Pricing_Test_Part_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pricing Test Solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement:\n",
    "\n",
    "Should the company sell its software for \\$39 or \\$59?\n",
    "\n",
    "The VP of Product is interested in having a holistic view into user behavior, especially\n",
    "focusing on actionable insights that might increase conversion rate. What are your main\n",
    "findings looking at the data?\n",
    "\n",
    "[Bonus] The VP of Product feels that the test has been running for too long and he should\n",
    "have been able to get statistically significant results in a shorter time. Do you agree with\n",
    "her intuition? After how many days you would have stopped the test? Please, explain\n",
    "why."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "user_table=pd.read_csv('../user_table.csv')\n",
    "test = pd.read_csv('../test_results.csv')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, lets take a look at the data and spot any abnormalities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    275616\n",
       "city       275616\n",
       "country    275616\n",
       "lat        275616\n",
       "long       275616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             316800\n",
       "timestamp           316800\n",
       "source              316800\n",
       "device              316800\n",
       "operative_system    316800\n",
       "test                316800\n",
       "price               316800\n",
       "converted           316800\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=test.drop_duplicates('user_id')\n",
    "a.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    275616\n",
       "city       275616\n",
       "country    275616\n",
       "lat        275616\n",
       "long       275616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_table.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id    275616\n",
       "city       275616\n",
       "country    275616\n",
       "lat        275616\n",
       "long       275616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=user_table.drop_duplicates('user_id')\n",
    "a.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good news! There are no duplicate user_ids in the test and user_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "minute must be in 0..59",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/pandas/tseries/tools.py\u001b[0m in \u001b[0;36m_convert_listlike\u001b[0;34m(arg, box, format, name, tz)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 410\u001b[0;31m                 \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtslib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime_to_datetime64\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    411\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.datetime_to_datetime64 (pandas/tslib.c:33358)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Unrecognized value type: <class 'str'>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-693827588047>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimestamp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'raise'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/pandas/util/decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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     92\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/pandas/tseries/tools.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)\u001b[0m\n\u001b[1;32m    419\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mABCDataFrame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMutableMapping\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/home/jeremy/anaconda3/lib/python3.5/site-packages/pandas/tseries/tools.py\u001b[0m in \u001b[0;36m_convert_listlike\u001b[0;34m(arg, box, format, name, tz)\u001b[0m\n\u001b[1;32m    411\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    412\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 413\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    414\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/pandas/tseries/tools.py\u001b[0m in \u001b[0;36m_convert_listlike\u001b[0;34m(arg, box, format, name, tz)\u001b[0m\n\u001b[1;32m    399\u001b[0m                     \u001b[0mdayfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdayfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    400\u001b[0m                     \u001b[0myearfirst\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myearfirst\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m                     \u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrequire_iso8601\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    402\u001b[0m                 )\n\u001b[1;32m    403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.array_to_datetime (pandas/tslib.c:46691)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.array_to_datetime (pandas/tslib.c:46307)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.array_to_datetime (pandas/tslib.c:46196)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.parse_datetime_string (pandas/tslib.c:35434)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/dateutil/parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(timestr, parserinfo, **kwargs)\u001b[0m\n\u001b[1;32m   1162\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparserinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\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   1163\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mDEFAULTPARSER\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimestr\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   1165\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/jeremy/anaconda3/lib/python3.5/site-packages/dateutil/parser.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, timestr, default, ignoretz, tzinfos, **kwargs)\u001b[0m\n\u001b[1;32m    575\u001b[0m                 \u001b[0mrepl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'day'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmonthrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcyear\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmonth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mrepl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweekday\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: minute must be in 0..59"
     ]
    }
   ],
   "source": [
    "test['date']=pd.to_datetime(test.timestamp,errors='raise')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is an issue with the timestamp format on some entries.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1729</th>\n",
       "      <td>383035</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3025</th>\n",
       "      <td>594095</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5202</th>\n",
       "      <td>860346</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9164</th>\n",
       "      <td>794954</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14125</th>\n",
       "      <td>94635</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14893</th>\n",
       "      <td>982735</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000</th>\n",
       "      <td>113977</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25889</th>\n",
       "      <td>422311</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26941</th>\n",
       "      <td>371355</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29024</th>\n",
       "      <td>66603</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31381</th>\n",
       "      <td>503732</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32268</th>\n",
       "      <td>286286</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35435</th>\n",
