6FKkpOvbF2bOhOefh0qVoH9/+Oqr7HV2\n7QrbihXhtdfCCPKWW+Dzz8OU6umnhynRs86CypVD3cqV4eabYc0aWL4chg+Hhx+GkSOhd+/w7rBr\n1zBqFDkcpT4IgR7u3gpoDQwxs8w3EjWBv7h7S2A3MAK4CPgf4P7DfMY+4CHgzlzODXX3NKA50N7M\nGptZVWAi0Bm4ADjxMJ8nUmoNGwZXXQXXXRcWumRkhIDbuRN2R/M3qalhe8klYcHMJZeE94ZffQXH\nHQcrVoTR4Oefw4knhpCrVi1c07AhNG8OI0aEkE1PD8Gbng6rV8P06cXRaynNysJimcFmdnm0fxJQ\nH1gO7HD3/4vK04Et7r7XzNKB1Hw853ngTjOrk6O8u5n1JvwsTwQaA1WBz919LYCZvQBcfzgPM7N+\nQD+A8kcl56O5IkUvPR3uugs6dYK9e8MUaZUq0KxZ2DZpAqtWQefOYQp01iw49VR45RWoXh3OPBO+\n+w6eeAJOOw3efjuEYea0aaY1a2Du3BCY8+bB9u0weTJs3BieK3I4SnUQmtnFQBvgHHffEU1DRhMp\nJL473AfsStg/7H67+55oYc3tCc9vANwMnOXuP5nZ9ITnex632kv2kXjl3Cq5+yRgEkBSSoO87iVS\nohx3XBgBDh8ewqlxY/j978OoLlGVKiH8brwRbropjPJefTWE4z/+EaZLv/wyrA69914YODD79YMH\nw+jRkJQUFuN06RLqtW4dRqIih6NUByFh+vPHKASbEKZHC9OzwKdAjej4KOBn4N9mlgJ0AN6O6pxm\nZvWAdUDiwu51QHsAMzsLOLmQ2yxSZFJS4K23cj/nOf46d8EFYQSZ0wknZH2VIi9z5mTtV6gQRpYi\n+VXa3xHOBqqa2QpgOPBJYT7M3XcBTwKZc5VLCaG3CpgMfBDV2w7cAMwB3gO+TLjNy0AtM1sG9M5x\nTkREiph5zr+mSYmTlNLAU3rs9xVHkRJF/wyTlDRmtiRazHhApX1EKCIickQUhCIiEmsKQhERiTUF\noYiIxJqCUEREYq20f48wFprVrslircgTESkUGhGKiEisKQhFRCTWFIQiIhJrCkIREYk1BaGIiMSa\nVo2WAukbtpA6dHZxN0NiTL9HVMoyjQhFRCTWFIQiIhJrCkIREYk1BaGIiMSaglBEDllqKphlfVq0\n2L/Opk2hvFo1qFED2raFVavCuV27oE8fSE6GKlXgzDPhnXfCuQULoE6dcO7FF7Pud/nlMGNGoXdN\nYkxBKCKHpU2bEEwzZsAf/pB7nU6d4KmnYMAAWLgQhgwJ5dOmwbPPhqB84AFYsQL69g3nhg2DRo2g\nfXu49dZQNm8e/PADdO9e+P2S+FIQHgIzyzCz5Wa22sxWmNkQMzvgz87MUs1sVbTf08yeyKPeW2Z2\ndGG0W6Qw1KsHl1wC3bpBhw77n09OhpEjoXNnuPDCUFYu+q9l376wbdoULr4YkpLg6Oj//du2hSBs\n2RK2boWMjBCgjz5a+H2SeFMQHpod7t7C3ZsA7YHOwL0FcWN37+zuPxXEvUSKwrRpcNRRcPzxYXSX\nm/T0cL5TJ6hdG8aPD+U9ekDXruH4zDOhalWYOjWc69UrhN5tt4X9CROgVStISyuSbkmMKQgPk7tv\nBPoBAy0ob2YPmdkiM1tpZv3zuPREM3vbzL4wszGZhWa2zsyOK5LGixyhvn1h5kx4/nmoVAn694ev\nvtq/3qmnwty5Yfrzu+9gTPT/+I8/htmz4dprw3vAjAzo2RPc4eabYc0aWL4chg+Hhx8OI8vevcO7\nw65dw6hRpKApCPPB3b8k/OyOB3oDW9y9NdAa6Gtm9XK5rAVwDdAMuMbMTi6q9ooUlGHD4Kqr4Lrr\n4JprQpB9/jns3Am7d2fVq14dfvUruPtuOPnkEJ4Qtrt3ww03hOvT0mDpUti8OZxv2BCaN4cRI0LI\npqeHa9LTYfVqmD69yLssMaBfsZZ/Fm1/BZxhZldFxzWBBsDnOerPd/ctAGb2KVAX+DbPm5v1I4w8\nKX9UcgE2WyR/0tPhrrvCdOfevWGKtEoVaNYsbJs0CatDn3sujOpatICVK+Gbb6B163CP+vXDdsyY\nsFDmo4/g2GPDJ9OaNWE0uWJFWCyzfTtMngwbN4bnihQ0BWE+mNkpQAawkRCIv3X3uTnqpOa4bFfC\nfgYH+dm7+yRgEkBSSgM/shaLHLnjjgsjwOHDQzg1bgy//z2ceGL2esnJ8NZb4R1f9epw6aUwdmw4\nd9NN8Nln8Oc/h5Br1ChMgZZLmJsaPBhGjw4LaTp0gC5d4N57Q5hed13R9VfiQ0F4mMwsGZgAPOHu\nbmZzgQFm9o677zGz04ANxdtKkYKXkhICLjee8Fe1Sy8Nn9xUrgzPPHPg58yZk7VfoQLMmnV47RQ5\nXArCQ1PFzJYDFYG9wPNA9HdcngFSgaVmZsAmoEtxNFJERA6fgvAQuHv5A5zbB9wVfRJtAZpGdaYC\nUxOuuTRhP7XgWioiIodLq0ZFRCTWFIQiIhJrCkIREYk1BaGIiMSaglBERGJNQSgiIrGmr0+UAs1q\n12Txg5cUdzNERMokjQhFRCTWFIQiIhJrCkIREYk1BaGIiMSaFsuUAukbtpA6dHZxN0NKmXVaYCVy\nSDQiFBGRWFMQiohIrCkIRUQk1hSEIiISawpCkTJu505o2BDMYODA/c9PnRrO5fysWwebNkGLFlCt\nGtSoAW3bwqpV4boFC6BOHUhOhhdfzLrf5ZfDjBlF0TORgqEgFCnj7r8f1q/P+3zbtiG4ZsyA55+H\nSpWgVi2oXTuc79QJnnoKBgyAhQthyJBQPmwYNGoE7dvDrbeGsnnz4IcfoHv3wu2TSEFSECYws60J\n+53N7Aszq1MA9001s1VHeh+Rw7VyJYwbByNG5F2nXj3o1i18KleG3buhVy+oWDGM9kaOhM6d4cIL\nQ/1y0Z8a27aFIGzZErZuhYyMEJKPPlro3RIpUPoeYS7M7CLgceBX7v7NIV5Twd33Fm7LRA7dvn3Q\npw/cdBO0bn1o10ycGIKuX7+ssvR0OPPMsF+7NowfH/Z79YJbbgn7t9wCEyZAq1aQllZwfRApChoR\n5mBmFwCTgUvcfW1Ulmxms8xsUfQ5PyofYWaTzOwvwLRo5PeemS2NPuflcv+D1hEpCM89F97zXX89\nbNgQyrZsCe/9crN2LcyfDx07QmpqVvmpp8LcufDAA/DddzBmTCi/+WZYswaWL4fhw+Hhh8PosXfv\n8O6wa9cwahQp6TQizC4J+BPQzt3XJJQ/Coxz9/ejqdK5QKPoXCvgF+6+w8yqAu3dfaeZNQBmADn/\nfrzxEOqIHLFvvw2h17x5Vtn06ZCUBE8+GY6TkrLOTZwI7uFdYKLq1eFXvwqfyZNh5kyYMiWca9gw\nbG++Gfr3D6PHmTPDO8nWrcPz+vcvvD6KFAQFYXZ7gA+B3sDNCeUXA43NLPP4KDOrEe2/4e47ov2K\nwBNm1gLIAE7L5RmHUgcz6wf0Ayh/VHK+OyTxdfXV0LRp2F+9Orwn7NgxBF3DhrB5c3i3B+G94NSp\nYSTXuXPWPZ57Loz4WrQI7xu/+Wb/adY1a8KIccWKsFhm+/YQmBs3wl69LJBSQEGY3T7gamCemd3l\n7qOi8nLAuQmBB0AUjImTP4OBfwLNo2t25vKMQ6mDu08CJgEkpTTwfPZHYqxx4/ABOO64sK1fP7zH\ny+nVV8Po8YEHshbDQFgs89Zb4f1f9epw6aUwdmz2awcPhtGjw+iyQwfo0gXuvTcE5nXXFU7fRAqS\ngjAHd99uZpcC75nZP939WeAvwEDgIQAza+Huy3O5vCaw3t33mVkPoHw+64gUqHbtwrRnpnXrsp/P\nXDWa06WXhs+BzJmTtV+hAsyald9WihQPLZbJhbv/CHQE7jazK4BBQJqZrTSzT4Eb8rj0KaCHmX1M\nmPLMbanAodQREZEiYu6adSvpklIaeEqP8cXdDCll9M8wSdyZ2RJ3P+hiRI0IRUQk1hSEIiISawpC\nERGJNQWhiIjEmoJQRERiTUEoIiKxpi/UlwLNatdksZbCi4gUCo0IRUQk1hSEIiISawpCERGJNQWh\niIjEmhbLlALpG7aQOnR2cTdDSgH9flGRw6cRoYiIxJqCUEREYk1BKCIisaYgFBGRWFMQipRRO3dC\nw4ZgBgMH5l7np5/g+uvh6KOhenVo0ybrXGpquDbz06JFKN+wAVq3hho1YMiQrPpjx0L//oXWHZFC\no1WjImXU/ffD+vUHrtOrF/zpT3DLLdCoEXz4YfbzbdrAgAFh/5hjwvbJJ+GHH0II3n9/OH/MMfDY\nY7BoUcH3Q6SwlYkRoZllmNlyM1tlZn82s6MP4ZoPD1Ynj+tuMbOqCcdb83MfkcK0ciWMGwcjRuRd\n58sv4bXXoHt3GD0afvMbmDIle5169eCSS6BbN+jQIZRt2wYnnAAXXRSOt26Fu++GQYMgOblQuiNS\nqMpEEAI73L2FuzcFfgRuOtgF7n5ePp91C1D1oLVEism+fdCnD9x0U5jCzMunn4btokVQrVr43HFH\n9jrTpsFRR8Hxx8Ozz4ay7t1h2TJo2xbS0sAdFi6E3/62cPojUtjKShAm+gioDWBm1c1svpktNbN0\nM7sis1LmSC6vOmZWzcxmm9mKaKR5jZkNAk4EFpjZgoR7/T6q97GZ1YrKLjOzT8xsmZnNSygfYWZT\nzOxdM/syuqdIgXnuOVi3Lrz727AhlG3ZAps2Za+3a1fYbtsGL70E558PY8bAvHmhvG9fmDkTnn8e\nKlUK7/+++grOOSdsP/4Y3n8fbrsNHnoIJk2CU06Bs8/OClmR0qBMvSM0s/LARUD0d1d2Al3d/d9m\ndhzwsZm94e6ecFmudYCOwHfufkl075ruvsXMhgC/dPfN0fXVgI/dfZiZjQH6AiOB94Fz3N3NrA9w\nO3BrdM3pwC+BGsDfzexpd99TKD8UiZ1vvw2h17x5Vtn06ZCUFN7vQdhPTQ37F1wAV14ZrnnnHVi7\nFi6+GIYNy7p+2bKwGObzz8N06QknhM9rr0HFitCuHdSsCQsWhOnVBx6AGTOKqsciR6asBGEVM1sO\npAJLgP+Lyg0YZWZtgH2EkWIt4B8J1+ZVJx142Mz+ALzp7u/l8ezdwJvR/hKgfbR/EvCSmaUAlYCv\nEq6Z7e67gF1mtjF6XrZlDWbWD+gHUP4ovXiRQ3f11dC0adhfvTq8J+zYMSxqadgQNm8O7/VatoRm\nzWD+fJg8OYwky5cPI8P0dLjrLujUCfbuDVOkVaqE+pl27YI77wxhuG9fmCKdORNWrID69Yul6yL5\nUlamRne4ewugLiF0Mt8RXgskA62i8/8EKue4Ntc67v450IoQiKPNbHgez96TMMLMIOsvF48DT7h7\nM6B/jufuSthPvOY/3H2Su6e5e1r5qjUP3HuRBI0bw1VXhU/btqGsfn1o1Sp7PbMwaqtfP7zf+/HH\nEHhNm8Jxx0FGBgwfDkOHQt26IfBOPDHr+vHjwwKaRo3CVylGjgzXb90aAlKktCgrI0IAoqnLQcCf\nzOxpoCaw0d33mNkvCUGZU651zOxE4Ed3nx69T+wZ1f+ZMKW5OZd75bxv9IaGHkfSL5H8atcujNQy\nrVuX/XyTJvDRR/tfl5ICb7114HvnXFhz550KQCmdylQQArj7MjNbAXQDXgD+bGaLgeXAmsSq0Tav\nOs2Ah8xsH7AHiL5NxSRgjpl97+6/PEBTRgAvm9kG4GOg3