Bootstrap_sale_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Should the company increase the price to \\\\$ 59 from \\\\$39?\n",
    "\n",
    "## Out line of the problem\n",
    "- ### VP of the product want to increase the sales by increasing the price of the software.\n",
    "\n",
    "- ### Actionable insight for increasing conversion rate and (sales?)\n",
    "\n",
    "## Approach\n",
    "\n",
    "- ### Check if the sales increased by raising the price to \\\\$59\n",
    "\n",
    "- ### Check if any user segment behaved differently\n",
    "\n",
    "- ### Confirm hypothesis by getting confidence intervals for each value using bootstrap\n",
    "\n",
    "## Summary\n",
    "- ### Overall, increasing the price to \\\\$59 decreased the conversion rate but significantly increased the sales.\n",
    "\n",
    "- ### Sales from \"mobile\" customers signficantly increased. Sales from \"web\" customers increased but the confidence interval was very close to 1 for sales ratio.\n",
    "\n",
    "- ### Sales increased significantly for \"iOS\" and \"mac\" users, but \"linux\" users did not buy higher priced software.\n",
    "\n",
    "- ### Users with \"friend referral\" has higher conversion rate.\n",
    "\n",
    "- ### Customers coming from \"ads-google\" and \"seo-google\" has higher sales during the test.\n",
    "\n",
    "- ### Customers coming from \"seo-bing\" and \"seo-yahoo\" has lower sales during the test, but need more data to determine it's significance.\n",
    "\n",
    "- ### Sales for customers in the \"east\" increased significantly for the test.\n",
    "\n",
    "- ### Sales for customers in the \"south\" did not significantly increase for the test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relevant analyses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import necessary packages\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "color = sb.color_palette()\n",
    "%matplotlib inline\n",
    "\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in the data: two tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>device</th>\n",
       "      <th>operative_system</th>\n",
       "      <th>test</th>\n",
       "      <th>price</th>\n",
       "      <th>converted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>604839</td>\n",
       "      <td>2015-05-08 03:38:34</td>\n",
       "      <td>ads_facebook</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>624057</td>\n",
       "      <td>2015-05-10 21:08:46</td>\n",
       "      <td>seo-google</td>\n",
       "      <td>mobile</td>\n",
       "      <td>android</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>317970</td>\n",
       "      <td>2015-04-04 15:01:23</td>\n",
       "      <td>ads-bing</td>\n",
       "      <td>mobile</td>\n",
       "      <td>android</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>685636</td>\n",
       "      <td>2015-05-07 07:26:01</td>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>820854</td>\n",
       "      <td>2015-05-24 11:04:40</td>\n",
       "      <td>ads_facebook</td>\n",
       "      <td>web</td>\n",
       "      <td>mac</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id            timestamp          source  device operative_system  \\\n",
       "0   604839  2015-05-08 03:38:34    ads_facebook  mobile              iOS   \n",
       "1   624057  2015-05-10 21:08:46      seo-google  mobile          android   \n",
       "2   317970  2015-04-04 15:01:23        ads-bing  mobile          android   \n",
       "3   685636  2015-05-07 07:26:01  direct_traffic  mobile              iOS   \n",
       "4   820854  2015-05-24 11:04:40    ads_facebook     web              mac   \n",
       "\n",
       "   test  price  converted  \n",
       "0     0     39          0  \n",
       "1     0     39          0  \n",
       "2     0     39          0  \n",
       "3     1     59          0  \n",
       "4     0     39          0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read the test_results.csv\n",
    "current_path = os.getcwd()\n",
    "file = '/datasets/Cost/test_results.csv'\n",
    "test_results = pd.read_csv(current_path + file)\n",
    "\n",
    "test_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</th>\n",
       "      <td>510335</td>\n",
       "      <td>Peabody</td>\n",
       "      <td>USA</td>\n",
       "      <td>42.53</td>\n",
       "      <td>-70.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>89568</td>\n",
       "      <td>Reno</td>\n",
       "      <td>USA</td>\n",
       "      <td>39.54</td>\n",
       "      <td>-119.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>434134</td>\n",
       "      <td>Rialto</td>\n",
       "      <td>USA</td>\n",
       "      <td>34.11</td>\n",
       "      <td>-117.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>289769</td>\n",
       "      <td>Carson City</td>\n",
       "      <td>USA</td>\n",
       "      <td>39.15</td>\n",
       "      <td>-119.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>939586</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>USA</td>\n",
       "      <td>41.84</td>\n",
       "      <td>-87.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id         city country    lat    long\n",
       "0   510335      Peabody     USA  42.53  -70.97\n",
       "1    89568         Reno     USA  39.54 -119.82\n",
       "2   434134       Rialto     USA  34.11 -117.39\n",
       "3   289769  Carson City     USA  39.15 -119.74\n",
       "4   939586      Chicago     USA  41.84  -87.68"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read in the user data\n",
    "file = '/datasets/Cost/user_table.csv'\n",