       "      <td>735454</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37887</th>\n",
       "      <td>700138</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39303</th>\n",
       "      <td>390411</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44443</th>\n",
       "      <td>978391</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47625</th>\n",
       "      <td>857079</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50331</th>\n",
       "      <td>477278</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50444</th>\n",
       "      <td>775366</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55248</th>\n",
       "      <td>566943</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57394</th>\n",
       "      <td>724096</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57928</th>\n",
       "      <td>174352</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66026</th>\n",
       "      <td>216173</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66082</th>\n",
       "      <td>752403</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69689</th>\n",
       "      <td>639075</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72621</th>\n",
       "      <td>357569</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78832</th>\n",
       "      <td>640603</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82429</th>\n",
       "      <td>786119</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82932</th>\n",
       "      <td>36944</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83074</th>\n",
       "      <td>481342</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215906</th>\n",
       "      <td>859410</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220044</th>\n",
       "      <td>642953</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221223</th>\n",
       "      <td>147464</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221244</th>\n",
       "      <td>706237</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221993</th>\n",
       "      <td>142251</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222068</th>\n",
       "      <td>322909</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222258</th>\n",
       "      <td>179401</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224505</th>\n",
       "      <td>484072</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225119</th>\n",
       "      <td>748287</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225126</th>\n",
       "      <td>149358</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226074</th>\n",
       "      <td>530888</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233549</th>\n",
       "      <td>719834</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233786</th>\n",
       "      <td>403952</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234233</th>\n",
       "      <td>520008</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234565</th>\n",
       "      <td>740654</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238901</th>\n",
       "      <td>889901</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240410</th>\n",
       "      <td>769297</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241163</th>\n",
       "      <td>7770</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241815</th>\n",
       "      <td>338160</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244374</th>\n",
       "      <td>717400</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244676</th>\n",
       "      <td>279738</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244757</th>\n",
       "      <td>855752</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245044</th>\n",
       "      <td>630876</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250444</th>\n",
       "      <td>195388</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250619</th>\n",
       "      <td>104904</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254551</th>\n",
       "      <td>518746</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260495</th>\n",
       "      <td>184166</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264484</th>\n",
       "      <td>267867</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269501</th>\n",
       "      <td>338492</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273654</th>\n",
       "      <td>473574</td>\n",
       "      <td>Saint Petersburg</td>\n",
       "      <td>USA</td>\n",
       "      <td>27.76</td>\n",
       "      <td>30.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id              city country    lat   long\n",
       "1729     383035  Saint Petersburg     USA  27.76  30.31\n",
       "3025     594095  Saint Petersburg     USA  27.76  30.31\n",
       "5202     860346  Saint Petersburg     USA  27.76  30.31\n",
       "9164     794954  Saint Petersburg     USA  27.76  30.31\n",
       "14125     94635  Saint Petersburg     USA  27.76  30.31\n",
       "14893    982735  Saint Petersburg     USA  27.76  30.31\n",
       "25000    113977  Saint Petersburg     USA  27.76  30.31\n",
       "25889    422311  Saint Petersburg     USA  27.76  30.31\n",
       "26941    371355  Saint Petersburg     USA  27.76  30.31\n",
       "29024     66603  Saint Petersburg     USA  27.76  30.31\n",
       "31381    503732  Saint Petersburg     USA  27.76  30.31\n",
       "32268    286286  Saint Petersburg     USA  27.76  30.31\n",
       "35435    735454  Saint Petersburg     USA  27.76  30.31\n",
       "37887    700138  Saint Petersburg     USA  27.76  30.31\n",
       "39303    390411  Saint Petersburg     USA  27.76  30.31\n",
       "44443    978391  Saint Petersburg     USA  27.76  30.31\n",
       "47625    857079  Saint Petersburg     USA  27.76  30.31\n",
       "50331    477278  Saint Petersburg     USA  27.76  30.31\n",
       "50444    775366  Saint Petersburg     USA  27.76  30.31\n",
       "55248    566943  Saint Petersburg     USA  27.76  30.31\n",
       "57394    724096  Saint Petersburg     USA  27.76  30.31\n",
       "57928    174352  Saint Petersburg     USA  27.76  30.31\n",
       "66026    216173  Saint Petersburg     USA  27.76  30.31\n",
       "66082    752403  Saint Petersburg     USA  27.76  30.31\n",
       "69689    639075  Saint Petersburg     USA  27.76  30.31\n",
       "72621    357569  Saint Petersburg     USA  27.76  30.31\n",
       "78832    640603  Saint Petersburg     USA  27.76  30.31\n",
       "82429    786119  Saint Petersburg     USA  27.76  30.31\n",
       "82932     36944  Saint Petersburg     USA  27.76  30.31\n",
       "83074    481342  Saint Petersburg     USA  27.76  30.31\n",
       "...         ...               ...     ...    ...    ...\n",
       "215906   859410  Saint Petersburg     USA  27.76  30.31\n",
       "220044   642953  Saint Petersburg     USA  27.76  30.31\n",
       "221223   147464  Saint Petersburg     USA  27.76  30.31\n",
       "221244   706237  Saint Petersburg     USA  27.76  30.31\n",
       "221993   142251  Saint Petersburg     USA  27.76  30.31\n",
       "222068   322909  Saint Petersburg     USA  27.76  30.31\n",
       "222258   179401  Saint Petersburg     USA  27.76  30.31\n",
       "224505   484072  Saint Petersburg     USA  27.76  30.31\n",
       "225119   748287  Saint Petersburg     USA  27.76  30.31\n",
       "225126   149358  Saint Petersburg     USA  27.76  30.31\n",
       "226074   530888  Saint Petersburg     USA  27.76  30.31\n",
       "233549   719834  Saint Petersburg     USA  27.76  30.31\n",
       "233786   403952  Saint Petersburg     USA  27.76  30.31\n",
       "234233   520008  Saint Petersburg     USA  27.76  30.31\n",
       "234565   740654  Saint Petersburg     USA  27.76  30.31\n",
       "238901   889901  Saint Petersburg     USA  27.76  30.31\n",