hF3TkRECpxlXzcSD2Z2LLDU3XMbIZY4\nSSkNPKXH+OJuhpQC+meYRLKY2RJ3TztYvbLyjvCQRVOeHwEPF3dbRESk+JW5qdGDcffvgNOKux0i\nIlIyxG5EKCIikkhBKCIisaYgFBGRWFMQiohIrMVusUxp1Kx2TRZrWbyISKHQiFBERGJNQSgiIrGm\nIBQRkVhTEIqISKxpsUwpkL5hC6lDZxd3M6SE0+8ZFckfjQhFRCTWFIQiIhJrCkIREYk1BaGIiMSa\nglBERGJNQShSBu3cCQ0bghkMHJh7nZ9+guuvh6OPhurVoU2brHNnnw01akDVqpCWBgsXhvL0dGjU\nKFwzdmxW/UGDYPTowuuPSGEq9UFoZlsT9jub2RdmVqeQn3mLmVU9hHrrzOy4wmyLSG7uvx/Wrz9w\nnV694IUXoHdvGD8eTj0169x558Fjj8E998Dy5dCnTygfPRqqVQsBescdsGMHfPYZvP02DBlSeP0R\nKUylPggzmdlFwONAR3f/5hCvye/3KG8BDhqEIsVh5UoYNw5GjMi7zpdfwmuvQffuIdx+8xuYMiXr\n/NixcNllcNFFkJQE5aI/KbZtg9TUEJR794aR55Ah8OCDoZ5IaVQmgtDMLgAmA5e4+9qo7DIz+8TM\nlpnZPDOrFZWPMLNJZvYXYJqZ9TSzV83s7Wg0OSbhvk+b2WIzW21m90Vlg4ATgQVmtiCvejnaVyW6\nf9/o+HUzWxLV71fIPx6JkX37wujtppugdeu86336adguWhRGeNWqhRFepi1bIDk5TJFWqgTPPBPK\ne/SA118PAdqlC3z4YQjDK68svD6JFLayEIRJwJ+ALu6+JqH8feAcdz8TeBG4PeFcK+AKd/91dNwC\nuAZoBlxjZidH5cPcPQ04A2hrZme4+2PAd8Av3f2XedVLeFZ14M/AH919clTWy91bAWnAIDM79kh/\nCCIAzz0H69aFqcsNG0LZli2waVP2ert2he22bfDSS3D++TBmDMybF8qrV4e//CVMj+7cCcOHh/Ir\nr4S1a0OAzpgBt98Ojz4Kw4ZB3bphBPn990XSVZECUxaCcA/wIdA7R/lJwFwzSwduA5oknHvD3Xck\nHM939y3uvhP4FKgblV9tZkuBZdH1jfNow4Hq/Ql4zt2nJZQNMrMVwMfAyUCDnDc0s37RKHNxxvYt\nefVdJJtvvw2h17w5XHddKJs+He68M4RfZgCmpobtBReEcLv66nC8dm3YVqgA7dvDb38LZ50FCxbA\n5s3hXN26YQHN00+H6ytVglGjshbUPPZYkXRVpMCUhd81ug+4GphnZne5+6io/HFgrLu/YWbtgBEJ\n12zLcY9dCfsZQAUzqwf8Dmjt7v8ys6lA5ZwPP4R6HwCdzOyP7u5RWy4GznX37Wb2bm73dfdJwCSA\npJQGftCfgggh0Jo2DfurV4f3hB07woABYRXp5s2wdSu0bAnNmsH8+TB5chhJli8fRoZz58LMmeE9\n4LffhunPWrXg2IR5i82bQ+D97W+wcWMomzIlBGnLlkXebZEjUhaCkChQLgXeM7N/uvuzQE0gmhyi\nRz5uexQhMLdE7xc7Ae9G534GagCbD1IPYDhwD/AUMCBq17+iNp8OnJOPtonkqnHj8AE4LlqvXL8+\ntGqVvZ5ZmNrs0yeM+urUgWnTQoju2AGffAJ//GNYAPOLX4RpU7Os6++5J3xlIjk5fG68ER55BE47\nLe+va4iUVGUiCAHc/Ucz6wgsNLPNhBHgy2a2gTAFWe8w77fCzJYBq4EvCSO7TJOAOWb2vbv/8gD1\nMt0CTIkW4twD3GBmK4G/R20TKXDt2oEnzCWsW5f9fJMm8NFH+1/XujWsWnXgez/9dPbjJ58MH5HS\nyNw161bSJaU08JQe44u7GVLC6Z9hEsnOzJZECxkPqCwslhEREck3BaGIiMSaglBERGJNQSgiIrGm\nIBQRkVhTEIqISKyVme8RlmXNatdksZbGi4gUCo0IRUQk1hSEIiISawpCERGJNQWhiIjEmhbLlALp\nG7aQOnR2cTdDSgD9PlGRgqcRoYiIxJqCUEREYk1BKCIisaYgFBGRWFMQiohIrCkIRUqhnTuhYUMw\ng4EDc69z9tlQowZUrQppabBwYSh3hzvvhBNPhMqV4fTT4aWXwrn0dGjUCI4+GsaOzbrXoEEwenTh\n9kmkuJT5IDSzrTmOe5rZE8XVHpGCcP/9sH79geucdx489hjccw8sXw59+oTyefPgwQchJQUeegg2\nbICePWHPnhB21arB9dfDHXfAjh3w2Wfw9tswZEihd0ukWJT5IDxSZqbvWkqJsnIljBsHI0YcuN7Y\nsXDZZXDRRZCUBOWi/9r37Qvb+vWhfXuoWTOMHMuVg23bIDU1hOjevWHkOWRICM6kpMLslUjxiXUQ\nmtllZvaJmS0zs3lmVisqH2H/P3v3Hu/VmPd//PVWdFAykiah7RBCNLVzCJXTYJiRuE0NP+XcmBxy\nmkwjGUYmIwmTKWIMMpIx6CaDcmYq0k4TTeROxijNdOuow+f3x/Xd9/7uXbt2tfeu3Xo/H4/vY63v\nta611nVth3fXta61k4ZLehF4WNLrktrknfempIMlHSrprdz5b0naL3e8p6SnJL0gaaakQXnnDpM0\nSdKHkm6q9k5bjbZ6dRrZ/exn0L79uusuXAhNmqQp0u22g/vvT+Xf/346f/ToNA369dfw2GNQqxb0\n6AFPPw3du0OXLvDWWykMu3at+r6ZbS5ZCMJ6kqYUf4Bf5R17Azg8Ir4HPA5cl3esHXBaRPwEuB/o\nCSBpX6BOREwFZgAdc+f3B27NO78N8GOgNfBjSbvnyvtFRCFwMNBJ0sGV213bmj34IMyenaYu585N\nZQsXwrx5a9Zt0ABefDFNjy5bBv37p/KPPoJHHkmB+NRT0LRpmhpdvDgF3qxZMHEijBoF110Hd90F\n/fpBixZpdPnPf1ZXb82qRxaCcGlEtCn+kAKr2G7AOElFwLXAgXnHnomIpbn90cCpkrYFzgceypU3\nAkZLmgbcWeb8lyNiYUQsA6YDLXLlZ0l6D3g/V/+AtTVa0sW5keOkVUsWblzPbaszZ04KvUMOgXPO\nSWWPPJIWvyxfnj7FatdOU5+XXQaHHgrjx8P8+fDMMyk8/9//g9NPh+OPT6E6fXo6r0WLtLhm2DA4\n+ug0mrz11pLFNkOHVm+fzapa1p9/3Q0MjohnJHUGBuQdW1y8ExFLJP0VOA04CyjMHboZGB8Rp0sq\nACbknZ/3vyRWAbUl7QlcA7SPiH9Legiou7aGRcRwYDhAnWYtY+O6Z1ubs86Cgw5K+x9+mJ4TnnQS\n/PSnaRXp/PmwaBGMGwdPPJGe9c2Zk6Y4mzaFxo3Ts0FIQbd0KTz3XAq7Pfcsuc/8+Snw/vY3+Oqr\nVDZyZBottm1brV02q3JZD8JGQG6CiR7rqXs/8CzwekQsWMv5PStwvx1IAbsw9zzyZEqHp9k6HXBA\n+gDsvHPa7r03tGtXut5OO8G776Znf3XqwFFHwaBB6XWLrl3TlOcjj6TR4l57wd13l1wP0krTyy9P\nzxibNIFLL4U77oB99y3/dQ2zmirrQTiANLU5F3gH2LO8ihExWdL/Ag/mFQ8C/iDpKuCV9d0sIj6Q\n9D7wIfAJ8OYmtN0yrnPn9E5gsdmzS/bbt4dp09Z+ngS/+U36lGfYsNLf7703fcy2RorwrFtFSNqV\nNHrbPyJWV+e96zRrGc16DKnOW9oWyn8Nk1nFSZqcW5y4TllYLLPJJJ0LvEta8VmtIWhmZlUr61Oj\nFRIRDwMPb+52mJlZ5fOI0MzMMs1BaGZmmeYgNDOzTHMQmplZpnmxTA3QunkjJnnZvJlZlfCI0MzM\nMs1BaGZmmeYgNDOzTHMQmplZpnmxTA1QNHchBX3Hbu5mWDn8+z/NajaPCM3MLNMchGZmlmkOQjMz\nyzQHoZmZZZqD0MzMMs8/UzwAACAASURBVM1BaFYJDjsMGjaE+vWhsBBee638ujNmQJ06IMGTT6ay\nAQPS97IfgPHjYY89oEkTePzxkuv86EcwalSVdcksM/z6hFkl6NABevWCL7+EG26ACy+Ejz9es14E\nXHQR1K4N335bUn7mmbD//mn/66+hd2/43vfS9379oFUraNwYrr4aunWDl15K9bp3r/q+mW3tPCIE\nJK2SNEXSNEmjJdXfyOsUShq6njo9Jd2zcS21LdXgwfDDH8Jxx6XR3jbl/Jc1bBjMng2XXFK6/KCD\nUsB16wZLl6ayXr3SdvHiFIRt28KiRbBqFVx1Fdx1V5V1xyxTPCJMlkZEGwBJjwK9gMEbepGImARM\nquS2WQ2wcGGaugTYcUe4//4168ydC9dfD48+CpPK+bckAoYPhx12gJ/8JJWdfz5ceWXav/JKuO8+\naNcuTcGa2abziHBNrwP7SCqQNK24UNI1kgbk9idI+o2kv0n6WNLRufLOkp7L7R8q6S1J7+e2++Xd\nY3dJL0j6SNKN1dg3qyINGsCLL8LQobBsGfTvv2advn1TeO2/PyxYkMq+/DKN8oqNHw8zZ8I556Rr\nAlxxRXquOGVKuu5vfwu33AIXXJCeHZ5+eho1mtnG8Ygwj6TawMnACxWoXjsiDpX0A+BG4Pgyx2cA\nHSNipaTjgVuBM3LHDgUOApYAEyWNzY0m89tyMXAxQK0dmmxsl6ya1K4NJ5yQPk8+mQJt3ry0gKZW\nLdh2W5gzB159FVq2LDnvssvSCPKcc9L3++5L2+Jp0WL75f4YdcUVaVq1qAieeAI+/xzat4dHHllz\nutXMKsZBmNSTNCW3/zrwALDres55KredDBSs5Xgj4A+SWgIBbJt37K8R8TWApKeAoygzpRoRw4Hh\nAHWatYwK98Sq3bhxKZQ6dEhh99Zb0LRpGuntsguccgo89xzcdFMKR0j1R49Oi186dkxlX30FTz8N\nRx4JrVuveZ8ZM9K9PvggLZZZsgRGjEjnrVxZff0129o4CJP/e0ZYTNJKSk8d1y1zzvLcdhVr/zne\nDIyPiNMlFQAT8o6VDTYHXQ22007w7rvw2GNpocxRR8GgQSWvPxTr1Klkf1pu0v3ww9P0JsDIkbBi\nxZqjwWJ9+sDAgekeJ54IXbrAjTemEWHxiNLMNpyDsHz/AnaR1BhYBJxKxaZMizUC5ub2e5Y5doKk\nnYClQBfg/E1rqm1O7duXBFtZUc4fcQYMSJ98ffumT3mef75kv3ZtGDNmQ1ppZuXxYplyRMQK4FfA\nu8BzpGd+FTo1tx0EDJT0JlCrTJ03gD8CU4AxZZ8PmplZ9fGIEIiIBuWUDwXWeC8wIjrn7c+n5Blh\nY2BBrvxtYN+8027IlT8EPLTJjTYzs0rhIKwkkn4E/BpPc5qZ1SgOwkoSEc8Az2zudpiZ2YbxM0Iz\nM8s0B6GZmWWag9DMzDLNzwhrgNbNGzHptlM2dzPMzLZKHhGamVmmOQjNzCzTHIRmZpZpDkIzM8s0\nL5apAYrmLqSg79jN3Qxbi9lexGRW43lEaGZmmeYgNDOzTHMQmplZpjkIzcws0xyEZmaWaQ5Cs0pw\n2GHQsCHUrw+FhfDaa+XXnTED6tQBCZ58sqR8zhw47TTYfnto1AjOPjuVFxVBq1aw444weHBJ/csv\nh4EDq6Y/Zlni1yfMKkGHDtCrF3z5JdxwA1x4IXz88Zr1IuCii6B2bfj229Llp58O06fDdddBs2bw\n97+nYwMHpnA891z4+c/hpz+F2bPhhRdSSJrZpsnUiFBSSPpj3vfakuZJeq6Srj9A0jUbeM5blXFv\n27wGD4Yf/hCOOy6N9rYp57+sYcNSiF1ySeny8eNh8mS46iro2xcuvhiGDEnHFi+GgoIUtitXwrJl\nqd5tt6V7mdmmyVQQAouBgyTVy30/AZi7GdtDRHTYnPe3yrFwITRpkqZIt9sO7r9/zTpz58L116cw\n3GGH0semT0/bMWPS9OoOO8DQoamsRw94+mno3h26dIG33kph2LVr1fbJLCuyFoQAzwPFvw6kOzCq\n+EDZEZ2kaZIKcp8Zku7PlT0q6XhJb0qaKenQvOsfIumVXPlFues0kPSypPckFUk6Le8ei6q2u1Yd\nGjSAF19M4bVsGfTvv2advn3T88P994cFC1LZl1/CokWwfHn6vu228Oc/w557wpVXpunVrl1h1iyY\nOBFGjUpTp3fdBf36QYsWaRT6z39WX1/NtjZZDMLHgW6S6gIHA+9W8Lx9gLty5+wP/AQ4CrgG+EVe\nvYNJQXsE0F/SrsAy4PSIaAscA9whSeu6maSLJU2SNGnVkoUV7pxtHrVrwwknwGWXwaGHpqnOefNS\nKK5YkerMmQOvvAItW8Ldd6eyyy5Lo72CgvT9lFPSgplTTknPDT/9NJW3aJFCdNgwOProNOq89daS\nRTnFo0cz23CZWywTEVMlFZBGg/+9Aad+GhFFAJI+BF6OiJBUBBTk1ftLRCwFlkoaDxwKjAVuldQR\nWA00B5oCX66jncOB4QB1mrWMDWinVbNx4+CJJ9IzvDlz0tRl06ZppLfLLinUnnsObrophSOk+qNH\nw9VXQ8eOaVp1l13S1Og++6TVpA0awPe+V3Kf+fNT4P3tb/DVV6ls5Mg0Wmzbtvr7bba1yFwQ5jwD\n/BboDDTOK19J6VFy3bz95Xn7q/O+r6b0z7FsaAVwNtAEaBcRKyTNLnNtq8F22gnefRceeywtXjnq\nKBg0KL0eka9Tp5L9adPS9vDDYY890v6TT8Kll8LPfgb77QdPPZXCsdgNN6RXJpo0SZ9LL4U77oB9\n94Xevau2j2Zbs6wG4UhgYUQUSeqcVz4bOBVAUltgz4249mmSBgLbk4K2L/BfwFe5EDwGaLHxTbct\nTfv2JcFWVpQzlh8wIH3yHX30ul+HGDas9Pd7700fM9s0mQzCiPic9LyvrDHAuZKmABOBtbwJtl5/\nI02F7gHcHBFfSHoUeFbSJGAKMGPjWm5mZpVNUd4fWW2LUadZy2jWY8jmboathf8+QrMtl6TJEVG4\nvnpZXDVqZmb2fxyEZmaWaQ5CMzPLNAehmZllmoPQzMwyLZOvT9Q0rZs3YpJXJ5qZVQmPCM3MLNMc\nhGZmlmkOQjMzyzQHoZmZZZoXy9QARXMXUtB37OZuxlbPvy7NLJs8IjQzs0xzEJqZWaY5CM3MLNMc\nhGZmlmkOQjMzyzQHoVkZhx0GDRtC/fpQWAivvbb2er//Pey2G9SrB6edBl9/ncpfegn23hvq1IGd\nd4bu3eGbb9KxO+6AHXeEVq2gqCiVLV4Me+0Fn35a9X0zszU5CM3K6NABhg6FG26AKVPgwgvXrPP+\n+9CrVwq0m26CsWOhT590bLvt4KKLYMQIOPJIePxxuPtuWLIE+vaFHj1SeN52W6p/663QrRvsuWf1\n9dHMSjgI10PSKklTJH0g6T1JHXLlu0p6MrffU9I9m7elVlkGD4Yf/hCOOy6N6rZZy38lDz2Utrfe\nCtddl8Jz1ChYtgw6doQrr4Tvfx++971Ub5ttYPlyWLky1d1zT1i0CGbPTuf94hfV1TszK8sv1K/f\n0ohoAyDpRGAg0CkivgDOrIwbSKoVEasq41q26RYuhCZN0v6OO8L9969Zp3gas3nztN1ttxRyc+ZA\ny5Zw330lI8ROnaB3b2jQALp0SaO/WrXgiSfg2mvhxhvTMTPbPDwi3DA7AP8GkFQgaVresV0lvSBp\npqRBxYWShkmaJOlDSTfllc+W1F/SG8B/VVsPbL0aNIAXX0zTo8uWQf/+6z8nIm2ltD3jjDRd2r07\nvPoqjBmTyseMgYkTYdas9Pzws8/g+OPT6LNFC+jXr2r6ZGbl84hw/epJmgLUBZoBx5ZTrw3wPWA5\n8JGkuyNiDtAvIhZIqgW8LOngiJiaO2dZRBy1totJuhi4GKDWDk0qsTu2PrVrwwknpM+TT8L48TBv\nXlpAU6sWbLttyfO8zz+HXXeFuXPTebvtlsp33z19WrVKU5+jR6dng9tskxbgrF4NXbvCPfek54cS\nTJiQFs2cey7st99m675Z5jgI1y9/avQI4GFJB62l3ssRsTBXbzrQApgDnJULtdqkID0AKA7CP5V3\n04gYDgwHqNOsZVRSX2w9xo1LU5YdOqRpzrfegqZN0/O8XXaBU06B555LYTV0aBrBnXBCqte9O9St\nm6ZEd9wxjfBGj07XPeCA0vcZOTKF5BFHwFNPwT/+UfLcceXKau2yWeY5CDdARLwtaWdgbUO05Xn7\nq4DakvYErgHaR8S/JT1EGlkWW1xljbWNstNO8O678NhjaaHMUUfBoEElU57F2rWDe++FX/8aXn8d\nTj4Z7ryz5BrDhsH8+elZY69eMGBAybnffJMW2RS/ltG7N7zySnq14tJL4cADq6WrZpbjINwAkvYH\nagFfA/UrcMoOpLBbKKkpcDIwocoaaJusfXuYNm3tx6LMuPzSS9OnrBtuSJ/yNGwIn3xS8r1FC5g8\necPbamaVw0G4fsXPCAEE9IiIVSo7RFiLiPhA0vvAh8AnwJtV10wzM9sYDsL1iIha5ZTPBg7K7T8E\nPJR37NS8/Z7lnF9QaY00M7ON5tcnzMws0xyEZmaWaQ5CMzPLNAehmZllmoPQzMwyzatGa4DWzRsx\n6bZTNnczzMy2Sh4RmplZpjkIzcws0xyEZmaWaQ5CMzPLNAehmZllmleN1gBFcxdS0Hfs5m5GjTbb\nq27NrBweEZqZWaY5CM3MLNMchGZmlmkOQjMzyzQHoWXGzJlwzDHQuDE0bAgnnACzZq1Zr2dPkEp/\nCgrSsZdegr33hjp1YOedoXt3+OabdOyOO2DHHaFVKygqSmWLF8Nee8Gnn1ZHD81sYzgILTPmzoXV\nq+Gmm+C881KoXXjhmvV++lMYNSp9brwxlbVtm7bbbQcXXQQjRsCRR8Ljj8Pdd8OSJdC3L/ToAfXq\nwW23pfq33grdusGee1ZPH81sw/n1iTIk7QbcCxxA+oPCc8C1EfFtmXq7AkMj4szqb6VtjA4d4NVX\nS74/+ih8+OGa9Q47LH0AevdO21690rZjRzj0UPjPf+CTT+CZZ2CbbWD5cli5Mt3j889h0SKYPTuF\n6dSpVdotM9tEHhHmkSTgKeDpiGgJ7As0AH5dpl7tiPjCIVizbLddyf6kSbBgQQq28ixZAo88kqZC\nTzihpPy++6BZszSy7NQpheV3vgNduqTR31/+kkaG116bRpQNGlRdn8xs0zkISzsWWBYRDwJExCqg\nD3C+pEsljZb0LPCipAJJ0wAkHSjpb5KmSJoqqaWk7SWNlfSBpGmSfpyr21/SxFzZ8Fz4WjX66CM4\n7bT03O/uu8uv9/jjsHAhXHJJek5Y7IwzYOzY9Hzw1VdhzJhUPmYMTJyYnjvuvDN89hkcfzwcdxy0\naAH9+lVpt8xsIzkISzsQmJxfEBH/C/wPaRr5CKBHRBxb5rxewF0R0QYoBD4HTgK+iIhDIuIg4IVc\n3Xsion2urB5w6toaIuliSZMkTVq1ZGEldc+mT0+juNq14ZVX0sguApYtgxUrSte97760KOa880qX\n7747/OAH8OvcPMHo0Wm7zTZQWJiO9+kDd92VglaCCRPS88KPPqryLprZBnIQliYg1lH+14hYsJbj\nbwO/kPRzoEVELAWKgOMl/UbS0RFRnGbHSHpXUhFpBHrg2hoSEcMjojAiCmvVb7Sp/TJgzhzo3Bnm\nz08LYt59N436PvssLXA5/fSSuu+/n0Z3Z56ZRnfF+vRJU6IPPVTy/PCAA0rfZ+TItHL0iCNg1Sr4\nxz9SfUjPEc1sy+LFMqV9CJyRXyBpB2B3YBWweG0nRcRjkt4FTgHGSbowIl6R1A74ATBQ0ovAIOB3\nQGFEzJE0AKhbZb2xUmbNgnnz0v7115eUr+3Vht//Pm2LF8kU22knGDYshWmTJun4gAElx7/5Jo38\nXnstfe/dO40877gDLr0UDlzrH3vMbHNSxNoGQNmUe143kbQa9GFJtYD7gP8ljfAKI6J3rm4B8FxE\nHCRpL+DTiAhJQ4DZwBPAgohYJqkL0DP3+QgoAGoB7wBPRsSAdbWrTrOW0azHkErta9b4l26bZY+k\nyRFRuL56nhrNE+lPBacD/yVpJvAxsAz4xXpO/TEwTdIUYH/gYaA18LdcWT/gloj4DzCCFKpPk0LX\nzMw2I48IawCPCDedR4Rm2eMRoZmZWQU4CM3MLNMchGZmlmkOQjMzyzQHoZmZZZpfqK8BWjdvxCSv\nejQzqxIeEZqZWaY5CM3MLNMchGZmlmkOQjMzyzQHoZmZZZpXjdYARXMXUtB37OZuRo3k3zFqZuvj\nEaGZmWWag9DMzDLNQWhmZpnmIDQzs0xzENpWb+ZMOOYYaNwYGjaEE06AWbPWXnfaNDj2WKhXL9W/\n7rr1Hxs/HvbYA5o0gccfL6n/ox/BqFFV1y8zqxxeNWpbvblzYfVquOkm+PhjuPtuuPDCFGD5li6F\nk05K21/9CurXh/nz13+sXz9o1SqF49VXQ7du8NJL8PXX0L179fbVzDbcFhWEku4EPouIIbnv44A5\nEXFh7vsdwNyIGLyB170SGB4RS9ZybALQDFgK1AHujIjhm9SRDWvbbKAwIuZX1z2zpkMHePXVku+P\nPgoffrhmvVGjUmiOGAFnn51GfhU5tngxHHoo7LYbjB0Lq1bBVVfByJFV1yczqzxb2tToW0AHAEnb\nADsDB+Yd7wC8uRHXvRKov47jZ0dEG+BI4DeSttuIe9gWaru8f5qTJsGCBdCx45r1pk9P28GD04iv\nSRN44on1Hzv/fLjrLrj22rR/333Qrh0UFlZdn8ys8mxpQfgmuSAkBeA04BtJ35FUB2gFvA8g6VpJ\nEyVNlXRTrmx7SWMlfSBpmqQfS7oc2BUYL2n8mrcspQGwGFiVu973Jb0t6T1JoyU1yJXPlnRTrrxI\n0v658iaS/por/72kzyTtnDv2tKTJkj6UdHEl/sysgj76CE47DQoK0vRoWcuXp22zZjBmDNSpAz17\nwjffrPvYFVfAjBkwZQr07w+//S3ccgtccEF6dnj66WnUaGZbpi0qCCPiC2ClpD1Igfg28C5wBFAI\nTI2IbyV9H2gJHAq0AdpJ6gicBHwREYdExEHACxExFPgCOCYijinn1o9Kmgp8BNwcEatyAfZL4PiI\naAtMAq7KO2d+rnwYcE2u7EbglVz5n4E98uqfHxHtcv24XFLjjf5B2QabPh06dYLateGVV1KgRcCy\nZbBiRapTUJC2Z50FXbvC0UenZ4JffLHuYwD77QeHHAIDBsAll0BRURoxFhWladhHHqnmDptZhW1R\nQZhTPCosDsK3876/lavz/dznfeA9YH9SMBYBx0v6jaSjI2JhBe95dkQcTAquayS1AA4HDgDelDQF\n6AG0yDvnqdx2MlCQ2z8KeBwgIl4A/p1X/3JJHwDvALvn2lsuSRdLmiRp0qolFe2Grc2cOdC5c1rc\n8tOfwrvvptWdn32WnvWdfnqq1717Guk9+GB6Fvjyy9C8Oey