       "240410   769297  Saint Petersburg     USA  27.76  30.31\n",
       "241163     7770  Saint Petersburg     USA  27.76  30.31\n",
       "241815   338160  Saint Petersburg     USA  27.76  30.31\n",
       "244374   717400  Saint Petersburg     USA  27.76  30.31\n",
       "244676   279738  Saint Petersburg     USA  27.76  30.31\n",
       "244757   855752  Saint Petersburg     USA  27.76  30.31\n",
       "245044   630876  Saint Petersburg     USA  27.76  30.31\n",
       "250444   195388  Saint Petersburg     USA  27.76  30.31\n",
       "250619   104904  Saint Petersburg     USA  27.76  30.31\n",
       "254551   518746  Saint Petersburg     USA  27.76  30.31\n",
       "260495   184166  Saint Petersburg     USA  27.76  30.31\n",
       "264484   267867  Saint Petersburg     USA  27.76  30.31\n",
       "269501   338492  Saint Petersburg     USA  27.76  30.31\n",
       "273654   473574  Saint Petersburg     USA  27.76  30.31\n",
       "\n",
       "[145 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#these users actually are in Russia according to the lattitude and longitude\n",
    "user_table[(user_table['lat']==27.76) & (user_table['long']==30.31)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">39</th>\n",
       "      <th>count</th>\n",
       "      <td>202672.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.019889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.139620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">59</th>\n",
       "      <th>count</th>\n",
       "      <td>114128.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.015570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.123806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 converted\n",
       "price                     \n",
       "39    count  202672.000000\n",
       "      mean        0.019889\n",
       "      std         0.139620\n",
       "      min         0.000000\n",
       "      25%         0.000000\n",
       "      50%         0.000000\n",
       "      75%         0.000000\n",
       "      max         1.000000\n",
       "59    count  114128.000000\n",
       "      mean        0.015570\n",
       "      std         0.123806\n",
       "      min         0.000000\n",
       "      25%         0.000000\n",
       "      50%         0.000000\n",
       "      75%         0.000000\n",
       "      max         1.000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test[['price','converted']].groupby('price').describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeremy/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price_x_converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.775682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.918644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price_x_converted\n",
       "price                   \n",
       "39              0.775682\n",
       "59              0.918644"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=test[['price','converted']]\n",
    "a['price_x_converted']=a['price']*a['converted']\n",
    "a[['price','price_x_converted']].groupby('price').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.92225772946e-09\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "tmp_a=a[a['price']==39]\n",
    "tmp_b=a[a['price']==59]\n",
    "t_stat,p_value=ttest_ind(tmp_a['converted']*39/59,tmp_b['converted'],equal_var=False)\n",
    "print(p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I multiply group A by the price ratio to scale it even with group B.  That way I can compare the two conversion rates.\n",
    "The p value is very small indicating strong evidence that the company should sell the software for $59."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAJECAYAAADt8BmRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff0ec10ff98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "a=test[['source','converted','price']].groupby(['source','price']).mean()\n",
    "a=a.reset_index()\n",
    "xlabels=a.source[a['price']==39]\n",
    "with sns.plotting_context(\"notebook\",font_scale=1.5):\n",
    "    sns.set_style('whitegrid',{'figure.figsize':(40,40)})\n",
    "    graph=sns.barplot(x='source',\n",
    "                 y='converted', hue='price',\n",
    "                  data=a,\n",
    "                 \n",
    "               ) \n",
    "    graph.tick_params(labelsize=9)\n",
    "    graph.set_xticklabels(xlabels,rotation=45)\n",
    "    sns.plt.title(\"Average Conversion by Source and Price\",fontsize=22)\n",
    "    plt.legend(loc='upper left',fontsize=10)\n",
    "    plt.ylabel(\"Average Conversion\", fontsize=18)\n",
    "    plt.xlabel(\"Source\", fontsize=18)\n",
    "    plt.rc('savefig',dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This graph provides a lot of insight into what sources to focus on, friend referral is by far the best source for \n",
    "conversion.  Direct traffic is quite weak which is probably expected.  There is a large discrepency in conversion for the 39 and 59 pricings for seo-bing and seo-yahoo sources which should be looked into.  Ads-bing is quite a weak source and other sources should probably be considered as a replacement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeremy/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAIPCAYAAABuRih6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xtczvf/P/DHe9WV6JOU89lCUYkJIybmWE6ZEPNxaMPG\nxjYbHyOb0zBmzBzKaLZhctgMQ6mcEmEiiUk5lUPn0rnevz/6Xu/f+9J1dbhcdSWP++222+p6v673\n6/lyXV3X8/16vw6CKIoiiIiIiIgIAPCavgMgIiIiIqpKmCATEREREckwQSYiIiIikmGCTEREREQk\nwwSZiIiIiEiGCTIRERERkQwTZCIiIiIiGSbIREREREQyTJCJiIiIiGSYIBMRUZWwYcMG2NjYwMbG\nBnFxcfoORysHDhyQ2vDHH3/oO5xXSnV4/1DVYajvAIj0ZenSpfj1118BAEZGRjhz5gxq166t56hI\nl86dO4fg4GCEhYXh6dOnSE5OhpGREerUqYPWrVujR48ecHV1Rb169fQdKv0fQRAgCIK+w3hhVbkN\nDx8+xNtvv63xeM2aNVG7dm00adIEHTt2hJOTE7p3716JEWqvurx/SP8EURRFfQdBVNlyc3PRq1cv\npKWlSY/Nnz8fEyZM0GNUpCuXL1/GN998g2vXrgHQnKyIogiFQoEJEyZg1qxZUCgUlRkmPSc/Px95\neXkAABMTEz1Ho50DBw7gf//7HwRBwDfffIMRI0boO6RilAlyWRJJZYrQqlUrfPzxxxg8eHBFh6e1\n6vD+oaqDPcj0Sjp+/DhSU1Px2muvwd7eHuHh4di/fz8T5GrgwIEDWLhwIfLz8yEIAnr27ImhQ4fC\nzs4OdevWRU5ODqKjoxEQEIB9+/YhOzsbP/30E65evQpvb29+seqRoaEhDA35tVSZnJ2d8d1336k8\nlpGRgcTERFy9ehUBAQE4c+YMYmJi8Mknn+D06dNYunQpXnut6o3Q5PuHdKnqvcOJKsHevXshCAI6\ndOiAMWPGAACioqIQGRmp58joRZw5cwbz589HQUEBTE1NsXnzZvj4+GDYsGF4/fXXYWZmhnr16uHN\nN9/EggUL8Ndff6FNmzYAgIsXL2LBggV6bgFR5TIwMICJiYnKf/Xq1YONjQ1Gjx4Nb29v7N+/H61a\ntYIgCDhw4AC++uorfYdNVOGYINMr5/79+zh//jwAYPDgwejXrx+MjIwAAPv27dNnaPQCnj17hnnz\n5gEo+tL38fFB7969S3xO06ZNsW3bNlhYWEAURRw5cgQXL16sjHCJXho2Njbw8/NDq1atIIoi/Pz8\ncPToUX2HRVSheC+CXjl79+6FKIowMDCAi4sLzMzM4OzsDH9/fxw6dAhz585VOxb1yJEj+PTTTwEA\nM2fOxMyZM0usZ8OGDdiwYQMA4Pvvv8egQYNUjt+6dQs7d+7EhQsXEB8fDwCwtLREx44d4ebmBicn\nJ7XnVY5xBIoS+jp16mDFihU4c+YM8vPz4e/vjwYNGqg8JyAgAH/99RciIiKQmJiIgoICmJubo337\n9hg2bBhcXV1LbEtKSgq2bt2KoKAgxMXFQaFQwMrKCqNHj8bw4cNRUFAAOzs7AICbmxu++eYbtefR\nts1l4efnh4SEBAiCgIkTJ6JTp05lel7dunUxY8YM7Nu3D0OHDkXLli3Vlnvw4AF+/fVXhIaGIi4u\nDpmZmTA3N4eVlRX69OmDMWPGqB2eIZ8QNXfuXEyePBk3b97E9u3b8c8//+DRo0cwMjKClZUVhg0b\nBg8PD5Xb1y4uLrhz5w5MTEwQGhoKY2NjjW3JycnBm2++iaysLLRu3RqHDh1SOZ6bm4t9+/YhICAA\nUVFRSE1NhZmZGZo2bYq+ffvCw8ND40TVvn37Ii4uDs7Ozti8eTP27NmDrVu3Ii4uDqNHj4aXl5dU\nNjs7G7t370ZQUBBu376N1NRUGBgYwNLSEra2thg6dCgGDBhQrA7530xgYCAaN25crMyzZ8+wc+dO\nnDx5Enfu3EFaWhpq1aqFJk2awMnJCePHj0fDhg3L1IakpCT8/PPPCAwMRFxcHAoKCtC4cWP06dMH\nU6dO1dmk3b///ht+fn64desWUlNTUadOHXTp0gVTpkyBra2tVC4+Ph59+vQBAHTr1g0///xziee9\nefMmhg8fDgAYMWIEVqxYoZN41TE1NcUPP/yAoUOHQhRFfP/99xgwYIDGoRYnT57EH3/8gfDwcCQk\nJMDY2Bj169eHk5MTxowZAysrK5Xy3t7e0lCPb775Bm5ubiXG87///Q8HDhwAUPS3b29vX6b3T0ZG\nBvbt24fjx48jNjYWaWlpsLCwgKOjIzw8PODo6FhiveVtF728mCDTK6WwsBB//PEHBEHAm2++Ka1e\nMGLECPj7+yMtLQ0BAQFwcXEp9tw+ffrAxMQE2dnZOH78eKkJ8rFjxyAIAmrVqoW+ffuqHFu3bh02\nb94MURRVJso8fPgQDx48wKFDhzBkyBCsXLkSBgYGas8vCAKysrIwZ84cxMbGSo8VFBRIZXJzc/Hx\nxx8jODhYOq6UkJCAkydP4uTJkzh48CB+/PFHteP3YmJiMGHCBCn5BIoSoH/++QeXL19GYGAglixZ\nUuz8z9NFm0vy+++/AyjqPZ40aVK5njt+/HiMHz9e4/Fdu3Zh+fLlyMvLU4k9MTERiYmJOH/+PLZt\n24YtW7agXbt2as+hfN6RI0cwd+5c5OfnS8dyc3MRHh6O8PBwXLx4EWvXrpWOubi4YMOGDcjOzsap\nU6fQv39/jXGePHkSWVlZEAQBw4YNUzkWHR2NDz/8EHfv3lVpQ3JyMpKTk3H16lX88ssvWLt2Lbp2\n7VpiG/bt2wcvLy/p96ysLKnMgwcPMGnSJDx48EClnoKCAsTHxyMuLg7+/v5wdnbGDz/8IN29UVfP\n865cuYIPP/wQSUlJKmXS0tKQlpaGyMhI7NixA19//bXGyXHK592+fRtTpkzB06dPVY7HxMTgzp07\nOHHiBPbu3QtTU1O15ymrr7/+Grt27VKJ9+nTpzh8+DCOHj2Kb7/9Vvq8adSoEbp27YoLFy4gLCwM\njx490pjsA5B6cQVBkBLlimRlZYXBgwfj8OHDuHv3Ls6fP19sdYuMjAx89tlnOHnypBQbAOTl5SEj\nIwPR0dHYtWsX5syZg4kTJ0rPc3V1xXfffQdBEHD8+PESE+SCggIEBgZCEAS0aNEC9vb2Ksc1vX/C\nw8MxY8YMlc8yAHjy5AkOHz6Mw4cPY+LEiVIHhC7aRS8vDrGgV8qpU6fw+PFjAMA777wjPd67d29Y\nWFgA0DzMwsTEBH369IEoivj3339x7949jfXExMTg33//BQD0799fpUfa29sbmzZtAlB063L9+vUI\nCAhASEgItm3bhh49ekAQBBw+fLjUsX779+9HQkICvv32W5w9exbBwcGoX7++dPy7775DcHAwBEFA\njx494Ovri9OnTyM4OBhbt26Fg4OD9O/yww8/FDt/YWEhZsyYgcTERAiCgAEDBmDPnj0IDQ3FgQMH\n4ObmBn9/f2zZsqXEOHXZZnUSEhIQExMDQRDg6Oio02Xbjh07hq+//hr5+fmoV68eFi9eDH9/f1y4\ncAF//fUXpk+fDkNDQzx58gSTJ09GUlKSxnPdvn0b//vf/9C5c2f8/PPPOHfuHE6fPo21a9fC0tIS\nQFHSo/wSBqBysXb8+PFSY1WS3xVITk7GlClTcPfuXRgaGsLT0xN+fn4IDQ3F0aNH8cUXX6BWrVpI\nTEzEBx98gDt37misIz8/H+vWrcOAAQPw119/4cKFC/j888+l43PnzsWDBw9gYGCAqVOn4sCBAwgJ\nCcHp06exfft2qYf05MmTUm9fWTx8+BCenp5ITk6GQqHABx98gIMHD+LChQsICAjA119/LU3CnD9/\nPkJCQjSeKycnBx999BGMjY2xZs0anDp1CqGhofjll1+kZOvu3bvSe1ZbR48exa5du+Du7g4/Pz+c\nP38e/v7++OSTT2BkZITCwkL873//w6NHj6TnKBNDURTx559/lnp+ANK4+sogvxN25syZYsc/+eQT\nnDx5EoIgoH///ti+fTvOnDmD4OBgrF69Gi1atEBeXh5WrFiBv/76S3pekyZN4ODgAFEUERISonLR\n9bxz584hNTUVADBkyJAyxR0XF4epU6ciMTERFhYWWLp0KYKCgnDmzBls27YNHTt2BADs2LEDW7du\n1Vm76CUmEr1CZsyYIVpbW4tdunQRc3JyVI598803orW1tdi+fXsxPj5e7fP9/f1Fa2tr0cbGRvTx\n8dFYz8aNG6VyZ8+elR6Pj48XbW1tRRsbG3HkyJFiVlaW2udPmzZNtLa2Ftu1aydevXpV5dj+/ftF\na2tr0draWuzQoYMYEBCg9hyZmZlix44dRRsbG7F///5idnZ2sTKpqalily5dpH+TwsJCleOHDh2S\n2vHee++prWfVqlViu3btRBsbG9HGxkacN2+eynFdtLk0AQEBUpxr1qwp13NLkpeXJzo5OYk2NjZi\nx44dxXv37qktt3PnTqn+hQsXqhx78OCBdMzGxkacOnWqWFBQUOwcR48elcrNnz9f5djw4cOl1ygv\nL09tDDk5OWKnTp1EGxsbcezYsSrHFi5cKJ378OHDap9/+fJl6XWaNm1aseN9+vQRbWxsRHt7e9HN\nza3Ye0UUi15rZT1LlixRW48oFr3WNjY2Yrdu3VTO88MPP0jPf/jwocpzPvroo1LbEBUVJbVh0KBB\nGttgY2Mj9u7dW0xMTCxW5vHjx9I5+vXrp7ENmij/PpX1rFixQm25bdu2SeWWL18uPf7s2TPp73bw\n4MEa67l586b0fE11aCJ/T86YMaNcz01OTpae++6776ock39eLFq0SO3zk5KSRGdnZ9HGxkZ0cnIS\nMzMzpWO+vr7S848cOaIxhi+//FIqFxsbKz1e0vtn9uzZorW1tWhrayveuHGj2DmfPXsm9u/fX7S2\nthbfeOMNMT09XWftopcTe5DplZGYmCj1prq4uBQbZ6zsuSksLNTYi/zWW2/hP//5DwDA399fY13K\nnjxLS0uVW5C//vqrdGv9yy+/RI0aNdQ+X3mLTxRFjbEIgoBGjRppXPA/NTUVLi4u6N27N8aNG6d2\n7KqZmRl69eoFAEhPTy/Wcyhv44cffqi2nlmzZqFu3bpqjwG6bbMmCQkJ0s/NmjUr13NLEhwcLJ17\n3LhxGs89duxYNG7cGKIo4u+//5bWYpUT/29oyYIFC9SO23R2dpYev337tsoxZS9yeno6zp07pzaG\nU6dOITMzEwAwdOhQ6fH09HSVYUXqhg8BQKdOnTB48GCIoohTp04VG3qgbENeXh48PT3V3saW94SW\nNBbzq6++wu7du3H48OEyrcWblJQk3VK3tbXV2AZra2u4urpCFEXExsbi6tWratsAAB9//LF010iu\nfv36sLOzgyiKePDgAXJyckqNTx1RFGFsbIwZM2aoPT5u3DjUqlULQNEcAaWaNWuif//+EEURMTEx\natsAQGWS3PPDaSqSubm59FmSnJysckw5ZtrMzAxffPGF2ufXqVMHH374IURRRGJiIoKCgqRjgwcP\nlv4GNH2+FhQU4MSJE9J7oUWLFqXGnJKSAn9/fwiCgN69e8PGxqZYmZo1a8Ld3R0GBgYwNjbG5cuX\nddYuejkxQaZXxv79+6VEbeTIkcWOW1tbo3379hBFUZr88TyFQoF+/fpBFEVcu3ZNGq4hd+/ePURF\nRUEQBLi6uqokAMrbvpaWlnjjjTc0xtqiRQu0adMGoiji7NmzGst169ZN47GGDRti2bJl2Lx5c4lj\ncps0aSL9nJKSonJM+eVcq1Yt6Rbk8xQKhZSUqKPrNqujvN0KQLqA0YXQ0FDp5379+mksJwiCdKGR\nkZGBmzdvqi3Ttm1bjUm2sbEx6tSpA0C1PYDqcAlNiYPyoszAwEBlM4eLFy8iNzcXADBw4ECNbQAg\njW8W/+82tyaa3nfKYSJA0WRS+UY8cg0bNoSDg4NK+ZJcunRJ+tstaQw2AJWVS0pakaSk17NRo0bS\nz5raUBpBENClSxeNY5iNjY2lIQVxcXEq9cjHT2saZqF8va2srDSOe68oZmZmEEVR5fMiIyMDERER\nEAQBTk5OqFmzpsbny//t5X/r9evXR+fOnSGKIk6ePKn2QvPChQtSYl7WC4Pz589L758ePXpoLOfp\n6Ynr168jJCQEb731ls7aRS8nJsj0ylD2SlpZWaFDhw5qyygT54cPH6okR3LK3itRFFV6fpTkPTvy\n8XGiKOLmzZsQBAENGzZEZmZmif8pe+AePnyosRfr+dUqtCHvSS8sLJR+zsvLw6NHj6SJMCX19HXu\n3Fnt4xXRZnXkPbLyNryoW7duST8r10vWpHXr1tLPz/cAK5XW22VsbCz10srJx2cGBgYWe15ubi6C\ngoKkL3Flog1AZW3vunXrlvjvL0/eo6Oj1cZoYGCg8Y5Bs2bN0KlTJ4iiiKtXr2LIkCFYt27dC68v\nLr/gKO11kPdcK+cBPM/c3BxmZmYazyG/y6EuSSurtm3blni8adOm0s93796Vfu7evTsaNGgAURRx\n+PBhlQmdQNH7Kzo6Wu1kzMqgvOCSf3ZERUVJf3v169cv8X2mUCik99Dz7zPlxWBmZqbaJPPvv/8G\nUPQ3r+lOwvPkf4/NmzfXWE7dnR1dtYtePlzFgl4JFy9eRGxsbKmzvV1dXbFy5Urk5+dj3759aie+\n9OjRA3Xq1EFKSgqOHz9ebPUDZYLcvHlzldnV6enpKCgogCAIiIiIKLE3VU55y07dkkXyREiT2NhY\n7NmzB5cvX8aTJ0+QlJSE7OzsUp+Xnp6OwsJCCIJQaj3yL/rnz6HrNqsjX47r+V7wF6HsqTI0NCx1\nNQNzc/NSYyip96k0Li4uCA8PR2JiIi5evKiyHNXZs2eRkZEBQRCKTVqSx1LayitKoiiqHWIBoNSl\nz7777ju8//77iI6OxtOnT7Fp0yZs2rQJ5ubm6NGjB/r164e+fftqHGqjjrwN6oZFyMlfh+d74pVe\n5HUoj9Imi8r/LTMyMqSflYmvj48PUlNTERwcrNI7KV+9Qj6cprKkp6cX+1yQv0a+vr7w9fUt07me\nf58NHDgQS5cuRUFBAY4fPw5nZ2fpWGFhoTS8olu3biUO7ZJLTEyUfi7v0n26ahe9fNiDTK8EPz8/\nAEVf/GvWrIGNjY3a/7p37468vDypd1j+paVkYGCAAQMGQBRFXLp0SeUD9MGDB4iMjFT7xSWflS0I\nQrn+e/bsmdp2lbYtsq+vL1xdXbFt2zaEh4cjPj4eOTk5xc6vjrKXCIDadaHlNCUcFdFmdeSJtKbe\nW20oe7FLWntYSV6mLBcg5SUfn/n8ahbK2+01atQoNnRA29dAOZ75eaUlto0aNcKff/6JJUuWwMbG\nRjpfamqqtJZ43759pb/JspD/e5b2WlT061AepcUqX+JO/vcGqA6z+OOPP1SOHT16FIIgoHPnzirD\nQSrD7du3pR5Ved3y98uLvM/q1KmDN998E6IoIigoSOWOUFhYmJTslufCQP5vW96tqHXVLnr5sAeZ\nqr2MjAwcP368TJOBlERRRHZ2Ng4dOoSxY8cWO+7q6orff/9dmjCiXDJO0/AKQDWJfOutt0pdGu1F\nBQcHY8WKFRAEAUZGRnB3d0f//v3RokULmJmZSWsNb968WW0s8i/v0m4za1qSqbLa7ODgIL2+utwJ\nT3kBUpZES16mInooleMzw8LCEBAQgPnz5wMoem2UE9j69u1b7KJJHoufn5+0oUtFMjAwwKhRozBq\n1Cg8fvwYp06dwunTpxESEoJnz54hKSkJCxcuRHp6OqZMmVLq+eRtKmn5LwAqQ3Mqq6dYk+eT3ufJ\nY30+mbaysoKdnR0iIiJw8uRJpKWlwczMDNHR0bh9+7behleEhYVJP8vHosv/refMmQNPT0+t63B1\ndcWZM2eQkpKCCxcuSHfylJ+vxsbGpY5Fl5Nf1Gm6q6CJLttFLxcmyFTtHTx4UNo8YcaMGcUWlVdn\n7ty5SE1Nxb59+9QmyF26dEH9+vXx9OlT+Pv7SwmysifP1tYWLZ/bkc3U1BQKhQJ5eXklrpWrK/Jb\ngatWrVKZuFUWyhn2QNEt1ZLIVy+Qq6w2m5qawt7eHlevXsW///6LGzdulHvi0m+//Ya+ffuq9Iop\nb+cXFBRICYom8hn98tv8uuTq6oqwsDDEx8cjIiICdnZ2CAkJQVpamsaEST4RTn6rubI0aNAA7u7u\ncHd3R25uLvbu3YtVq1YhJycHP/zwA9zc3EodwiMfVlHa+6gyXoeyKm24jzxZU3fr383NDREREcjP\nz8eJEyfg5uaGI0eOACi6q/P87pyVYf/+/dLPPXv2lH6Wv89e9G+9f//+WLRoEfLy8uDv7y/1KMtX\noijPBi7yoRjPr7xRGl22i14uHGJB1Z5ycl6tWrXw/vvvo3fv3qX+p1zuKiIiQu0te0EQMGjQIIii\niHPnziEnJwdxcXG4du2axkRFEARYW1tDFEVER0cXm3ijazdu3ABQ9AFfUnKsbsUFoKjXpU6dOhBF\nscRNUQCoLIkkV5ltll/IqNv0pCQnT57EkiVLMHDgQOzcuVN6XL4cVFRUVInnkE8IU7eMlC4MHDhQ\nukWsnKyn7FUzNzdXSVjUxVJaGyqaQqHAuHHj8NFHH0l3aTS9d+TkFzultUE+sbKiXoeyKm24j3xi\n3vMX1EDRBZHy9VZOTlMujde7d2+drthSFidPnpQ+49566y20atVKOmZtbS0NAXrR95mpqSl69eol\nDbMAiu4MKZdcLO+4a/nEzpiYGI3lMjMzcenSJVy6dAlxcXEAdNsuerkwQaZqLSoqCtevX5d2PyrL\nWFJA9QN47969assoZ1vn5ubiwoUL0rjQkmZXOzk5ASi6TaxuBQw5Pz8/hISEaD2LPjMzE4IglNjT\nEhMTo7Ib1vNLtSkTjOTkZI0rAuTl5eHgwYMa66isNg8ZMgQtW7aUvlTLOsb1/v378PLyAlDUUyy/\nwyBfEqqkda8LCgqk7bwtLS1LXWlBW/LxmWfPnkVhYaE0vGLw4MFqt+ju3LmzNETh0KFDJZ4/NjYW\nO3fuxMOHD7WK7/r169i5c2ep9chXdyjLa/3GG29If7slvQ4AcOLECennytpdTh1RFBEaGqpxmEVm\nZiauXr0KQRBgZWWldj6Bubk5nJ2dpQvxc+fOSdvKV/bkvMePH2PBggUAij7jnl/fuWbNmujYsSNE\nUcSFCxfw5MkTjefKy8uDj49PiaubKD9f4+PjcefOHenz1czMTGUpv7Lo1q2bNGSspPWJjxw5Im07\nf/78+QppF708mCBTtbZnzx7p5/KM13vjjTfQtGlTiKKIgwcPoqCgoFgZBwcHaQ3h06dPS9sDlzS7\netSoUTA0NIQoili9erXGW3anT5+Gl5cXpkyZUq7teOUaNmwIURTx8OFDtfUkJiZi1qxZJd6+ln8R\nqdt+FShatUC+UcfzKqvNCoUCq1atkupatGgRfH19Na7PDBT18E2cOBGPHz+GIAiYOXOmSoLcs2dP\nNGvWDKIoYu/evSo9fnLbtm1DQkICBEHA6NGjyzXevbyUiUNERASCg4NL3XLX1NQUQ4YMgSiKuH37\nNrZt26a2XG5uLhYtWoTFixdj6NChpQ6rUWfTpk1YvHgxlixZonHYDQCVzU7KcjFhZmYGFxcXiKKI\nW7duadzK959//pFuw3fu3Fll6b3KJggCMjIyNI673759uzQGuaT1qZWT9fLz87Fo0SIARf8e8tUd\nKtrVq1fh4eGBp0+fQhAEzJo1S+1SmR4eHlKsX331lca/vQ0bNmDNmjV45513NK633adPH+miQfn5\nKggCBg4cqDI/oixMTU2lO37Xr1+XeuPlcnNzpbtHJiYmKhsw6bJd9PJggkzVVm5uLg4dOgRBEFCv\nXj2VHe3KQplwJCcnq117FoD0pR0cHIxLly6VuuxS06ZNpaW2Hjx4AHd3dxw8eBCPHj1CUlISIiIi\nsHLlSmlHpnr16mHy5Mnlilupb9++AIo+1GfOnIkrV64gJSUFMTEx2L59O4YNG4acnByp9xQo6sF9\n8uSJtHrHqFGjULt2bYiiiD///BNeXl64efMmUlNTERkZic8//xy+vr4lTrSqzDZ36NABGzduhImJ\nCURRxIoVK/DOO+/gt99+w61bt5CcnIyEhAScO3cOXl5ecHNzQ1xcHARBwKRJk/DBBx+onE8QBHz1\n1VcQBAFZWVl49913sW/fPjx69AhpaWm4ceMGli