997qPFZsxA8aNgz590nPCJUtS3a++\ngpUrN0vXzawCtsQgLH5O2Jo0NfoOaUSY/3xQwMCIaJP77BMRD0TEx0A7UiAOlNR/Q24cEfNIwXpY\n7h5/zbvHARFxQV713GQZqyhZdKS1XVdSZ+B44IiIOIQU4HXX05bhEVEYEYW16jfakG5YGbNmwbx5\nKZyuvz6F2tpWc+66Kzz2WKp7xRWwzz7wzDNpFLmuY8X69IGBA1NgnngidOkCN94IbdrAOedUX3/N\nbMNsUatGc94ErgY+iYhVwAJJO5KeGV6UqzMOuFnSoxGxSFJzYAWpPwsi4hFJi4CeufrfAA2Bda7M\nlFQf+B4wCPgf4F5J+0TEP3LHdsuFbXneAM4iLbj5PvCdXHkj4N8RsST3PPHwCv80bJN17pymQdem\nbHnXrumzNus6BvD88yX7tWunZ4lmtuXbEoOwiLRa9LEyZQ2KXzGIiBcltQLelgSwCDgH2Ae4XdJq\nUjD+NHf+cOB5Sf8s5znho5KKX594KCImA0jqCYzKLdSB9MxwXUF4U67+j4FXgX+SQvgFoFfec8h3\nKvrDMDOzqqUo74/KtsFygbkqIlZKOgIYlnstY5PUadYymvUYsukNzCD/NUxm2SVpckSs90WmLXFE\nWJPtATyRewfyW0qmcs3MbAvlIKxEETGT9IzRzMxqiC1x1aiZmVm1cRCamVmmOQjNzCzT/IywBmjd\nvBGTvPrRzKxKeERoZmaZ5iA0M7NMcxCamVmmOQjNzCzTHIRmZpZpXjVaAxTNXUhB37Gbuxk1hn+/\nqJltCI8Izcws0xyEZmaWaQ5CMzPLNAehmZllmoPQtkozZ8Ixx0DjxtCwIZxwAsyatWa92bNBKv25\n8sr1Hxs/HvbYA5o0gccfL7nej34Eo0ZVde/MrDJ51ahtlebOhdWr4aab4OOP4e674cILU4CtTa9e\n0KlT2t9vv/Uf69cPWrVKQXv11dCtG7z0Enz9NXTvXjV9MrOqkckglLQoIhrk9n8A3AUcFxH/U079\nAcCiiPitpAnANRExqYL3uhIYHhFLKqXxViEdOsCrr5Z8f/RR+PDD8usXFqbRXP36FTu2eDEceijs\nthuMHQurVsFVV8HIkZXXBzOrHpmeGpV0HHA3cFJ5IVgJrgTW8r9Xq0rbbVeyP2kSLFgAHTuWX/+i\ni2D77eHAA+Gdd9Z/7Pzz4a674Npr0/5990G7dik0zaxmyeSIEEDS0cAI4AcRMStX1gIYCTQB5gHn\nrWOUuA3wIDAnIn4paRjQHqgHPBkRN0q6HNgVGC9pfkQcs7Z6VdvTbPvoIzjtNCgoSNOjZW2/fZo+\nbdMmPVfs2xfOPjs9T1zXsSuugJNOgmXL0rPCtm3hjTfgggvgr39NofjII+kaZrZlU0Rs7jZUO0kr\ngG+AzhExNa/8WVI4/UHS+cCPIqLLWqZG+wJXANMi4te5c3eKiAWSagEvA5dHxFRJs4HCiJi/rnrr\nam+dZi2jWY8hlftD2IoV/2aZ6dPh2GOhTh2YMAH23BMiYPlyqFULtt12zXPbtYP33oOlS6Fu3Yod\nu+IKaNYsBeZ//Rd8/jm0b5+eHV5ySdX108zWTdLkiFjvPE1WR4QrgLeAC0iBVuwIoGtu/4/AoHLO\n/z3wRHEI5pwl6WLSz7QZcACwtoCrUL1cnYsBau3QpGK9sv8zZw507pymRG+5Bd59N30OPzwF4imn\nwHPPwYgRMHEiHHYYfPopTJkChxySgm5dx4rNmAHjxsEHH6TFMkuWpPO++gpWrtxs3TezDZDVIFwN\nnAW8JOkXEXFrOfXKGy6/BRwj6Y6IWCZpT+AaoH1E/FvSQ0DdsidVtB5ARAwHhkMaEVa8awZp+nLe\nvLR//fUl5Z9+WrrevvvCAw+kVx5q107TnUOGrP9YsT59YODANOo88UTo0gVuvDGNCM85p+r6Z2aV\nJ6tBSEQskXQq8Lqkf0XEA6SA60YaDZ4NvFHO6Q8AHYHRkk4HdgAWAwslNQVOBibk6n4DNATmr6ee\nVaLOndM06Nrkl3fqtObimIocK/b88yX7tWvDmDEb1Ewz2wJkNggBcs/qTgJekzQfuBwYKelacotl\n1nHuYEmNKAnN94EPgU+AN/OqDgeel/TP3GKZ8uqZmdlmkMnFMjWNF8tsGP81TGYGFV8sk+n3CM3M\nzByEZmaWaQ5CMzPLNAehmZllmoPQzMwyLdOvT9QUrZs3YpJXQpqZVQmPCM3MLNMchGZmlmkOQjMz\nyzQHoZmZZZqD0MzMMs2rRmuAorkLKeg7dnM3o0r494Ka2ebmEaGZmWWag9DMzDLNQWhmZpnmIDQz\ns0xzENoW4fLLoWlTkODUU8uvN2QIFBRAnTqw555w990lx/7zHzj3XNhxR2jQADp2TOVz50L79tCw\nIVx1VUn9wYPhkkuqpDtmVoM4CG2L0a3buo/PnAl9+sA226QQW7EiBeicOen4+efDo4/CBRekwNxn\nn1R+773w9dcpBO+8M11n/nwYOhRuuaVq+2RmW74KB6GkRVXZkI0h6XBJH0gqkvSHddTrLOm5vO+3\nSBonqc4G3m+ApGs2ob0/ktR3Y8/fmg0dmkJuXVavTtvmzeH44+G7300jw7p14ZNP4M9/hu7dYeBA\nOO88GDky1V+8ONU97rj0fdEi+OUvU4g2aVJ1fTKzmqGmjwh/DVwZEa2BARU5QVI/4EigS0Qsr8K2\nrSEinomI29bSJr/PWQH77Qe33QZvvgn77w/vvw/Dh6cwmz491Zk4EbbfPn1+/vNU1r17qtupExQW\nQgS89hpcdtnm64uZbTk2KAglNZD0sqT3cqOw03LlBZJmSLpf0jRJj0o6XtKbkmZKOjRXb4CkP0h6\nUdJsSV0lDcpd6wVJ2+bqzZa0c26/UNKEcpr0LbAbQER8WoH2Xw38APhhRCzNlfWXNDHX7uGSlCu/\nXNJ0SVMlPZ53mQMkTZD0iaTLN7D/PSXdk9t/SNJgSeOB32zIP4esmjcvPRNs0waefhoOOQR694bP\nP4fluT/SLF4Mf/oTHHkkDBoEL70Ehx8On34K77wDb7wB114Lt9+eQnSvveCww0qC1MyyZ0NHhMuA\n0yOiLXAMcEdxcAD7AHcBBwP7Az8BjgKuAX6Rd429gVOA04BHgPG5Ed3SXPmGmAUMlFRYgbpHAr2A\nkyMif5r3nohoHxEHAfWA4qUafYHvRcTBufOK7Q+cCBwK3Fgc3lS8//n2BY6PiKsr0P5MWr68JOTG\nj08LX7p2hdNOS9tvvoG3304LaACOPjqVn3VW+j5rVtp+97sp8P77v2HbbaFzZ7jiCvjDH+CAA+Dm\nm6u7Z2a2pdjQIBRwq6SpwEtAc6Bp7tinEVEUEauBD4GXIyKAIqAg7xrPR8SKXHkt4IVcedl6625I\nGo02Ak4GHpPUUlITSRPLOeUfufZ/v0z5MZLelVQEHAscmCufCjwq6RxgZV79sRGxPCLmA19tRP/z\njY6IVeX072JJyc2lJQAAHUVJREFUkyRNWrVkYTmnbz3Gjk0jOUiLX+6/Py1q2W8/aNw4le+1V9o+\n8gg88EBaGAOw777Qti20bg0vvwwjRsCDD0KtWmlkWGz5crj++rRgZvXqNEX6xBPwwQewMv+fsJll\nyoYG4dlAE6BdRLQB/gXUzR3Lf962Ou/7akr/TtPlALnAWJELi7L1Vua1rS5rdyIpbIqAC4C/AL2B\nP5VT/1+kadE7JR0DIKku8DvgzNyodETe/U4B7gXaAZPznuPl93NVXpsr2v98i8spJyKGR0RhRBTW\nqt+ovGpbjdtvh765ZURTp8JFF6VngfkKC+GOO1Kg/exnaXvPPWmKVIJRo2DvvdOzvwUL4OGH4aCD\nSs4fMgROPBFatUqvUtxyS6qzaFEKSDPLpg1dpNEI+CoiVuTCpEUVtAlgNimAngfOKKfO+8CPJT0e\nEa9L+jPQb11tioiPJXUFnpZ0Su4+APMlNQDOBJ6UtA2we0SMl/QGaZqzQSX0y8oxYcLay3v2LP39\nqqtKvwuY78AD0zRpeYoXzxS7/noHoJlVcESYGw0tBx4FCiVNIo0OZ1RRu24C7pL0OmnUtTYPkKYd\np+Ta04z0PO5JSfXLu3BETATOA54BGpNGgUXA00DxtGot4JHcdOn7wJ0R8Z9N7pWZmW1xVDIzuY5K\n0iHAiIg4tOqbZGXVadYymvUYsrmbUSX81zCZWVWRNDki1ruYcr0jQkm9gFHALyujYWZmZluS9T4j\njIj7gPuqoS1mZmbVrqb/ZhkzM7NN4iA0M7NMcxCamVmmOQjNzCzT/Lce1ACtmzdikl8zMDOrEh4R\nmplZpjkIzcws0xyEZmaWaQ5CMzPLNC+WqQGK5i6koO/Yzd2MTebfK2pmWyKPCM3MLNMchGZmlmkO\nQjMzyzQHoZmZZZqD0Krd5ZdD06Ygwamnll/vP/+Bc8+FHXeEBg2gY8eSYyNHwt57Q716cOKJMHdu\nKi8qglat0jmDB5e+58CBVdMfM6vZHIS2WXTrtv46558Pjz4KF1wAQ4bAPvuk8kmT4MILoXlz+M1v\nYMIE+OlP07GBA2H77VOA/vznsHQp/P3v8MILcNVVVdYdM6vBHIR5JJ0uKSTtv7nbsjUbOhT69Fl3\nnU8+gT//Gbp3T+F23nlpFAjw6qsQAZdckkZ6bdvCc8/B11/D4sVQUAAdOsDKlbBsWQrA226DOnWq\nvGtmVgM5CEvrDrwBVGC8YlVp+vS0nTgxjfC23z6N8AB22SVt33gDZsyAmTNTMM6eDT16wNNPpwDt\n0gXeeiuFYdeum6UbZlYDOAhzJDUAjgQuIBeEkppJek3SFEnTJB0tqZakh3LfiyT1ydW9SNJESR9I\nGiOpfq78IUnDJI2X9ImkTpJGSvq7pIc2V3+3dMuXp+3ixfCnP8GRR8KgQfDSS3DWWen7ffel54Hf\nfpvq1q2bAm/WrBSgo0bBddfBXXdBv37QogUcdxz885+br19mtuVxEJboArwQER8DCyS1BX4CjIuI\nNsAhwBSgDdA8Ig6KiNbAg7nzn4qI9hFxCPB3UqAW+w5wLNAHeBa4EzgQaC2pTTX0rUZYvrwkAAsK\n0vboo1O4nXVW+j5rVprifO01mDIFpk2Dww5LIbjXXqlOixZQWAjDhqXzt9sObr01nQNpatbMrJiD\nsER34PHc/uO57xOB8yQNAFpHxDfAJ8Beku6WdBLwv7lzDpL0uqQi4GxS0BV7NiICKAL+FRFFEbEa\n+BAoWFtjJF0saZKkSauWLKzUjm5uY8emUR7AnDlw//1penO//aBx41Teti20bg0vvwwjRsCDD0Kt\nWmkkuGpVesb4/vvwu9+lUeKll6YVpMXmz0+Bd/PNqT6kZ4yzZqVnh2ZmxRyEgKTGpBHb/ZJmA9cC\nPwZeBzoCc4E/Sjo3Iv5NGh1OAH4G3J+7zENA79wo8Sagbt4tcuMcVuftF39f6+97jYjhEVEYEYW1\n6jfa1C5uUW6/Hfr2TftTp8JFF8Gbb5auI6Wpzb33hssugwUL4OGH4aCD0rFXX4VeveDxx6F37zTi\ny3fDDWkhTZMmcOCBKSjvuAN22inVNzMr5l+6nZwJPBwRlxQXSHqVFIJvRsQISdsDbSX9N/BtRIyR\nNIsUgAANgX9K2pY0IpxbrT2oQSZMWHt5z56lvx94ILz99pr1ttkmTYuuy7Bhpb/fe2/6mJmV5SBM\nugO3lSkbQwq5xZJWAIuAc4HmwIOSikfT1+e2NwDvAp+RpkAbVnGbzcysEig9urItWZ1mLaNZjyGb\nuxmbzH8Nk5lVJ0mTI6JwffX8jNDMzDLNQWhmZpnmIDQzs0xzEJqZWaY5CM3MLNMchGZmlml+j7AG\naN28EZP86oGZWZXwiNDMzDLNQWhmZpnmIDQzs0xzEJqZWaZ5sUwNUDR3IQV9x27uZpTLv0PUzGoy\njwjNzCzTHIRmZpZpDkIzM8s0B6GZmWWag9AqxeWXQ9OmIMGpp5Zf77DDoGFDqF8fCgvhtddKH583\nD3beOV3nt79NZXPnQvv26byrriqpO3gwXHJJ5ffFzLLFQWiVplu39dfp0AGGDoUbboApU+DCC0sf\nv+IKWLq0dNm998LXX6cQvPNOmDkT5s9P17nllsprv5ll03qDUNKiMt97Sront99L0rlV1ThJAyRd\ns4HnzJa0cznlRZI+kPSipO9uQrsKJE3byHMXrb9WzTN0KPTps/56gwfDD38Ixx0HderANnn/Bj7/\nPDz7LPz856XPWbwYvvvddA7AokXwy1+mUWiTJpXXBzPLpk0aEUbEfRHxcGU1phocExGHAJOAX5Q9\nKKlW9TcpWxYuTOF12GGw3XZw//2pfNEi6NULBg6EPfYofU737vD++9CpU5pOjUhTqpddVv3tN7Ot\nzyYFYf6ITdIESXdKek3S3yW1l/SUpJmSbsk752lJkyV9KOnivPKTJL2XG7G9nHebA3LX/kTS5eu7\nTgW9BuyTu84iSb+S9C5whKT+kiZKmiZpuCTl6rXLte1t4Gd57agl6fbcOVMlXZIrb5b7WUzJXevo\nvHN+nbvWO5KabmDba7QGDeDFF9MIctky6N8/lf/mN+m54fe/D199lcq+/hr+/W84/HD49FN45x14\n4w249lq4/XYYPhz22iuF6vTpm69PZlazVeQ3y9STNCXv+07AM+XU/TYiOkq6AvgL0A5YAMySdGdE\nfA2cHxELJNUDJkoaQwrkEUDHiPhU0k5519wfOAZoCHwkaVhErFjbdXLXr4hTgaLc/vbAtIjoDyBp\nekT8Krf/x1zdZ4EHgcsi4lVJt+dd6wJgYUS0l1QHeFPSi0BXYFxE/Do30qyfd793IqKfpEHARcBW\n+6Rr+fK0rVMnbWvXhhNOSJ8nn4Tx49PzvjlzYMYM2G+/knNvuw223z5Ng373u+nz5z/DtttC587Q\nqFE6f+RIuPlmGDWq2rtnZluBigTh0ohoU/xFUk+gsJy6xQFZBHwYEf/MnfMJsDvwNXC5pNNz9XYH\nWgJNgNci4lOAiFiQd82xEbEcWC7pK6Ap8Hk511lfEI6XtAqYCvwyV7YKGJNX5xhJ15GCayfgQ0mv\nATtGxKu5On8ETs7tfx84WNKZue+Ncm2ZCIyUtC3wdEQU/2HiW+C53P5k4IS1NTQ3yr0YoNYOW/6D\nsLFjYVruqemcOWnKs1OnFHjz56epz3Hj4Ikn0oKZOXPgrbfSStPGjaF375LVphMmpAUy554LZ55Z\nco/ly+H661MYrl6dpkifeAI++AD23rvau2xmW4nK/l2juT//szpvv/h7bUmdgeOBIyJiiaQJQF1A\nQKznmpBCa13XWZ9jImJ+mbJlEbEKQFJd4HdAYUTMkTSgAu0TaaQ4bo0DUkfgFOCPkm7PPU9dERHF\n11pFOf8MImI4MBygTrOW5d17i3H77fBq7o8JU6fCRRfBgw+WrrPTTvDuu/DYY2mEeNRRMGhQelWi\nsDB9IIUmQOvWsP/+JecPGQInngitWqXvt9ySRo1Nm6aANDPbGNX9S7cbAf/Ohdf+wOG58reBeyXt\nWTw1WmZUWNHrbKriMJ0vqQFwJvBkRPxH0kJJR0XEG8DZeeeMA34q6ZWIWCFpX2AusDMwNyJGSNoe\naAvUpIVFG2TChLWX9+xZst++fcmocV169ix9XrGyq0mvv94BaGabrrqD8AWgl6SpwEfAOwARMS83\nFfiUpG2AryhnynBd19lUucAbQZranU2a3ix2Hmmqcwkp/IrdDxQA7+UW1swDugCdgWslrQAWAVX2\nmomZmW08lczS2ZaqTrOW0azHkM3djHL5r2Eysy2RpMkRUd6alv/j3yxjZmaZ5iA0M7NMcxCamVmm\nOQjNzCzTHIRmZpZpDkIzM8u06n6P0DZC6+aNmORXFMzMqoRHhGZmlmkOQjMzyzQHoZmZZZqD0MzM\nMs2LZWqAorkLKeg7tlrv6d8famZZ4RGhmZllmoPQzMwyzUFoZmaZ5iA0M7NMcxCamVmmOQitXG++\nCQcfDHXqQNu28N57a9ZZsQKuvBKaNoVGjeDSS1MZQOfOIJX+dO6cjt1xB+y4I7RqBUVFqWzxYthr\nL/j00+ronZlZUmODUFJIuiPv+zWSBmzgNTpL6pD3/SFJZ1bgvFWSpuR9+m5Q49dy7y3NsmVwxhnw\nzTdw553wr3/BmWfCqlWl6w0dCnfdBaedBuefD8OGwd13p2P9+8OoUelz6aWprG1bWLIE+vaFHj2g\nXj247bZ07NZboVs32HPP6uunmVlNfo9wOdBV0sCImL+hJ0uqDXQGFgFvbeDpSyOizYbes4yNvXe1\neP75FH6DBqUQ+/JLuPlmmDABjjuupN6ECWl7443QuDEMGQIPPQRXXQXHHltS75FH0vaSS2D5cli5\nEjp0gM8/h0WLYPbsFJhTp1ZP/8zMitXYESGwEhgO9Cl7QFILSS9Lmprb7pErf0jSYEnjgT8BvYA+\nuVHd0bnTO0p6S9InFRkdlrlvf0kTJU2TNFyScuWXS5qea8/jkgrKufcWo3h6snnztN1tt7T95JPS\n9XbZJW1ffhlefLH0ucXmzEnBeuyxsN9+8J3vQJcuafT3l7+kkeG116YwbdCgavpjZlaemhyEAPcC\nZ0tqVKb8HuDhiDgYeBQYmndsX+D4iDgDuA+4MyLaRMTruePNgKOAU4HbyrlvvTJToz8uvm9EtI+I\ng4B6uWsA9AW+l2tPr4iYXc69t1gRaZuivcTPfw677prC7Iwz0lRn3bql64wYAatXQ69eJWVjxsDE\niTBrFuy8M3z2GRx/fBpttmgB/fpVbX/MzIrV5KlRIuJ/JT0MXA4szTt0BNA1t/9HYFDesdERUeZJ\nVylPR8RqYLqkpuXUKW9q9BhJ1wH1gZ2AD4FnganAo5KeBp5eX78AJF0MXAxQa4cmFTmlUhU/p/v8\n87SdO7ekfNkyqFULtt0W9t0XZs5MC16+8x049FA44ICS66xcCQ88AN/9bhoFFttmGygsTAHZtSvc\nc096tiil6da99oJzz00jSDOzqlTTR4QAQ4ALgO3XUSfy9hev53rL8/ZVbq0yJNUFfgecGRGtgRFA\n8djoFNLotR0wOfd8cp0iYnhEFEZEYa36ZQe8Ve/kk9O057Bh6fPAA1BQkD716sHpp6d6U6akFaDT\np0Pv3rBwIVxzTcl1nn0WvvgCLrggBWdZI0emlaNHHJEW4vzjH+kZI6QQNTOrajU+CCNiAfAEKQyL\nvQV0y+2fDbxRzunfAA0rqSnFoTdfUgPgTABJ2wC7R8R44DpgR6BBJd+70tWtC6NHp2d2V1yRQnH0\n6DQSLGvkyLQI5uOP4Xe/gx/+sOTY73+fRn8XX7zmed98k1aKFq8a7d07Lbi54460QOfAA6umb2Zm\n+Wr01GieO4Deed8vB0ZKuhaYB5xXznnPAk9KOg24bAPuV0/SlLzvL0REX0kjgCJgNjAxd6wW8Eju\nOaZIzwX/I6nUvbfE54QdO5a845cv8sbXbdqs+72/F14o/1jDhqUX37RoAZMnb3g7zcw2hSL//2q2\nRarTrGU06zGkWu/pv4bJzGo6SZMjonB99Wr81KiZmdmmcBCamVmmOQjNzCzTHIRmZpZpDkIzM8s0\nB6GZmWXa1vIe4VatdfNGTPLrDGZmVcIjQjMzyzQHoZmZZZqD0MzMMs1BaGZmmebFMjVA0dyFFPQd\nWynX8u8QNTMrzSNCMzPLNAehmZllmoPQzMwyzUFoZmaZ5iA0M7NMcxBm1JtvwsEHQ5060LYtvPfe\n2utNmwbHHgv16kHjxnDddal89myQSn+uvDIdGz8e9tgDmjSBxx8vudaPfgSjRlVpt8zMNlhmXp+Q\n9F1gCNAeWA7MBp4GfhQRp26mNt0PDI6I6dV532XL4IwzUrjdeSf8+tdw5pkwcybUqlVSb+lSOOmk\ntP3Vr6B+fZg/v/S1evWCTp3S/n77pW2/ftCqVQrOq6+Gbt3gpZfg66+he/fq6aOZWUVlIgglCfgz\n8IeI6JYrawP8cHO2KyIu3Bz3ff55+Ne/YNAguPRS+PJLuPlmmDABjjuupN6oUTB3LowYAWefnYKz\nrMLCNNKrX7+kbPFiOPRQ2G03GDsWVq2Cq66CkSOrvGtmZhssK1OjxwArIuK+4oKImAK8DjSQ9KSk\nGZIezYUmktpJelXSZEnjJDXLlU+Q9BtJf5P0saSjc+U9JT0l6QVJMyUNKr6XpGGSJkn6UNJNeeUT\nJBVW08/g/3z6ado2b562u+2Wtp98Urre9Nw4dfDgFHRNmsATT5Suc9FFsP32cOCB8M47qez88+Gu\nu+Daa9P+ffdBu3YpNM3MtjRZCcKDgMnlHPsecCVwALAXcKSkbYG7gTMjoh0wEvh13jm1I+LQ3Hk3\n5pW3AX4MtAZ+LGn3XHm/iCgEDgY6STq4crpVOSLSNv0RoMTy5WnbrBmMGZOeJ/bsCd98k8Lvppvg\n6afht7+Fjz9Oo0aAK66AGTNgyhTo3z8dv+UWuOCC9Ozw9NPTqNHMbEuQianR9fhbRHwOIGkKUAD8\nhxSef80NEGsB/8w756ncdnKufrGXI2Jh7lrTgRbAHOAsSReTft7NSKE7dV2NytW/GKDWDk02unNr\ns+eeafv552k7d25J+bJl6TnhtttCQUEqP+ss6NoVRo9Oi1+++CI9D+zfv+Sajz2WFtwsWwZ165Y8\nL7ziCrjkEigqSqPJzz+H9u3hkUdSuZnZ5paVIPwQOLOcY8vz9leRfiYCPoyII9ZzTnH9cq8laU/g\nGqB9RPxb0kNA3fU1OCKGA8MB6jRrGeurvyFOPhl22QWGDYOGDeGBB1LoFRSk54CnnALPPZcWtvTr\nBw8+CNtsAy+/nKZT9947PTecOBEOOyxNtU6ZAocckkKw2IwZMG4cfPBBWiyzZEk676uvYOXKyuyR\nmdnGy8rU6CtAHUkXFRdIag90Kqf+R0ATSUfk6m4r6cCNvPcOwGJgoaSmwMkbeZ1KU7duGt01aJBG\nbLvskr7nrxgF2HXXNNKbNy/V22cfeOYZqF0b9t0Xpk5Nr0zce29aXTp6dOnz+/SBgQPTlOqJJ0KX\nLnDjjdCmDZxzTvX118xsXTIxIoyIkHQ6MERSX2AZJa9PrK3+t5LOBIZKakT6OQ0hjSw39N4fSHo/\nd+4nwJsb14vK1bFjmq4sK8qMPbt2TZ+yOnUqWRxTnuefL9mvXTs9ZzQz29Ioyv6fz7Y4dZq1jGY9\nhlTKtfzXMJlZVkia/P/bub8Que4qgOPfQ9aktmqTmKprU2zjg5Ai2hjQqEgRtW0sFd9afKhWX6yI\nfxBJCIg+tvUhFMVERKkSa2usCoFSpPjgU2qqJk0126xJ1K3FNIh/8KnFnw/3THZ23dlk7u7M3OX3\n/cBl7j2/O3PPnjt3TubeO8kbFZdVy6lRSZKWZCOUJFXNRihJqpqNUJJUNRuhJKlqNkJJUtWq+B3h\nWvfWa6/mmD97kKSR8BuhJKlqNkJJUtVshJKkqtkIJUlVsxFKkqpmI5QkVc1GKEmqmo1QklQ1G6Ek\nqWo2QklS1WyEkqSq2QglSVWzEUqSqmYjlCRVzUYoSaqajVCSVDUboSSpajZCSVLVbISSpKrZCCVJ\nVbMRSpKqZiOUJFXNRihJqpqNUJJUNRuhJKlqNkJJUtVshJKkqtkIJUlVsxFKkqpmI5QkVS1KKZPO\nQZcQEf8GZiadx5C2ABcmncSQ1mLOsDbzNufxWYt5r1bObyqlXHOplaZWYUMavZlSys5JJzGMiDhm\nzuOxFvM25/FZi3mPO2dPjUqSqmYjlCRVzUa4Nnx70gm0YM7jsxbzNufxWYt5jzVnb5aRJFXNb4SS\npKrZCDssIm6NiJmImI2IPRPY/nUR8cuI+ENEPBsRn8v45oj4RUSczsdNGY+IeDDzPRERO/pe6+5c\n/3RE3N0Xf0dEPJPPeTAiYpVyXxcRv42II7l8Q0Qcze0/EhHrM74hl2dz/Pq+19ib8ZmIuKUvPpL9\nEhEbI+JwRJzKmu/qeq0j4gv53jgZEQ9HxBVdrHVEfDcizkfEyb7YyGs7aBsryPmBfH+ciIifRsTG\nvrGhathmP7XJuW/sSxFRImJLLneizgCUUpw6OAHrgD8C24D1wHFg+5hzmAZ25PyrgeeA7cD9wJ6M\n7wHuy/ndwONAAO8CjmZ8M3AmHzfl/KYcewrYlc95HLhtlXL/IvBD4EguPwrcmfMHgE/n/L3AgZy/\nE3gk57dnzTcAN+S+WDfK/QI8BHwq59cDG7tca+Ba4Czwyr4af7yLtQbeB+wATvbFRl7bQdtYQc4f\nAqZy/r6+nIeu4bD7qW3OGb8OeAL4E7ClS3UupdgIuzrlzn6ib3kvsHfCOf0c+CDNj/unMzZN8ztH\ngIPAXX3rz+T4XcDBvvjBjE0Dp/riC9ZbQZ5bgSeB9wNH8qC50PcBcrG2eXDuyvmpXC8W17u33qj2\nC/AamqYSi+KdrTVNI/xLfmBNZa1v6WqtgetZ2FRGXttB22ib86KxjwKHlqrNpWrY5phYSc7AYeBt\nwDnmG2Fn6uyp0e7qfcj0zGVsIvL0yE3AUeD1pZQXAPLxdbnaoJyXi88tEV+p/cCXgf/m8muBf5RS\nXl5iOxdzy/F/5vrD/i0rtQ14EfheNKd0vxMRV9HhWpdSnge+DvwZeIGmdk/T/Vr3jKO2g7axGu6h\n+VbUJuc2x0QrEXEH8Hwp5fiioc7U2UbYXUtdv5nILb4R8SrgJ8DnSyn/Wm7VJWKlRby1iLgdOF9K\nefoy8lpubGw5pymaU0rfKqXcBPyH5hTPIBPPO6/DfITmVNwbgauA25bZzsRzvkydzzMi9gEvA4d6\noQE5tMl51f6eiLgS2Ad8ZanhIXMbWZ1thN01R3NevWcr8NdxJxERr6BpgodKKY9l+G8RMZ3j08D5\njA/Kebn41iXiK/Ee4I6IOAf8iOb06H5gY0T0/kvB/u1czC3Hrwb+3uJvWak5YK6UcjSXD9M0xi7X\n+gPA2VLKi6WUl4DHgHfT/Vr3jKO2g7bRWt48cjvwsZLnAlvkfIHh91Mbb6b5h9LxPCa3Ar+JiDe0\nyHl0dW57vt1ptBPNN4Qz+SbqXeS+ccw5BPB9YP+i+AMsvDB9f85/mIUXv5/K+Gaa61+bcjoLbM6x\nX+e6vYvfu1cx/5uZv1nmxyy8MeDenP8MC28MeDTnb2ThzQdnaG48GNl+AX4FvCXnv5p17mytgXcC\nzwJX5ms+BHy2q7Xm/68Rjry2g7axgpxvBX4PXLNovaFrOOx+apvzorFzzF8j7E6dV+MAdhrNRHNX\n1XM0d33tm8D230tz6uEE8LucdtNcL3gSOJ2PvTdpAN/MfJ8Bdva91j3AbE6f6IvvBE7mc77BEBfl\nLyP/m5lvhNto7jibzQ+ADRm/Ipdnc3xb3/P3ZV4z9N1hOar9ArwdOJb1/ll+CHS61sDXgFP5uj+g\n+SDuXK2Bh2muY75E883ik+Oo7aBtrCDnWZrrZ73j8UDbGrbZT21yXjR+jvlG2Ik6l1L8n2UkSXXz\nGqEkqWo2QklS1WyEkqSq2QglSVWzEUqSqmYjlCRVzUYoSaqajVCSVLX/AX28JGK1MkgYAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee7bfd4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['Technology', 'Test_type', 'Service Provider', 'LSA']\n",
    "\n",
    "for c in columns:\n",
    "    v = df[c].unique()\n",
    "    g = df.groupby(by=c)[c].count().sort_values(ascending=True)\n",
    "    r = np.arange(len(v))\n",
    "    print(g.head())\n",
    "    plt.figure(figsize = (6, len(v)/2 +1))\n",
    "    plt.barh(y = r, width = g.head(len(v)))\n",
    "    total = sum(g.head(len(v)))\n",
    "    print(total)\n",
    "    for (i, u) in enumerate(g.head(len(v))):\n",
    "        plt.text(x = u + 0.2, y = i - 0.08, s = str(round(u/total*100, 2))+'%', color = 'blue', fontweight = 'bold')\n",
    "    plt.margins(x = 0.2)\n",
    "    plt.yticks(r, g.index)\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "170a4107f680e457322b228b2833f801ccf440bd"
   },
   "source": [
    "Basic bar plots of various categories in dataset. Number of times a category appears in given column.\n",
    "\n",
    "* In technologies, 4G comprises of almost 90% of the dataset while 3G is only 10%. Hence number of people with 3G are much smaller compared to 4G users.\n",
    "* Upload and Download tests are almost equal as they should be.\n",
    "* In Service Providers JIO is currently dominating with 55.35% of whole dataset that is greater than all other service providers put together.\n",
    "* State wise, most samples occur in Maharashtra while least samples occur in Chennai."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "c3e334ba1453f37aeb0057cddc86bed984b76f28"
   },
   "outputs": [],
   "source": [
    "def sel(df, column_name, value):\n",
    "    data = df.loc[(df[column_name] == value)]\n",
    "    return data\n",
    "\n",
    "pandas.DataFrame.mask = sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "4a5a7d021ee5602b556838148f510a36f3b2dec9"
   },
   "outputs": [],
   "source": [
    "def plot_graphs(provider, state):\n",
    "    # plot distributions of speeds\n",
    "    #columns = ['Technology', 'Test_type', 'Service Provider', 'LSA']\n",
    "    data4g = df.mask('Test_type', 'Download').mask('Technology', '4G')\n",
    "    data3g = df.mask('Test_type', 'Download').mask('Technology', '3G')\n",
    "    \n",
    "    if provider != 'All':\n",
    "        data4g = data4g.mask('Service Provider', provider)\n",
    "        data3g = data3g.mask('Service Provider', provider)\n",
    "    \n",
    "    if state != 'All':\n",
    "        data4g = data4g.mask('LSA', state)\n",
    "        data3g = data3g.mask('LSA', state)\n",
    "        \n",
    "    x1 = data4g['Data Speed(Mbps)']\n",
    "    x2 = data3g['Data Speed(Mbps)']\n",
    "\n",
    "    fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (9, 5))\n",
    "    #print(x1)\n",
    "    axes[0].clear()\n",
    "    axes[0].hist(x1, bins=100, label = '4G', normed = True)\n",
    "    axes[0].axvline(x1.mean(), color = 'k', linewidth = 1, label = 'avg:'+str(round(x1.mean(), 2)))\n",
    "    axes[0].legend(loc = 'upper right')\n",
    "    axes[0].set_xlabel('Data Speeds(Mbps)')\n",
    "\n",
    "\n",
    "    # print(x2)\n",
    "    axes[1].clear()\n",
    "    axes[1].hist(x2, bins=100, label = '3G', color = 'g', normed = True)\n",
    "    axes[1].axvline(x2.mean(), color = 'k', linewidth = 1, label = 'avg:'+str(round(x2.mean(), 2)))\n",
    "    axes[1].legend(loc = 'upper right')\n",
    "    axes[1].set_xlabel('Data Speeds(Mbps)')\n",
    "\n",
    "    fig.canvas.set_window_title('Provider-' + provider + ' ' + 'State-' + state)\n",
    "    #plot both histogram in one figure\n",
    "    # plt.figure(figsize = (9, 5))\n",
    "    # plt.hist(x1, bins=100, label = '4G', alpha = 0.5, normed = True)\n",
    "    # plt.axvline(x1.mean(), linewidth = 1, color = 'b', label = 'avg:'+str(round(x1.mean(), 2)))\n",
    "    # plt.legend(loc = 'upper right')\n",
    "    # plt.hist(x2, bins=100, label = '3G', color = 'g', alpha = 0.5, normed = True)\n",
    "    # plt.axvline(x2.mean(), linewidth = 1, color = 'g', label = 'avg:'+str(round(x.mean(), 2)))\n",
    "    # plt.legend(loc = 'upper right')\n",
    "\n",
    "    plt.suptitle('State-' + state + \"    \" + 'Provider-' + provider, fontsize = 16)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d9df98f14aef7052f00ba5c981317adab7133dc5"
   },
   "source": [
    "Widgets are added to plot distributions of 4g and 3g speeds for different states and providers. The vertical line gives average speed of the distribution. This distribution gives answer to certain anamolies that appear in last section where we do visualisation of average speeds for various service providers.\n",
    "\n",
    "From seeing the distributionns, it is obvious that the distribution of speeds over customers decreases exponentially as Download Speeds increases. Hence more customers are provided with lower speeds while the number of customers with higher speeds is much less. As speed increases, the number of customer having that speed decreases rapidly.\n",
    "\n",
    "Some providers do not provide 4g, hence an empty graph is plotted with average speed set to nan. Example include Aircel, Cellone etc. which do not provide 4g. Similarly Jio that do not provide 3g."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "6abe842041b49e8ff2264cdbd47355b0c5e606e9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4affe82568634bdeb2c99016ecc09ff3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zvgn4cHklSV8BvgFsAxxendDMrJp8ZsXMikoVyjY5cxIRl0XE3wOnA9+uuCFp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\nc7JiZmZmheZkxczMzArt/wO3DoGkaVqz2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee7ae3208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KJElSJZiUSJKkSjApkSRJlWBSIkmSKsGkRJIkVYJJiSRJqgSTEkmSVAkmJZIk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\npTvQD4qe4H+i6MF7YqfjqYv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef26074e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZmVmpOVkxMzOzUnOyYmZmZqXmZMXMzMxKzcmKmZmZlZqTFTMzMys1JytmVlOS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef260a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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idWnJ6WMz6yLyJCV5ul0b6zSkp2q2BpYBnwd+FxGrgdckPUJyrni9pCQiJgGT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\nHJYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee7ba3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from ipywidgets import HBox\n",
    "state_select = widgets.Dropdown(\n",
    "    options=['All', 'North East', 'Kolkata', 'Bihar', 'Chennai', 'Jammu & Kashmir', 'Delhi',\n",
    "       'Tamil Nadu', 'Maharashtra', 'Punjab', 'UP East', 'Rajasthan',\n",
    "       'Gujarat', 'West Bengal', 'Mumbai', 'Kerala', 'Andhra Pradesh',\n",
    "       'UP West', 'Orissa', 'Assam', 'Madhya Pradesh', 'Karnataka', 'Haryana',\n",
    "       'Himachal Pradesh'],\n",
    "    value='All',\n",
    "    description='States:',\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "provider_select = widgets.Dropdown(\n",
    "    options=['All', 'JIO', 'VODAFONE', 'AIRTEL', 'IDEA', 'CELLONE', 'UNINOR', 'DOLPHIN', 'AIRCEL'],\n",
    "    value='All',\n",
    "    description='Provider:',\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "\n",
    "def on_change(change):\n",
    "    if change['type'] == 'change' and change['name'] == 'value':\n",
    "        plot_graphs(provider_select.value, state_select.value)\n",
    "\n",
    "\n",
    "state_select.observe(on_change)\n",
    "provider_select.observe(on_change)\n",
    "\n",
    "hb = HBox([state_select, provider_select])\n",
    "display(hb)\n",
    "plot_graphs(provider_select.value, state_select.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "4e6d1dd0a120fa85625264fe80ae1a3d17b084ec"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee764cf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2d histograms\n",
    "#columns = ['Technology', 'Test_type', 'Service Provider', 'LSA']\n",
    "import matplotlib.colors as colors\n",
    "\n",
    "data = df.dropna().mask('Test_type', 'Download').mask('Technology', '4G')\n",
    "#print(data.isnull().sum())\n",
    "x = data['Signal_strength']\n",
    "y = data['Data Speed(Mbps)']\n",
    "plt.hist2d(x, y, bins = 40, norm=colors.LogNorm())\n",
    "plt.ylabel('Data Speed(Mbps)')\n",
    "plt.xlabel('Signal_strength')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "2260cdc159133158ab7b3e4673786018f74bc8e2"
   },
   "source": [
    "Here is a 2D histogram plotted for Download Speed of 4g Networks vs Signal Strength.\n",
    "\n",
    "* The max Speed increases as Signal Strength increases.\n",
    "* Optimal values of max speeds occur in interval [-80, -70] of signal strength.\n",
    "* Most of the sample lies in [-110, -80] (Yellow Color), but the speeds of most sample is much lower. In this interval most of the customers are having much lower speeds. As speed increases, the number of customers decreases. Hence it is less likely to get a higher speed with signal strength in this interval.\n",
    "* While in interval [-80, -70], which is also an optimal interval for max speeds, as you move upwards towards increasing speeds, number of customers do not decrease exponentially. Hence this interval can be said to be optimal in terms of speed distribution. As you are more likely to get a higer speed in this interval.\n",
    "* After  -70, max speeds again start decreasing.\n",