1bhu+++w5A0TrHU6dO1Sr2surfvz8UCgUKCwul\nehs3blziEnqffvop6tatC1EUsWrVKnh5eSEiIgIpKSm4f/8+jhw5gjFjxuD8+fMQBAHTpk3T6va9\ncoe91NRUjBkzBjt27MDt27eRnJyMp0+fIjw8HEuWLMG2bdsgCAK6d+9e4o57cp999hnMzc0hiiLm\nz5+PDRs24M6dO0hPT8fdu3fh6+uLqVOnorCwEAqFQkom9al///7YuHEjli1bhqioKKSlpeHevXv4\n4YcfsHHjRgBFyduECRM0nqN3797SWOp79+5pnSRqUlBQUGw9X+W/6d9//42PP/4YY8aMkf5OPD09\nNb7Hhw4dCicnJ2m97v/+9784e/YskpKS8PjxY5w7dw4fffQRtmzZAkEQ0KNHD40bd5iYmKBPnz4A\nij6XlMO85JvmlMenn34qfZZ98cUX2LJlC+7fv4/ExESEhIRg4sSJ0gpE06dPV5lvoMt20cuDk/So\n2jp69Kg0eUlT71pJhg4dis2bNwMoGsesbta0q6srfHx8pA/vssyunj59OrKzs+Hj44O4uDi1W5cK\ngoBmzZrhxx9/1Hqi0QcffIDAwEA8ePAAly9fLjbZsEWLFti6dSssLCxQu3ZtpKWl4ezZs3jrrbcw\nbNgwrFq1Cqampli9ejVmzpyJ3Nxc7NmzR6VXXhAETJw4Ee7u7iWuUFFZbQaKVsv4/fffsWzZMly4\ncAE3btzAkiVL1JYVhKLtuufNm6exF8/JyQkrV67EwoULkZCQgC+//FLteaytrbFly5Zyre+rDeX4\nzBMnTkiL5QCyAAAgAElEQVSrGZT2/q5Tpw5+/fVXTJs2Dffu3Sv2OgJFbXjttdcwceJETJs2TavY\nOnXqhMWLF2Px4sV48uQJli9frracIAjo0KEDVq9eXeZz161bF76+vpg2bRqePHmCDRs2FLvTIAgC\nzM3NsW7dugob5lIeQ4YMQZ06dfDLL7/gl19+UTkmCAKMjY3x/fffl/h+NzIygqurK3777TfpMV2t\nXqEcjlTSxZXybkjTpk3x+eefl7ob44YNGzB79mycOnUKFy9eVLvqg/Li6Pvvvy/xXC4uLjhy5Ig0\nlrt+/fol7h5akkaNGsHHxwczZ87E06dPsXbtWqxdu7ZYXOPHj1f7/tdlu+jlwASZqq19+/ZJa1Jq\n84ViZWWF9u3b48aNGzhz5gyePn1abOF/GxsbWFlZ4c6dOwAAZ2fnMs2unj17NlxcXLB7926cP38e\njx49Qm5uLszMzGBtbY3+/ftj5MiRGsdMl7R+sZK5uTn27NmDzZs3IygoCI8ePYKBgQGaNWuG4cOH\nY8yYMVKs69atwzfffIO7d++idu3aKkuB9erVC/v374ePjw/Onz+PhIQE1KxZEw4ODpg8eTK6d++u\nMmZVU1wv2ubyaNu2LX7++WdcunQJJ06ckMYOJicnS7td2dnZ4e2330b//v1L7Y0bNmwYunbtih07\ndiAkJAQPHz5EdnY2zM3N0b59ewwaNAjDhw9XuxOX/N+kLEMvyvLaurq6Snc1ynoB2LJlSxw+fBh7\n9+6Fv7+/dCfAyMgIjRs3hqOjI0aPHo327du/UGzu7u7o1q0bdu/ejQsXLuD+/ft49uwZDA0NYWlp\nCVtbWwwaNEjjOP2S6rCxscHRo0exa9cuBAUF4fbt20hPT4epqSlatWoFZ2dnjBs3rsTe77K0oTzl\nSnquQqHA119/ja5du2Lv3r34999/kZaWBktLS3Tv3h3vv/++ykQ3TUaMGIHffvtNuqCTbxKjrdLa\nZmZmBktLSzg4OKBXr14YOHCg2jHuzzMxMcGWLVtw8uRJHDx4EP/88w+SkpIgiiIsLS1hb2+P4cOH\nS3e4SvLWW2/BzMxMGu5Tlt7jkl63Dh064MiRI/j1119x4sQJ6b1pYWGBzp07w8PDA126dKnwdtHL\nQRBLGqBHRFQGt2/fxpAhQ6QeGOVkHiJ6cbm5uejcuTPy8/Mxffp0zJo1S98hEVV7VXoMclxcHKZN\nm4Zu3bqhb9++Jd6O27FjBwYNGgRHR0eMHz8e169fl47l5ORg2bJl6N27N7p27QpPT0+VGfnlqYeI\nilMuiQSorhtKRC/u6NGjyMvLgyAIGDlypL7DIXolVOkEeebMmWjYsCECAwPh6+sLf39/tfugBwYG\n4scff8S3336LkJAQODs7Y9q0adLOVt9++y0uX76MPXv24NSpU2jUqJE0aag89RC9amJjY7F+/Xp8\n/PHH0gYF6oSGhko/29raVkZoRK8EURSxdetWae3hZs2a6TskoldClU2Qr127hlu3buHzzz9HrVq1\n0Lx5c0yePLnYxBKgaCmvkSNHwt7eHgqFAu+99x4EQZDG6P3nP//B3Llz0aBBA9SoUQMTJ07EvXv3\n8PTp03LVQ/Sqyc3NxcaNG3H8+HFs2bJFZWtcpfj4eGnNYXNzc72uPUtU3fz444/S5icffvihnqMh\nenVU2QQ5MjISTZo0UZnw1L59e8TExCAzM1OlbEREhMrEEkEQ0K5dO1y7dg0AMGvWLHTt2lU6HhcX\nB2NjY9SuXbtc9RC9atq2bYsBAwYAKNpxb+LEiTh9+jQSEhLw5MkT/PXXXxg/fjzS09MhCAI+/PBD\nKBQKPUdN9PLKycnBs2fP8O+//2LBggXYsGEDBEGAm5ub2rWHiahiVNlVLFJSUlTWIQQgLYWTnJyM\nmjVrlli2du3aSElJKXbe1NRULF++HJ6enlAoFOWqR5P8/HykpqbC2NhY4yx2opfVl19+iadPn+LK\nlSsIDw/H+++/r3JcOWt83LhxeOedd3hhSfQCDh48iK+++kr6Xbkk3meffca/LaIXVFhYiJycHNSu\nXVvaxl2TKpsgA8W3vX1RT548wfvvvw9bW1uVMcgvWk9qaqq0/SdRdTRnzhycOXMGZ8+exd27d5GR\nkQEjIyOYm5ujbdu2ePvtt9G6dWvcuHFD36ESvdQSExNhYGAAURRRt25d9OzZE8OHD+d3DJEOtWzZ\nstQJ5VU2QbawsCjWA5ySkgJBEFS2xlWWTU5OLla2bdu20u/37t3DpEmT0LdvX3z55ZfSOonlqUcT\n5bqtjRo10skaruWRn5+vsiIHVX9t2rQp9cq3IrRs2RLvvvtupddL9Cpp2bJlsU19iEg3cnJyEB8f\nX6ZcrcomyHZ2doiPj0dKSoo05OHq1auwsrKS9meXl71+/bq0Z31hYSEiIyPh7u4OoGiohKenJ9zd\n3YttJVueejRRDqswNzcv05AMXQoLC8Oc5b+gtmWTSq2X9CM18SF++Po9jYvZE1UHubm5CA8P13cY\nVIkcHBw4f4EqXGZmJuLj48s0HLbKJsjt2rWDvb091qxZg7lz5+Lx48fw9fWVtnccNGgQli9fjjfe\neAMeHh747LPPMGTIEFhbW2Pr1q0wNjaGs7MzAGDNmjVwcHAolhyXpZ6XQW3LJrBsZKXvMIiIdCI8\nPBwfLdrKC/9XBC/8qSqqsgkyULT97cKFC9GzZ0+YmprCw8MDHh4eAIC7d+9KExZ69eqFTz/9FLNn\nz0ZSUhLs7e3h7e0tXY3u378fBgYGOH78OARBgCiKEAQBS5YswbBhw0qsh4iIKh8v/IlIn6p0gtyg\nQQN4e3urPfb8ZKCxY8dqHLcVGRmpdT1ERERE9GrhmmRERERERDJMkImIiIiIZJggExERERHJMEEm\nIiIiIpJhgkxEREREJMMEmYiIiIhIhgkyEREREZFMlV4HmYiIiIheTGVv314dtg5ngkxERERUjVXm\n9u3abh0eFRWFFStWICIiAjVq1ECXLl2wYMECWFpa4tChQ/D29sb9+/fRtGlTzJs3D05OThXUgiJM\nkImIiIiquaq8fXtubi48PT0xYcIE+Pj4ICMjAx9//DG++uorTJw4EfPmzcO6devQu3dvnDp1CrNm\nzcKhQ4fQsGHDCouJY5CJiIiISG+ys7PxySefYOrUqTAyMkKdOnUwYMAA3Lp1C4GBgejatSvefvtt\nGBoaom/fvujZsycOHjxYoTExQSYiIiIivTEzM8OoUaPw2mtFaemdO3dw4MABuLi4AAAEQShWPioq\nqkJjYoJMRERERHoXFxcHOzs7DBkyBB06dMDHH3+MPn364Pz58wgMDEReXh7CwsIQFBSE1NTUCo2F\nCTIRERER6V3jxo0RERGBo0ePIiYmBnPmzEGXLl3g5eWFlStXokePHti5cyfc3NxgYGBQobEwQSYi\nIiKiKqN58+b45JNPcPjwYSQnJ2P06NE4duwYwsLCsHbtWmRmZqJBgwYVGgMTZCIiIiLSm9DQUAwa\nNEjlMUEQIAgCsrOzcfjwYZVjZ8+eRadOnSo0JibIRERERKQ3dnZ2yMjIwLfffovs7GwkJSVhw4YN\ncHR0RF5eHubOnYvg4GAUFBRg06ZNyM7OlibwVRSug0xERERUzaUmPqyy9ZiammL79u1YvHgxunfv\njpo1a+LNN9/EsmXLUL9+fSxbtgyLFy9GcnIybG1tsXXrVtSoUaMCov//mCATERERVWMODg744ev3\nKrW+8mrTpg1++eUXtceGDx+O4cOHv2hY5cIEmYiIiKgaUygU5d76+VXHMchERERERDJMkImIiIiI\nZJggExERERHJMEEmIiIiIpJhgkxEREREJMMEmYiIiIhIhgkyEREREZEME2QiIiIiIhluFEJERERU\njeXm5iI8PLzS6nNwcIBCoai0+ioCE2QiIiKiaiw8PByfbJ4P8yaWFV5XysNErJ2+vNw799nY2ECh\nUEAQBIiiCEEQ4O7ujgULFuDQoUPw9vbG/fv30bRpU8ybNw9OTk4V1IIiTJCJiIiIqjnzJpao26qB\nvsPQSBAEHDt2DI0aNVJ5PCwsDPPmzcO6devQu3dvnDp1CrNmzcKhQ4fQsGHDCouHY5CJiIiISK9E\nUYQoisUeDwoKQteuXfH222/D0NAQffv2Rc+ePXHw4MEKjYcJMhERERHp3erVq9GnTx906dIFXl5e\nyMzMBFDUuyxnZmaGqKioCo2FCTIRERER6VXHjh3h5OSE48eP4/fff8eVK1ewePFi9OnTB+fPn0dg\nYCDy8vIQFhaGoKAgpKamVmg8TJCJiIiISK92796Nd955B0ZGRnj99dcxZ84cHDp0CB07doSXlxdW\nrlyJHj16YOfOnXBzc4OBgUGFxsNJekRERERUpTRp0gQFBQVISkrC6NGjMXr0aOnY0qVL0aBBxU44\nZA8yEREREenNjRs3sHLlSpXHoqOjoVAoUFhYiMOHD6scO3v2LDp16lShMTFBJiIiIiK9sbCwwO+/\n/w4fHx/k5uYiJiYG69evx5gxY5CXl4e5c+ciODgYBQUF2LRpE7Kzs+Hi4lKhMXGIBREREVE1l/Iw\nscrW06BBA3h7e2P16tXYtGkTjI2N4ebmhtmzZ0OhUGDZsmVYvHgxkpOTYWtri61bt6JGjRoVEP3/\nxwSZiIiIqBpzcHDA2unLK7W+8nJ0dMTu3bvVHhs+fDiGDx/+omGVCxNkIiIiompMoVCUe+vnVx3H\nIBMRERERyTBBJiIiIiKSYYJMRERERCTDBJmIiIiISIYJMhERERGRDBNkIiIiIiIZJshERERERDJM\nkImIiIiIZJggExERERHJMEEmIiIiIpJhgkxEREREJMMEmYiIiIhIhgkyEREREZEME2QiIiIiIhkm\nyEREREREMkyQiYiIiIhkmCATEREREckwQSYiIiIikmGCTEREREQkwwSZiIiIiEiGCTIRERERkQwT\nZCIiIiIiGSbIREREREQyTJCJiIiIiGSYIBMRERERyTBBJiIiIiKSYYJMRERERCTDBJmIiIiISIYJ\nMhERERGRDBNkIiIiIiIZJshERERERDJMkImIiIiIZJggExERERHJMEEmIiIiIpIx1HcARERE9Ooq\nLMhHZGSkvsOgSuTg4ACFQqHvMErEBJmIiIj0Jj35MXxOnYN5tKW+Q6FKkPIwEWunL0eXLl30HUqJ\nmCATERGRXpk3sUTdVg30HQaRhGOQiYiIiIhkmCATEREREckwQSYiIiIikmGCTEREREQkwwSZiIiI\niEiGCTIRERERkQwTZCIiIiIiGSbIREREREQyTJCJiIiIiGSYIBMRERERyTBBJiIiIiKSYYJMRERE\nRCTDBJmIiIiISIYJMhERERGRDBNkIiIiIiIZJshERERERDJMkImIiIiIZJggExERERHJVOkEOS4u\nDtOmTUO3bt3Qt29frF69WmPZHTt2YNCgQXB0dMT48eNx/fp1leN3797FyJEj0bNnz2LPtbGxQYcO\nHeDg4CD9f+nSpTpvDxERERFVfYb6DqAkM2fOhL29PQIDA5GYmIj3338fdevWxaRJk1TKBQYG4scf\nf8TWrVthbW2Nn3/+GdOmTUNAQABq1KiB0NBQfPHFF+jUqROePHlSrB5BEHDs2DE0atSoklpGRERE\nRFVVle1BvnbtGm7duoXPP/8ctWrVQvPmzTF58mTs2bOnWNk9e/Zg5MiRsLe3h0KhwHvvvQdBEBAY\nGAgASE1Nha+vL5ydndXWJYoiRFGsyOYQERER0UuiyibIkZGRaNKkCUxNTaXH2rdvj5iYGGRmZqqU\njYiIQPv27aXfBUFAu3btcO3aNQDAwIED8frrr5dY3+rVq9GnTx907doVXl5exeogIiIioldDlR1i\nkZKSAjMzM5XHzM3NAQDJycmoWbNmiWVr166NlJSUMtXVsWNHODk5YeXKlbh//z5mz56NxYsXY8WK\nFeWKOSsrq1zldSE7O7vS6yT9ys7O5gUcVWv8XCOq3vT1PVaePK3KJsgAKm3Yw+7du6WfX3/9dcyZ\nMwcffvghlixZAiMjozKfJzY2tgKiK1lMTEyl10n6FRMTo3KBSFTd8HONqHp7Gb7HqmyCbGFhUawH\nOCUlBYIgwMLColjZ5OTkYmXbtm2rVd1NmjRBQUEBkpKS0KBBgzI/r2XLljAxMdGqTm0VXYHFVmqd\npF+tWrVCu3bt9B0GUYXh5xpR9aav77GsrKwyd2ZW2QTZzs4O8fHxSElJkYZWXL16FVZWVsWSUDs7\nO1y/fh0jRowAABQWFiIyMhLu7u6l1nPjxg0cPHgQc+fOlR6Ljo6GQqFA/fr1yxWziYlJpV8R1ahR\no1LrI/2rUaNGlb/yJnoR/Fwjqt5ehu+xKjtJr127drC3t8eaNWuQkZGB6Oho+Pr6Yty4cQCAQYMG\n4fLlywAADw8P/PnnnwgPD0d2djY2btwIY2PjYqtWqBuyYWFhgd9//x0+Pj7Izc1FTEwM1q9fjzFj\nxkAQhApvJxERERFVLVW2BxkA1q1bh4ULF6Jnz54wNTWFh4cHPDw8ABRt/KEc4N2rVy98+umnmD17\nNpKSkmBvbw9vb28oFAoAgKenJ8LCwlBYWIiCggJ06NABgiDgp59+gqOjI7y9vbF69Wps2rQJxsbG\ncHNzw+zZs/XWbiJNCgvyERkZqe8wqBI5ODhIn2VERFQ5qnSC3KBBA3h7e6s9duPGDZXfx44di7Fj\nx6ot+9NPP5VYj6Ojo8pEPaKqKj35MXxOnYN5tKW+Q6FKkPIwEWunL0eXLl30HQoR0SulSifIRFSc\neRNL1G1V9smjREREVD5VdgwyEREREZE+MEEmIiIiIpLReohFQEAA/vjjD8TGxiI7O1vjph6CICAg\nIEDrAImIiIiIKpNWCbKfnx+8vLzKtNMdl0ojIiIiopeJVgnyjh07IAgCPvvsM2kJNibCRERERFQd\naJUgx8bGYvjw4Xjvvfd0HQ8RERERkV5pNUnP1NQULVu21HEoRERERET6p1WC/MYbb+DWrVu6joWI\niIiISO+0SpA//fRTnDlzBvv379d1PEREREREeqXVGOTQ0FC4ubnBy8sL27dvh62tLSwsLNSWFQQB\nn3/++QsFSURERERUWbRKkJcsWQJBECCKIv7991/8+++/GssyQSYiIiKil4lWCfKMGTO4rBsRERER\nVUtaJcgfffSRruMgIiIiIqoStN5qWi4vLw9paWkQBAFmZmYwNNTJaYmIiIiIKp3WmWxubi5+/vln\nHDp0CNHR0SgoKAAAGBgYwMbGBu7u7hgzZozOAiUiIiIiqgxaJchZWVmYNGkSrl69ClEU8dprr6FW\nrVooLCxEVlYWIiIicP36dQQFBWHjxo147TWtVpMjIiIiIqp0WiXIvr6+CA8PR69evTB9+nTY29tD\noVAAALKzs/HPP/9g48aNOHnyJHbt2oXx48frNGgiIiIiooqiVdfusWPHYGtrC29vb3Tu3FlKjgGg\nRo0a6N69O7Zv347WrVvj4MGDOguWiIiIiKiiaZUg3717F2+++WaJS70ZGhrCyckJt2/f1jo4IiIi\nIqLKplWCXFhYCCMjo1LL1axZE3l5edpUQURERESkF1olyA0bNkR4eHip5cLDw9GoUSNtqiAiIiIi\n0gutEmRnZ2eEhobiu+++Q1ZWVrHjz549w+rVqxESEoI+ffq8cJBERERERJVFq1Uspk6diqNHj8LH\nxwe//PILrK2tYWlpCVEUkZSUhKioKOTk5KBRo0aYNm2armMmIiIiIqowWiXIlpaW2L17N5YuXYrg\n4GBcuXJF5biBgQEGDRqE+fPno06dOjoJlIiIiIioMmi9k16jRo3w448/IjU1FZGRkUhKSoIgCLCw\nsED79u1hZmamyziJiIiIiCqF1gmyUu3atdG9e3ddxEJEREREpHdlSpDDwsLQvHlzNGjQQPq9PLp0\n6VL+yIiIiIiI9KBMCfJ///tffPHFF5g8eTIAYMKECSVuEiInCAIiIyO1j5CIiIiIqBKVKUF2dHRE\nw4YNpd/ZI0xERERE1VWZEuRffvmlxN+JiIiIiKoLrTYKISIiIiKqrrROkCMjI7F8+XKVx6KiovDu\nu++iU6dOcHV1xdGjR184QCIiIiKiyqRVgnzz5k28++672LlzJwoLCwEAaWlpmDJlCi5evAiFQoE7\nd+7g008/xaVLl3QaMBERERFRRdIqQd62bRvy8/OxceNGvPZa0Sn8/PyQlJSEcePG4fz58zh27BjM\nzMywY8cOnQZMRERERFSRtEqQw8LCMGDAALz11lvSY/7+/jA0NMTMmTMBAM2bN8eAAQPwzz//6CZS\nIiIiIqJKoFWCnJCQgNatW0u/P3v2DBEREXBwcICFhYX0eJMmTZCUlPTiURIRERERVRKtEmQDAwPk\n5ORIv1+4cAH5+flwcnJSKZeVlQUTE5MXi5CIiIiIqBJplSC3aNEC586dk37ftWsXBEGAs7OzSrlr\n165J21MTEREREb0MyrRRyPMGDBiA9evXY+zYsXjttdfwzz//oGPHjmjfvj0AoKCgALt27cK5c+cw\nZcoUnQZMRERERFSRtEqQPT09cenSJZw9exYA0KhRI6xatUo6Hhsbi6VLl6Jx48ZMkImIiIjopaJV\ngmxsbIyffvoJsbGxSEtLg42NDRQKhXT89ddfx6RJkzB58mSVSXtERERERFWdVgny7du3Ubt2bbRs\n2VLtcUEQMG/evBeJi4iIiIhIL7SapDdy5EgcOHBA17EQEREREemdVglyy5Yt8eDBA13HQkRERESk\nd1olyEuWLMGZM2fg4+ODxMREXcdERERERKQ3Wo1B3rZtG2xsbLBx40asXbsW9evXR506dWBgYFCs\nrCAI8PPze+FAiYiIiIgqg1YJ8rFjx1R+f/ToER49eqS2rCAI2lRBRERERKQXWiXIO3bs0HUcRERE\nRERVglYJcteuXXUdBxERERFRlaBVgiyXl5eH6OhoJCQkoF27drC0tNRFXEREREREeqHVKhYAkJyc\nDC8vL3Tr1g1ubm54//33ceXKFen4hAkTcPXqVZ0ESURERERUWbRKkNPT0+Hh4YE9e/YgPz8fbdq0\nUTl+//59XL58GVOmTMGdO3d0EigRERERUWXQKkHesmULYmNjMXnyZFy4cAEbN26EKIrS8WbNmuHn\nn39GdnY2fHx8dBYsEREREVFF02oMckBAABwdHTF37lwA6pdyc3R0RP/+/REaGvpiERIRERERVSKt\nepDj4+PRpUuXUsu1adMGT58+1aYKIiIiIiK90CpBNjAwQF5eXqnlnj17BmNjY22qICIiIiLSC60S\n5DZt2iAwMBD5+fkayzx79gx///13sQl8RERERERVmVYJspubG+7cuYNp06bh5s2bKCwsBFA0Fjkr\nKwsBAQF49913ER8fDzc3N50GTERERERUkbSapDdmzBiEhITg+PHjGDFiBARBgCAI+OSTT5CbmwsA\nEEURb7/9NkaPHq3TgImIiIiIKpJWCbIgCFi/fj38/Pywe/duREVFobCwEDk5OTA0NIStrS1GjRqF\nUaNGqV3hgoiIiIioqnqhrabd3d3h7u6OvLw8pKSkQBAEmJubw9DwhXewJiIiIiLSC63GIC9YsAAX\nL16UfjcyMkK9evVQt25dJsdERERE9FLTKpvdu3cv9u3bh8aNG2P48OEYPnw4WrRooevYiIiIiIgq\nnVY9yJ9//jnat2+Phw8fYuPGjRg0aBDGjh2LXbt2ITU1VdcxEhERERFVGq16kD09PeHp6Yn79+/j\n8OHDOHLkCK5cuYLw8HB88803cHZ2xrBhw+Ds7MwhF0RERET0Unmh7LVZs2aYPn06pk+fjujoaBw5\ncgRHjhzB8ePH4e/vj9q1a8PV1RULFy7UVbxERERERBVKqyEW6lhZWeGjjz7C33//jYMHD2LatGnI\ny8vDzp07dVUFEREREVGF0+n4h9TUVPj7++PEiRMIDQ1FVlYWh1gQERER0UvlhbPXlJQU+Pv74+jR\nozh//jwKCgogiiLs7OwwdOhQuLq66iJOIiIiIqJKoVWCnJycjICAgGJJcfPmzTF06FAMHToULVu2\n1HGoREREREQVT6sEuVevXlJSbGlpicGDB2PYsGHo0KGDruMjIiIiIqpUWiXIRkZGcHFxwdChQ9Gj\nRw8YGBjoOi4iIiIiIr3QKkE+d+4catSooetYiIiIiIj0TqsEWZkcp6Wl4e7du8jJyYEoihrLd+nS\nRbvoiIiIiIgqmVYJcnp6OubNm4egoKASE2MAEAQBkZGRWgVHRERERFTZtEqQV61ahRMnTsDQ0BCt\nW7fGf/7zH13HRURERESkF1olyEFBQWjYsCF+//13NGjQQNcxERERERHpjVZbTaempmLIkCFMjomI\niIio2tEqQW7QoAFMTEx0HQsRERERkd5plSC7urri2LFjyMvL03U8RERERER6pVWCPHPmTDRt2hTT\np09HREQECgoKdB0XEREREZFeaDVJz8PDAwAQFRUFd3d3GBgYaFzJQhAEhISEaB8hEREREVEl0ipB\njoiIUPk9Pz8fycnJassKgqBNFUREREREeqFVgnzixAldx0FEREREVCVolSA3adJE13EQEREREVUJ\nWiXIcjdu3MD169eRnJwMQRBgaWkJe3t7tG7dWhfxERERERFVKq0T5MuXL2PhwoW4c+eO2uMODg5Y\ntmwZrKystA6OiIiIiKiyaZUg3759G56ensjKykLz5s1hb28PCwsLiKKIpKQkXLlyBVeuXMGECROw\nf/9+NGzYUNdxExERERFVCK0SZG9vb+Tl5WHNmjVwdXVVW8bPzw+LFi2Ct7c3vLy8XihIIiIiIqLK\nolWCfOHCBfTr109jcgwA7u7uCA4OxqlTp7QOjoiIiIiosmm1k15CQgLatGlTajkbGxs8efJEmyqI\niIiIiPRCqwTZxMQESUlJpZZLT0+HQqHQpgoiIiIiIr3QKkFu06YNgoKC8OzZM41lMjIycOLECbRt\n2959vp0AACAASURBVFbr4IiIiIiIKptWCfLIkSMRFxcHd3d3HDhwALGxsUhPT0daWhpiYmLg5+eH\nd955RypDRERERPSy0GqS3qhRoxAWFoY///wT8+fPV1tGFEWMHj0abm5uWgcXFxeHr7/+GleuXEGt\nWrXg4uKCOXPmqC27Y8cO7Ny5EwkJCbC2tsb8+fNha2srHb979y4++eQTPHnyBGfOnNG6HiIiIiKq\n3rTqQQaAlStXYsOGDejbty8aNGgAQ0NDGBkZoWHDhujfvz+8vb2xePHiFwpu5syZaNiwIQIDA+Hr\n6wt/f3/4+voWKxcYGIgff/wR3377LUJCQuDs7Ixp06YhOzsbABAaGooJEyagWbNmL1QPEREREVV/\nL7TVdL9+/dCvXz9dxaLi2rVruHXrFnbs2IFatWqhVq1amDx5Mnbs2IFJkyaplN2zZw9GjhwJe3t7\nAMB7772HHTt2IDAwEC4uLkhNTYWvry/Cw8Nx6dIlreshIiIioupPqx7kwsLCEo+npqZqFYxcZGQk\nmjRpAlNTU+mx9u3bIyYmBpmZmSplIyIi0L59e+l3QRDQrl07XLt2DQAwcOBAvP766y9cDxERERFV\nf+XqQS4sLMTmzZsRFRWF9evXqy3z4MEDjBgxAp999hk8PDy0DiwlJQVmZmYqj5mbmwMAkpOTUbNm\nzRLL1q5dGykpKTqtpzRZWVllLqsrymEkRFQ9ZWdnv3IX6/xcI6re9PW5Vp48rVwJ8vr167F582bU\nqFEDubm5atc4jo6ORl5eHhYvXgwTExOMGDGiPFWoEEVR6+fqo57Y2FidnKc8YmJiKr1OIqo8MTEx\n5bpQrw74uUZUvb0Mn2tlTpCjoqKwZcsW1KtXD5s2bdK4AUjv3r2xf/9+TJgwAYsWLUKvXr1gaWlZ\n7sAsLCyK9QCnpKRAEARYWFgUK5ucnFysbFnWYC5PPaVp2bIlTExMyvWcF1V0BRZbqXUSUeVp1aoV\n2rVrp+8wKhU/14iqN319rmVlZZW5M7PMCfKBAwcAABs2bICdnV2JZa2srPD999/jv//9L3bt2oWZ\nM2eWtRqJnZ0d4uPjkZKSIg15uHr1KqysrIoloXZ2drh+/brUW11YWIjIyMgyrcFcnnpKY2JiUulX\nRDVq1KjU+oioctWoUaPK97ToGj/XiKq3l+FzrcyT9EJDQ+Ho6AgHB4cyle/atSscHR0REBCgVWDt\n2rWDvb091qxZg4yMDERHR8PX1xfjxo0DAAwaNAiXL18GAHh4eODPP/9EeHg4srOzsXHjRhj/v/bu\nPKyqet/j+GcDMgeK5jwdNQMLHA4JqInihCVC5gTenns8app6k0qveW+i1zqVN7XhKb15tDh6c8Bj\nPmhHwQGn1MwpFdScpcQBBdQcGNf9w8d9FwcH3GyG9P36J9Zav/37fdd+npYffvzWWi4u6tKlS7E+\n77aU4kHjAAAA4PFS6hnkzMxMBQcHP1Tn/v7+Wrp06UMXdcenn36qyZMnq1OnTvL09FR0dLT1xr8z\nZ85YF3g///zzevPNNxUbG6usrCz5+/tr7ty51mUgw4YN065du1RUVKTCwkIFBATIYrFo/vz5CgwM\nvO84AAAAeLyUOiBfu3btodcSe3t7Kz8//6GLuqNOnTqaO3fuXY8dPny42PbgwYM1ePDgu7adP3++\nzeMAAADg8VLqJRbe3t7KzMx8qM4zMjLk7e390EUBAAAAlaXUAblly5batm1bqTvOz8/Xpk2bSvUk\nCQAAAKCqKHVADg0N1alTp6xPs3iQOXPmKDMzU2FhYTYXBwAAAFS0UgfkAQMGqHr16poyZcp9Q3JB\nQYE+++wzzZ49W7Vr1y7Vo9YAAACAqqLUN+m5u7tr1qxZGjFihP7jP/5D8+fPV/fu3dW8eXO5u7vr\n6tWrSktLU3Jysi5duiQXFxd9+umncnFxKc/6AQAAALt6qFdNh4SEaOHChZo0aZKOHz+uEydOFDt+\n5znDrVq10vvvvy9fX1/7VQoAAABUgIcKyJLUtm1brVmzRlu3btWOHTv066+/6vr16/L09FSzZs3U\nqVMnBQYGlketAAAAQLl76IAsSRaLRZ07d1bnzp3tXQ8AAABQqUp9kx4AAADwOCAgAwAAACYEZAAA\nAMCEgAwAAACYEJABAAAAEwIyAAAAYGK3gJyXl6eioiJ7dQcAAABUijIF5O+++07Dhg1T+/bt1bp1\na23cuNF67PPPP1d2dnaZCwQAAAAqkk0vCikqKlJsbKzWrVsnwzBksViKHf/111/1+eefKykpSYsW\nLZKXl5ddigUAAADKm00zyEuWLNHatWvVtm1bxcfHa+XKlTIMw3q8fv36euONN3T8+HHNnz/fbsUC\nAAAA5c2mgLxixQo1adJE8fHxCg4Olru7e/FOHRw0cuRIPffcc1q/fr1dCgUAAAAqgk0B+eTJk+ra\ntaucnZ3v2y4wMFC//vqrTYUBAAAAlcGmgJyfny8PD48HtnN0dCyxPhkAAACoymwKyA0bNtSePXse\n2G7Lli1q2LChLUMAAAAAlcKmgNy9e3ft3LlTn3/++V2ffXz58mVNnjxZBw8eVPfu3ctcJAAAAFBR\nbHrM26uvvqrk5GR98cUXWrRokRo3biyLxaI5c+Zo9uzZOnbsmPLy8tSkSRMNGzbM3jUDAAAA5cam\nGWRPT08tXbpUffr00ZUrV/TTTz/JMAylpqYqLS1NRUVFioiI0KJFi/TEE0/Yu2YAAACg3Ng0gyxJ\n1atX10cffaQpU6bo4MGDysrKksViUa1atdSqVSt5enras04AAACgQtgckO/w9PRUSEiIPWoBAAAA\nKp1NATkjI6PUbS0Wi9zc3FS9enVbhgIAAAAqlE0BOSws7KGfb+zp6amuXbsqNjZW9evXt2VYAAAA\noNzZdJNeo0aNVK9ePTk6OsowDBmGIVdXV7m5uVm3HR0d5e3tLW9vb7m4uOjatWtauXKlBgwYoPPn\nz9v7PAAAAAC7sCkgr127Vh07dlS9evX03//939q5c6f27dunvXv3aufOnZo+fboaNmyowYMH64cf\nftBPP/2kdevWqX///rp8+bL+53/+x97nAQAAANiFTUss5s2bpy1btmjlypXy9vYudszb21uRkZHq\n3LmzoqKi1LBhQ/Xv31+NGjXSe++9p+PHj2vr1q12KR4AAACwN5tmkP/+978rKiqqRDg2q1Gjhvr2\n7av//d//Lba/Q4cOyszMtGVYAAAAoNzZFJDPnTsnB4cHf9QwDJ05c6bYvps3b/KMZAAAAFRZNgXk\nmjVras2aNbp58+Y92+Tn52vr1q1yc3Oz7rt48aJWrVqlJk2a2DIsAAAAUO5sWoPcq1cvxcfHKzIy\nUgMHDtQzzzyjGjVqSJKuXr2qn3/+WQkJCTp+/Lj69u0rSfrhhx8UGxurK1euaPz48fY7AwAAAMCO\nbArIr7/+utLS0rRr1y7NnDnzrm0Mw1CLFi00YcIESVK1atWUk5Ojf/3Xf1VUVJTtFQMAAADlyKaA\n7O7uroULF2r9+vXauHGjTpw4oezsbEm3XwjStGlTderUSX369FG1atUkSa1atVJycjLLKwAAAFCl\n2RSQ7+jevbu6d+9+z+Pnz5/XtWvX9NRTT8nNzY1wDAAAgCrPppv0Smv58uUaNmxYeQ4BAAAA2FWZ\nZpC3bNmin3/+Wbm5uSWOXb16VYmJicrPzy/LEAAAAECFsikg37hxQ8OHD9e+ffvu284wDPXu3dum\nwgAAAIDKYFNA/vLLL7V3717VqlVLHTp0kIuLi5YtW6Zu3bqpRo0a2rlzp3JzczVp0iT17NnT3jUD\nAAAA5camgLx27Vo1adJEK1askLu7u86ePatly5apX79+6tatm4qKihQXF6dFixapW7ducnR0tHfd\nAAAAQLmw6Sa9s2fPqmfPnnJ3d797pw4OiouL06VLlzR37twyFQgAAABUJJufYmEOxy4uLpJur02+\nw9nZWd27d9c//vGPMpQHAAAAVCybAnKdOnWUmppq3a5Ro4YsFouOHTtWrJ2Li4vOnz9ftgoBAACA\nCmRTQO7YsaNSUlL0n//5n8rIyJCjo6OaNGmiv//97zpz5owk6fr161q3bp1q1Khh14IBAACA8mRT\nQB49erRq1qypb7/9VidOnJAkDRw4UFlZWerTp4/69u2rLl266OjRo+rUqZNdCwYAAADKk01Psahd\nu7ZWrFihJUuWqGnTppKkoUOH6tSpU1q+fLmOHj0q6fZM81tvvWW3YgEAAIDyZvOb9J588kn927/9\nm3XbYrHo3Xff1bhx43T27FnVrVtXderUsUuRAAAAQEWxKSBv3rxZDRs2VPPmzUscq1WrlmrVqlXm\nwgAAAIDKYNMa5Ndff10bNmywdy0AAABApbMpIPv5+ennn3+2dy0AAABApbMpIH/44YdKT0/X1KlT\ndfDgQRUWFtq7LgAAAKBS2LQGefz48ZKkxMRELV26VJL0xBNPyNHRsURbi8Wi7du3l6FEAAAAoOLY\nFJDNb9G74+rVq3dta7FYbBkCAAAAqBQ2BWRu0AMAAMCjyqaA3KBBA3vXAQAAAFQJNr8o5I4LFy4o\nNTVVly5dUseOHdWwYUNJUlFRkRwcbLoHEAAAAKg0NifY9PR0DRs2TF27dtXYsWM1depU66PfCgsL\n1bt3b5ZiAAAA4HfHpoB88eJFxcTEaNu2bapbt66ef/75YsfPnz+vy5cva9y4cTpw4IBdCgUAAAAq\ngk0Bec6cObp06ZImT56sDRs2KC4uToZhWI83aNBAS5cuVbVq1fTVV1/ZrVgAAACgvNkUkLds2aLQ\n0FANGTJEFovlro9ya968uXr16qXdu3eXuUgAAACgoti8xMLf3/+B7Zo2baqcnBxbhgAAAAAqhU0B\n2dXVVdeuXXtgu8uXL8vDw8OWIQAAAIBKYVNA9vX11dq1a3X9+vV7tsnMzNQ//vEP+fn52VwcAAAA\nUNFsCsjR0dE6d+6cYmJilJKSovPnz0uSrl+/rmPHjik+Pl4DBgxQdna2Bg0aZNeCAQAAgPJk04tC\nXnjhBR04cEDx8fEaM2aMJMlisWjixInWNoZh6JVXXlHv3r3tUykAAABQAWx+k97bb7+t0NBQLV68\nWAcOHNDly5dlsVhUq1YttWnTRv3791eHDh3sWSsAAABQ7sr0qumQkBCFhITYqxYAAACg0tm0Bnno\n0KFKTEzUzZs37V0PAAAAUKlsCsg7duzQ22+/rQ4dOmjixInavn27vesCAAAAKoVNAfmTTz5Rjx49\nZBiGEhMTNWzYMIWGhmrGjBk6duyYvWsEAAAAKoxNa5DDw8MVHh6uGzduaMOGDVq9erW+//57zZs3\nT/Pnz5efn58iIyMVEREhHx8fe9cMAAAAlBubZpDvcHd3V0REhObMmaMdO3bo/fffV8eOHXX06FF9\n8MEH6ty5s0aNGmWvWgEAAIByV6aAbObp6al+/fpp3rx52rZtm9544w25uLho8+bN9hoCAAAAKHdl\neszbP/v555+VlJSkDRs26NixYzIMQ97e3vYcAgAAAChXZQ7IR44cUVJSkpKSknTmzBkZhiEXFxf1\n7NlTERERCg0NtUedAAAAQIWwKSAfPnzYGorT09NlGIYcHBwUHBysiIgI9ezZU56envauFQAAACh3\nNgXkl156SRaLRYZhqFWrVurbt69eeOEF1a5d2971AQAAABXKpoDcsGFDRUREKCIiQs2aNbN3TQAA\nAEClsSkgr1+/vlTtjh49qsWLF2vKlCm2DAMAAABUOLs95u2O/Px8rVq1SjExMYqMjNSSJUvsPQQA\nAABQbuz2mLdffvlFS5Ys0bfffqucnBwZhqFatWqpX79+9hoCAAAAKHdlCsiGYSglJUWLFy/W9u3b\nZRiGLBaLOnXqpIEDByosLEyOjo72qhUAAAAodzYF5MzMTCUkJGjZsmW6cOGCDMNQ/fr1lZGRoaio\nKH3wwQf2rhMAAACoEA8VkHfs2KHFixcrJSVFBQUFqlatmsLDw9W/f3+FhISoVatW5VUnAAAAUCFK\nHZDDw8Otb8p7+umn1a9fP0VGRqp69erlWR8AAABQoUodkE+fPq0mTZro3XffVfv27cuzJgAAAKDS\nlPoxb7Vr19aZM2f05z//WaNGjVJycrIKCgrKszYAAACgwpV6Bnnjxo1at26dvvnmG23atEmbN29W\njRo1FBkZqQEDBvBGPQAAADwSSh2QHR0dFR4ervDwcJ04cULffPONEhMT9fXXXys+Pl5t2rSRxWIp\nz1oBAACAcmfTm/SaN2+uuLg4bd26VXFxcWrRooX27dsnwzCUnJysKVOm6NChQ/auFQAAACh3ZXrV\ntLu7u2JiYrRq1SotWLBA4eHhysvL09KlS/Xyyy+rf//+WrZsmb1qBQAAAMqd3V413b59e7Vv316Z\nmZlaunSpli1bptTUVKWlpWnAgAH2GgYAAAAoV2WaQb6bJ598UmPHjlVKSoo++eQTPffcczb3lZGR\noZEjRyooKEhhYWGaMWPGPdvemcEODAzUkCFDlJaWZj2Wl5enuLg4hYaGKiQkROPGjVNOTo71uK+v\nrwICAtS6dWvrf9977z2b6wYAAMDvl91mkP+Z+aY+W40dO1b+/v5KSUnR5cuXNWLECNWqVUt/+tOf\nirVLSUnRF198oXnz5unpp5/W3/72N40cOVLr16+Xq6urZs2apcOHDyshIUFubm565513NGnSJM2Z\nM0eSZLFYlJycrHr16pXllAEAAPAIsPsMsr0cPHhQR48e1YQJE+Th4aHGjRtr6NChSkhIKNE2ISFB\n/fr1k7+/v5ydnTV8+HBZLBalpKSosLBQy5cv15gxY1SnTh15eXkpNjZWmzZtUmZmpiTJMAwZhlHR\npwgAAIAqqMoG5EOHDqlBgwby9PS07mvVqpVOnTqlGzduFGubmpqqVq1aWbctFov8/Px08OBBpaen\n69q1a/Lz87Meb9asmVxdXYstw5gxY4a6du2q9u3bKy4ursQYAAAAeDyU2xKLssrJyZGXl1exfdWr\nV5ckZWdny93d/b5tvb29lZOTo5ycHFksFnl7exc77uXlpezsbElSmzZt1LFjR02fPl2//PKLYmNj\nNW3aNH344YcPVfPNmzcfqr093Lp1q8LHBFBxbt269dj9ws51DXi0VdZ17WFyWpUNyJLsuuzhfn0t\nWbLE+nOzZs00fvx4jR49Wu+++66qVatW6jFOnz5dlhJtcurUqQofE0DFOXXqVLEJgccB1zXg0fZ7\nuK5V2YDs4+NT7EkTkqyzwT4+PiXa3pkNNrdt2bKlfHx8ZBiGcnJy5ObmZj1+5cqVEv3c0aBBAxUW\nFiorK0t16tQpdc1NmzYtNkZFuP0b2OkKHRNAxfnDH/5QbInY44DrGvBoq6zr2s2bN0s9mVllA/Kz\nzz6rc+fOKScnx7q04sCBA2revHmJEPrss88qLS1NUVFRkqSioiIdOnRIAwcOVKNGjeTt7a20tDTr\nUyqOHj2q/Px8+fv76/Dhw1q5cqUmTpxo7e/EiRNydnZW7dq1H6pmNze3Cv+NyNXVtULHA1CxXF1d\nq/xMi71xXQMebb+H61qVvUnPz89P/v7+mjlzpn777TedOHFC8fHxiomJkSSFh4dr7969kqTo6Ggl\nJiZq//79unXrlmbPni0XFxeFhobKwcFBAwcO1Jw5c3T+/HllZ2dr1qxZ6tmzp3x8fOTj46OlS5fq\nr3/9q/Ly8nTq1Cl99tlnGjRokCwWS2V+BQAAAKgEVXYGWZI+/fRTTZ48WZ06dZKnp6eio6MVHR0t\nSTpz5ox1gffzzz+vN998U7GxscrKypK/v7/mzp0rZ2dnSdLrr7+uGzduKDIyUoWFherataumTJki\nSapTp47mzp2rGTNmaM6cOXJxcdFLL72k2NjYyjlpAAAAVKoqHZDvhNe7OXz4cLHtwYMHa/DgwXdt\nW61aNU2ePFmTJ0++6/HAwMBiN+oBAADg8VVll1gAAAAAlYGADAAAAJgQkAEAAAATAjIAAABgQkAG\nAAAATAjIAAAAgAkBGQAAADAhIAMAAAAmBGQAAADAhIAMAAAAmBCQAQAAABMCMgAAAGBCQAYAAABM\nCMgAAACACQEZAAAAMCEgAwAAACYEZAAAAMCEgAwAAACYEJABAAAAEwIyAAAAYEJABgAAAEwIyAAA\nAIAJARkAAAAwISADAAAAJgRkAAAAwISADAAAAJgQkAEAAAATAjIAAABgQkAGAAAATAjIAAAAgAkB\nGQAAADAhIAMAAAAmBGQAAADAhIAMAAAAmBCQAQAAABMCMgAAAGBCQAYAAABMCMgAAACACQEZAAAA\nMCEgAwAAACYEZAAAAMCEgAwAAACYEJABAAAAEwIyAAAAYEJABgAAAEwIyAAAAIAJARkAAAAwISAD\nAAAAJgRkAAAAwISADAAAAJgQkAEAAAATAjIAAABgQkAGAAAATAjIAAAAgAkBGQAAADAhIAMAAAAm\nBGQAAADAhIAMAAAAmBCQAQAAABMCMgAAAGBCQAYAAABMCMgAAACACQEZAAAAMCEgAwAAACYEZAAA\nAMCEgAwAAACYEJABAAAAEwIyAAAAYEJABgAAAEwIyAAAAIAJARkAAAAwISADAAAAJgRkAAAAwISA\nDAAAAJgQkAEAAAATAjIAAABgQkAGAAAATAjIAAAAgAkBGQAAADAhIAMAAAAmBGQAAADAhIAMAAAA\nmBCQAQAAABMCMgAAAGBCQAYAAABMCMgAAACACQEZAAAAMCEgAwAAACYEZAAAAMCEgAwAAACYEJAB\nAAAAEwIyAAAAYEJABgAAAEwIyAAAAIAJARkAAAAwqdIBOSMjQyNHjlRQUJDCwsI0Y8aMe7ZdsGCB\nwsPDFRgYqCFDhigtLc16LC8vT3FxcQoNDVVISIjGjRunnJwcm8YBAADAo61KB+SxY8eqbt26SklJ\nUXx8vNatW6f4+PgS7VJSUvTFF1/oo48+0vbt29WlSxeNHDlSt27dkiTNmjVLhw8fVkJCgpKTk2UY\nhiZNmvTQ4wAAAODRV2UD8sGDB3X06FFNmDBBHh4eaty4sYYOHaqEhIQSbRMSEtSvXz/5+/vL2dlZ\nw4cPl8ViUUpKigoLC7V8+XKNGTNGderUkZeXl2JjY7Vp0yZlZmY+1DgAAAB49FXZgHzo0CE1aNBA\nnp6e1n2tWrXSqVOndOPGjWJtU1NT1apVK+u2xWKRn5+fDh48qPT0dF27dk1+fn7W482aNZOrq6vS\n0tIeahwAAAA8+pwqu4B7ycnJkZeXV7F91atXlyRlZ2fL3d39vm29vb2Vk5OjnJwcWSwWeXt7Fzvu\n5eWl7OzshxrnXoqKiqx13Lx5s5RnaB+5ubnycr4ll4LMCh0XlaPmEw7yzHVRtSyjsktBBfDMdVFu\nbq4uX75c2aVUKK5rjxeua4+Xyryu5ebmSvr/3HY/VTYgS5Jh2O9/lvv1VdZx7nzh586dK1M/tnBz\nc9Pbo1+u8HFRWZ6v7AJQCU6fPl3ZJVQormuPG65rj6PKvK7l5uYWWzlwN1U2IPv4+BR70oQk62yw\nj49PibbZ2dkl2rZs2VI+Pj4yDEM5OTlyc3OzHr9y5Yp8fHxUUFBQ6nHuxdvbW02bNpWLi4scHKrs\nqhUAAIDHVlFRkXJzc0usKribKhuQn332WZ07d045OTnWJQ8HDhxQ8+bNiwXdO23T0tIUFRUl6fYX\ncOjQIQ0cOFCNGjWSt7e30tLSVK9ePUnS0aNHlZ+fL39/f124cKHU49yLk5OTatasaa9TBwAAQDl4\n0MzxHVV2utPPz0/+/v6aOXOmfvvtN504cULx8fGKiYmRJIWHh2vv3r2SpOjoaCUmJmr//v26deuW\nZs+eLRcXF4WGhsrBwUEDBw7UnDlzdP78eWVnZ2vWrFnq2bOnfHx8HjgOAAAAHi8Ww54Lfe3swoUL\nmjx5sn788Ud5enoqOjpaY8aMkXQ7QP/1r39Vp06dJElLlizRl19+qaysLPn7+2vq1Klq0aKFJCk/\nP18ffvihvvvuOxUWFqpr166aMmWK9beI+40DAACAx0uVDsgAAABARauySywAAACAykBABgAAAEwI\nyAAAAIAJARkAAAAwISADAAAAJgRk4BGxYsUK62MPAeD35JVXXtGsWbPueTwgIEA7duyQJIWFhWnp\n0qUVVRoeU1X2TXoAAADS7TfcAhWJGWQAAADAhIAMVAFdunTRpk2brNsxMTEaNGiQdXvHjh0KDg5W\nRkaGRo0apeDgYAUFBWnixIm6fv16sb6+/fZbhYaGKigoSHFxcSooKKio0wDwGPH19dXq1av18ssv\nq3Xr1ho1apQuXLig4cOHq23bturXr58yMjKs7devX6/IyEi1bdtW3bp108KFC4v1d/PmTb311ltq\n27atevXqpeTk5GJjff/99yVqMAxDn332mXr06KE2bdpowIAB2rt3b/mdNB4bBGSgCggKCtK+ffsk\nSXl5eUpPT9fFixeVm5srSdqzZ4+Cg4M1evRoNWzYUFu2bNGaNWt08eJFTZ8+3drP1atX9dNPPykp\nKUmLFi3Shg0btGDBgko5JwCPviVLlmju3LlatWqVtm/frhEjRmj8+PH6/vvvVVRUpK+++kqSdOTI\nEcXGxmrcuHHavXu3/vKXv2jmzJnasmWLta+VK1cqIiJCP/74o4YMGaK33npLFy9evO/48fHxWr16\ntb766ivt3r1bUVFReu2113Tr1q1yPW88+gjIQBUQHBxsnfXYv3+/WrZsqZYtW2r//v2SpN27d+uZ\nZ57R8ePHNX78eDk7O8vHx0djxozRypUrrf3k5+dr3LhxcnNzU/PmzdWnTx9t3ry5Us4JwKMvIiJC\nNWvWVOPGjdWsWTMFBATI19dXHh4eat++vc6cOSPp9l+2OnTooLCwMDk6Oio4OFhdunTRmjVrrH0F\nBASoS5cuqlatmv7lX/5Fnp6e2r59+33HX758uYYOHapGjRrJyclJQ4YMkZeXlzZu3Fiu541H2SSH\nOQAACndJREFUHzfpAVVAUFCQpk2bpqKiIu3atUvt2rWTg4OD9uzZo3bt2mn//v0KCQlRQUGBgoKC\nrJ8zDENFRUXKzs6WJHl5ealmzZrW440bNyYgAyg3derUsf7s4uJSYvvOX8F+/fVXNW/evNhnmzRp\nUmw5RIsWLaw/Ozg4qEGDBg+cQU5PT9d7772n999/X9L/XxPPnz9v+0kBIiADVUL9+vX15JNPKi0t\nTbt379aIESMkSfPmzVPHjh3l4+Ojp556Sh4eHtqzZ889+3FwKP5HIcMw5OLiUq61A3h8/fM1x2Kx\n3LVdXl7eXfeb2//zZw3DkLOz833Hd3V11fvvv6/u3buXplyg1FhiAVQRQUFB2rVrl1JTU9W2bVsF\nBAQoNTVVu3btUocOHdSoUSNdv35dZ8+etX7m+vXrysnJsW5fuXKl2HZ6enqxGR0AqAyNGzfWyZMn\ni+07efKkGjdubN0+deqU9eeioiKdPXtWdevWvW+/jRo10pEjR4rtM18jAVsRkIEqIigoSMuWLVPT\npk3l6uoqDw8P1atXTytWrFBISIieeuoptW3bVn/5y1+UnZ2tq1evKi4uThMnTrT2Ua1aNX3++efK\nzc3VyZMntXr1avXo0aMSzwoApL59+2rbtm3avHmzCgsLtXXrVm3atElRUVHWNnv37tWOHTtUUFCg\nxYsXKy8v74EvPxo8eLC++eYb7d+/X0VFRVq9erX69OnDEguUGQEZqCKCgoJ0+vRp/fGPf7Tua9eu\nnU6ePKng4GBJ0qxZs1RUVKRu3bqpV69eMgxDH3zwgbV97dq15evrq+7duysmJkbh4eF6+eWXK/xc\nADz6/nlJxL2WV0hSmzZt9N5772nGjBlq3769ZsyYoVmzZikwMND62UGDBmnp0qV67rnntHDhQn3y\nySfy9PS0Hr/Tv3mc/v37KyYmRmPHjtUf//hHzZ8/X7Nnz37gzDPwIBbDMIzKLgIAAACoKphBBgAA\nAEwIyAAAAIAJARkAAAAwISADAAAAJgRkAAAAwISADAAAAJgQkAEAAAATAjIAAABgQkAGAAAATAjI\nAICHFhYWJj8/P2VkZFR2KQBgd06VXQAA4O7Onj2rbt26ldjv6uqq6tWr6+mnn1ZISIgiIyNVo0aN\nCq3ttdde02+//SZvb+8KHRcAKoLFMAyjsosAAJR0JyC7ublp3LhxunO5vnnzps6dO6cff/xR6enp\ncnNz09tvv62BAwdWcsUA8GhgBhkAqjhXV1f96U9/uuuxlJQUxcXFKS4uTjdu3LhnOwBA6bEGGQB+\nx8LCwjR//ny5urpq5syZSk9PL3Z8+/btGjFihIKDg/Xss88qNDRUEydO1KlTp6xtdu/eLV9fX0VG\nRt5znJEjR8rX11crV660juvr61tiDfLVq1c1ffp09ezZUwEBAQoKClJsbKxOnjxZok/DMLRkyRIN\nGjRI7dq1U+vWrRUeHq4ZM2bo2rVrZflaAKBMCMgA8Dv39NNPa/DgwSooKNDChQut+xcsWKBhw4Yp\nNTVVvXv31muvvaZ27dpp1apV6tevn3766SdJUmBgoOrXr6+jR4/qzJkzJfq/evWqtm3bJg8PD/Xs\n2dO632KxFGuXlZWlqKgoLViwQAEBARozZox69uyplJQUvfTSSzpw4ECx9uPGjdPUqVN17do1xcTE\n6M9//rNq1aqlefPmacCAAcrOzrbn1wQApcYSCwB4BISHhys+Pl5bt26VJJ08eVLTp09X7dq1tWzZ\nMtWuXdvadtOmTRo1apQmTZqkNWvWSJJefPFFzZs3T0lJSRo5cmSxvpOTk1VQUKCIiAi5urres4ap\nU6fq3LlzmjZtmgYMGGDd36NHD7366quaPHmyEhMTJUnLli3T2rVr1bFjR3355Zdycvr/f46mT5+u\nr7/+Wh9//LGmTZtW9i8HAB4SM8gA8Aho1qyZJOncuXOSbgfQoqIiDR8+vFg4lqQuXbooMDBQp0+f\n1r59+yRJERERMgxDSUlJJfpes2aNLBaL+vbte8/xr1y5ovXr18vb21v9+/cvdqxz584aPXq0Xnzx\nReXl5UmSlixZIovFon//938vFo6l2zPLzs7O+u6775Sfn/+Q3wQAlB0zyADwCHB3d5ck5eXlqaCg\nwLqcoWbNmjp79myJ9s2aNdOePXt08OBBtW3bVi1btlTLli115MgRpaenq3HjxpJuL5vYuXOn6tat\nq5CQkHuOn5aWpqKiIrVs2bLE0gtJev31160/5+fn68iRI7JYLHJ3dy9Rn2EYatq0qY4dO6YTJ07I\n19f34b8QACgDAjIAPAKuXLki6XZQdnJy0uXLlyVJb7755l3b3wmxly5dsu6LiIjQzJkzlZSUpFdf\nfVWSlJSUpMLCwvvOHkuyjlea5yLn5OSosLBQFotFPXr0KHV9AFBRCMgA8AhITU2VJLVo0ULS/wfM\niRMnWmeD76ZJkybWn/v06aNZs2YVC8ilWV4hSQ4Ot1fs3VlCcT93anN2dtbHH3+s+z2O38/P74H9\nAYC9EZAB4BGQmJgoi8Wi0NBQSVKtWrV0+vRp/eEPf1CXLl1K1Ue9evUUGBio3bt365dffpGrq6v2\n7NmjZ555Rs2bN7/vZ2vWrCnp9pKMB6levbqcnJyUl5enkJAQubm5lao+AKgo3KQHAL9zO3fuVFJS\nktzd3RUdHS1JatOmjQzD0LZt2+76mQsXLtx1tjciIkKStGHDBiUnJ6uoqEhRUVEPrMHPz09OTk46\ncuTIXZ9h/F//9V8aM2aMsrKy5OTkpGeeeUaS7lnfL7/88sAxAaC8EJAB4HcsOTlZo0ePlmEYmjZt\nmmrUqCFJeumll+To6Khvv/1WJ06cKPaZixcvKjo6Ws8//3yJMBseHi4nJydt2rRJGzZskJOTk154\n4YUH1uHt7a2wsDAVFBTo66+/LnZs7969Wrx4sU6fPi0fHx9JUv/+/WUYhr744gtdv369WPvdu3cr\nPDxcr7zyykN/HwBgDyyxAIAq7tatW/rqq6+s2wUFBcrMzNQPP/ygY8eOycPDQx999JFefPFFa5tm\nzZppwoQJmj59ugYMGKA+ffqoQYMGysjIUFJSkq5evap33nlHTzzxRLGxvLy81LlzZ23ZskWS1KlT\nJ2uofZBJkybpwIEDmjNnjo4cOaKAgACdPXtW3333nZydnfXOO+9Y2/bv31+bNm1SSkqKIiIi1Lt3\nb3l4eOjYsWNat26d3NzcNGHChLJ8bQBgM4txv7sjAACV5uzZs+revXuJ/RaLRV5eXtb1xYMGDVL1\n6tXv2seOHTv0t7/9Tfv379e1a9f0xBNPqHXr1nrllVfUsWPHu34mKSlJb7zxhiwWiz7++GP16tWr\nRJuwsDCdP39e69evV/369a37s7KyNHv2bG3cuFGZmZlyd3dX+/btNXr06BKPazMMQwkJCVqxYoWO\nHTum3NxcPfnkkwoODtaIESOsz3YGgIpGQAYAAABMWIMMAAAAmBCQAQAAABMCMgAAAGBCQAYAAABM\nCMgAAACACQEZAAAAMCEgAwAAACYEZAAAAMCEgAwAAACYEJABAAAAEwIyAAAAYEJABgAAAEwIyAAA\nAIDJ/wFv7+oqz/wtpwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d04314f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "a=test[['device','converted','price']].groupby(['device','price']).mean()\n",
    "a=a.sort(['converted'],ascending=True)\n",
    "a=a.reset_index()\n",
    "\n",
    "with sns.plotting_context(\"notebook\",font_scale=1):\n",
    "    sns.set_style('whitegrid',rc={'figure.figsize':(25,10)})\n",
    "    graph=sns.barplot(x='device',\n",
    "                 y='converted',\n",
    "                  data=a, hue='price',\n",
    "                 \n",
    "               ) \n",
    "    graph.tick_params(labelsize=10)\n",
    "    sns.plt.title(\"Average Conversion by Device\",fontsize=22)\n",
    "    plt.ylabel(\"Average Conversion\", fontsize=16)\n",
    "    plt.legend(loc='upper right',fontsize=10)\n",
    "    plt.xlabel(\"Device\", fontsize=16)\n",
    "    plt.rc('savefig',dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There isn't much to draw from this graph.  Web and mobile conversion are mostly the same which is pretty good as I would expect mobile conversion to be smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jeremy/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAIPCAYAAACFcZN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng4JlQgghhBBC1Ph/pDDaU3KYIuAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6d0181d128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "a=test[['operative_system','converted','price']].groupby(['operative_system','price']).mean()\n",
    "a=a.sort(['converted'],ascending=True)\n",
    "a=a.reset_index()\n",
    "\n",
    "with sns.plotting_context(\"notebook\",font_scale=1.5):\n",
    "    sns.set_style('whitegrid',{'figure.figsize':(25,10)})\n",
    "    graph=sns.barplot(x='operative_system',\n",
    "                 y='converted', hue='price',\n",
    "                  data=a,\n",
    "                           ) \n",
    "    graph.tick_params(labelsize=10)\n",
    "    sns.plt.title(\"Average Conversion by Operative System\",fontsize=20)\n",
    "    plt.ylabel(\"Average Conversion\", fontsize=18)\n",
    "    plt.xlabel(\"Operative System\", fontsize=18)\n",
    "    plt.rc('savefig',dpi=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Something happened with linux for price \\$59 as the conversion rate was 0.  This definitely needs to be examined before\n",
    "the company starts selling software for \\$59\\.  Also, linux conversion rate is low in general.  It seems like the company's software may not be very accomodating for linux users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id             316800\n",
       "timestamp           316800\n",
       "source              316800\n",
       "device              316800\n",
       "operative_system    316800\n",
       "test                316800\n",
       "price               316800\n",
       "converted           316800\n",
       "city                275616\n",
       "country             275616\n",
       "lat                 275616\n",
       "long                275616\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_merged=test.merge(user_table,how='left',on='user_id')\n",
    "test_merged.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Merge was successful, Only some city countries, lattitudes, and longitudes are missing.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "I have created some interactive map visualizations in my scripts Us_city_convert_map.py, Us_city_nonconver_map.py, and\n",
    "Us_city_conver_mean_map.py.  The map files are html files that can be opened in any internet browser: Conversion_Count_map.html, Conversion_Mean_map.html and Missed_Conversion_map.html\n",
    "\n",
    "It is a great tool for in depth analysis of conversion counts and conversion rates \n",
    "throughout the United States.  Something that really sticks out is the largest conversion count is in New York by far.  New York also has a relatively low conversion rate compared to other areas.  This should be a great area to focus on.\n",
    "\n",
    "The general trend is that conversions are mostly around metropolitan areas.  States like Wyoming, Montana, Idaho, and the Dakohtas have very small conversion counts.\n",
    "\n",
    "If I had more time I would probably create a new feature that would be something like number of impressions per city divided by conversion rate for that city squared.  The largest value's would indicate the most important cities to focus on.\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}