    "* The various rows where signal strength is set to None are dropped in this Histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "4353573d513c98fb74c4a6dc5cddede4c89703e0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uvtGCZfQTw1thhPU9Y2aNgb3AqTH1jnH3vcD/zOzDsD0JaAi8H45VFFgT7psH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\nXjLy009e/LffvPill3qfDh1gwQJITISSJeGmm6BTJyhbFl56CYoVg5Y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/wRpUj2d0l5d5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\nTwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efef26055c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# avg speeds of states and service providers\n",
    "#columns = ['Technology', 'Test_type', 'Service Provider', 'LSA']\n",
    "\n",
    "state = 'LSA'\n",
    "service = 'Service Provider'\n",
    "speed = 'Data Speed(Mbps)'\n",
    "\n",
    "values = df[state].unique()\n",
    "r = np.arange(len(values))\n",
    "\n",
    "plt.figure(figsize = (8, len(values)/2 +1))\n",
    "plt.xlabel(speed)\n",
    "\n",
    "\n",
    "# 4g\n",
    "data = df.mask('Test_type', 'Download').mask('Technology', '4G')\n",
    "group = data.groupby(by=state)[speed].mean().sort_values(ascending = True)\n",
    "plt.barh(y = r, width = group.head(len(values)), label = '4G')\n",
    "for (i, v) in enumerate(group.head(len(values))):\n",
    "    plt.text(x = v + 0.2, y = i - 0.1, s = str(round(v, 2)), color = 'blue', fontweight = 'bold')\n",
    "plt.yticks(r, group.index)\n",
    "\n",
    "# 3g\n",
    "data = df.mask('Test_type', 'Download').mask('Technology', '3G')\n",
    "temp = data.groupby(by=state)[speed].mean()\n",
    "# get correct positions of width according to previous sorting\n",
    "for v in group.index:\n",
    "    group.head(len(values))[v] = temp.head(len(values))[v]\n",
    "    \n",
    "\n",
    "plt.barh(y = r, width = group.head(len(values)), color = 'y', label = '3G')\n",
    "for (i, v) in enumerate(group.head(len(values))):\n",
    "    plt.text(x = v + 0.2, y = i - 0.1, s = str(round(v, 2)), color = 'yellow', fontweight = 'bold')\n",
    "\n",
    "\n",
    "plt.margins(x = 0.15)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "dac288e8ec84b8c98aa9625ee6f0d308891d0cfc"
   },
   "source": [
    "Average Download Speeds for different states for both 4g and 3g technologies.\n",
    "* Average speeds of 3g networks lies between 1-3 Mbps. This is a lot less than what a 3g network should offer.\n",
    "* Average speeds of 4g networks lies between 7-26 Mbps.\n",
    "* The highest 4g network speed occurs for Himachal Pradesh which is explained by the speed distribtution of Himachal Pradesh for all providers. Since the number of samples are less, hence the average speeds hit the max value compared to other states.\n",
    "* The lowest 4g speed occurs for north east states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "9edaf9c961bcf17cf6c2ca5e33b97d4b4a8c276a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['JIO' 'VODAFONE' 'AIRTEL' 'IDEA' 'CELLONE' 'UNINOR' 'DOLPHIN' 'AIRCEL']\n"
     ]
    },
    {
     "data": {
      "image/png": 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/YyryXoyILrsA3YHFwE5AT+BJYGSTOl8EpmfrE4G5FY5xCLBntt4feL5IjPsB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\nIDMzy4UTkJmZ5eL/A7oabFbQbVqRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee73ef080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efee7368c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# avg speeds of states and service providers\n",
    "#columns = ['Technology', 'Test_type', 'Service Provider', 'LSA']\n",
    "\n",
    "#4g\n",
    "print(df[service].unique())\n",
    "data = df.mask('Test_type', 'Download').mask('Technology', '4G')\n",
    "values = data[service].unique()\n",
    "group = data.groupby(by=service)[speed].mean().sort_values(ascending = True)\n",
    "r = np.arange(len(values))\n",
    "\n",
    "plt.figure(figsize = (6, len(values)/2 +1))\n",
    "plt.barh(y = r, width = group.head(len(values)), label = '4G')\n",
    "for (i, v) in enumerate(group.head(len(values))):\n",
    "    plt.text(x = v + 0.2, y = i - 0.1, s = str(round(v, 2)), color = 'blue', fontweight = 'bold')\n",
    "plt.yticks(r, group.index)\n",
    "plt.xlabel(speed)\n",
    "plt.margins(x = 0.15)\n",
    "plt.legend(loc = 'lower right')\n",
    "\n",
    "#3g\n",
    "data = df.mask('Test_type', 'Download').mask('Technology', '3G')\n",
    "values = data[service].unique()\n",
    "group = data.groupby(by=service)[speed].mean().sort_values(ascending = True)\n",
    "r = np.arange(len(values))\n",
    "\n",
    "plt.figure(figsize = (6, len(values)/2 +1))\n",
    "plt.barh(y = r, width = group.head(len(values)), color = 'g', label = '3G')\n",
    "for (i, v) in enumerate(group.head(len(values))):\n",
    "    plt.text(x = v + 0.02, y = i - 0.1, s = str(round(v, 2)), color = 'green', fontweight = 'bold')\n",
    "plt.yticks(r, group.index)\n",
    "plt.xlabel(speed)\n",
    "plt.margins(x = 0.15)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f371cdc67ae75b30da2f3dabb2b5bc38d88eded3"
   },
   "source": [
    "Finally bar plots for average 4g and 3g network speeds for each provider.\n",
    "* In 4g networks, jio is the one with max average speed followed by airtel.\n",
    "* In 3g networks, Aircel is the one with max average speed, which is doubtful. But the speed distribution of aircel reveals very less numbers of customers, hence the higher speeds.\n",
    "* Other than Jio all the service providers are giving average 4g speeds of 6-8 Mbps which is much lower than speeds that they should be giving.\n",
    "* For jio also, most of the customers have lower speeds, giving an exponential decrease in number of customers as we increase network speeds."
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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