    "user = pd.read_csv(current_path + file)\n",
    "\n",
    "user.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge dataframe so we can find where each user lives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>source</th>\n",
       "      <th>device</th>\n",
       "      <th>operative_system</th>\n",
       "      <th>test</th>\n",
       "      <th>price</th>\n",
       "      <th>converted</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>0</th>\n",
       "      <td>604839</td>\n",
       "      <td>2015-05-08 03:38:34</td>\n",
       "      <td>ads_facebook</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>USA</td>\n",
       "      <td>42.89</td>\n",
       "      <td>-78.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>624057</td>\n",
       "      <td>2015-05-10 21:08:46</td>\n",
       "      <td>seo-google</td>\n",
       "      <td>mobile</td>\n",
       "      <td>android</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Lakeville</td>\n",
       "      <td>USA</td>\n",
       "      <td>44.68</td>\n",
       "      <td>-93.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>317970</td>\n",
       "      <td>2015-04-04 15:01:23</td>\n",
       "      <td>ads-bing</td>\n",
       "      <td>mobile</td>\n",
       "      <td>android</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Parma</td>\n",
       "      <td>USA</td>\n",
       "      <td>41.38</td>\n",
       "      <td>-81.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>685636</td>\n",
       "      <td>2015-05-07 07:26:01</td>\n",
       "      <td>direct_traffic</td>\n",
       "      <td>mobile</td>\n",
       "      <td>iOS</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>Fayetteville</td>\n",
       "      <td>USA</td>\n",
       "      <td>35.07</td>\n",
       "      <td>-78.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>820854</td>\n",
       "      <td>2015-05-24 11:04:40</td>\n",
       "      <td>ads_facebook</td>\n",
       "      <td>web</td>\n",
       "      <td>mac</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>Fishers</td>\n",
       "      <td>USA</td>\n",
       "      <td>39.95</td>\n",
       "      <td>-86.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id            timestamp          source  device operative_system  \\\n",
       "0   604839  2015-05-08 03:38:34    ads_facebook  mobile              iOS   \n",
       "1   624057  2015-05-10 21:08:46      seo-google  mobile          android   \n",
       "2   317970  2015-04-04 15:01:23        ads-bing  mobile          android   \n",
       "3   685636  2015-05-07 07:26:01  direct_traffic  mobile              iOS   \n",
       "4   820854  2015-05-24 11:04:40    ads_facebook     web              mac   \n",
       "\n",
       "   test  price  converted          city country    lat   long  \n",
       "0     0     39          0       Buffalo     USA  42.89 -78.86  \n",
       "1     0     39          0     Lakeville     USA  44.68 -93.24  \n",
       "2     0     39          0         Parma     USA  41.38 -81.73  \n",
       "3     1     59          0  Fayetteville     USA  35.07 -78.90  \n",
       "4     0     39          0       Fishers     USA  39.95 -86.02  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results_user = pd.merge(test_results, user, on = 'user_id', how='left')\n",
    "\n",
    "test_results_user.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First, group by test and find if the coversion rate is significantly different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'test')"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Group by 'test'\n",
    "test_control_group = test_results_user.groupby('test') \n",
    "\n",
    "#test group got $59 price, control $39\n",
    "test_group = test_control_group.get_group(1) \n",
    "control_group = test_control_group.get_group(0)\n",
    "\n",
    "test_rate = sum(test_group['converted'])/test_group.shape[0]\n",
    "\n",
    "control_rate = sum(control_group['converted'])/control_group.shape[0]\n",
    "\n",
    "#plot the control rate and the test rate\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "#fig.suptitle('Current and target turnover rate')\n",
    "sb.barplot(control_rate*100, orient = 'v', ax =ax1)\n",
    "ax1.set_ylabel('converted/total (%)')\n",
    "ax1.set_xlabel('control')\n",
    "#ax1.set_ylim((0,10)) \n",
    "sb.barplot(test_rate*100, orient = 'v', color = 'red', ax = ax2)\n",
    "ax2.set_xlabel('test')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### test sites has lower sales number, but we want to compare the total sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'test sales/control sales')"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_ratio = (59/39 * test_rate)/control_rate\n",
    "ax1 = sb.barplot(sales_ratio,orient='v')\n",
    "ax1.set_ylabel('test sales/control sales')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Use bootstrap to find the confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we need to bootstrap to find the confidence interval\n",
    "control_converted=control_group['converted']\n",
    "test_converted = test_group['converted']\n",
    "\n",
    "sales_ratio_boot = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'test sales/control sales')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sb.distplot(sales_ratio_boot)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.113673\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.245304\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if there are any difference in the sales ratio with \"device\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mobile    186471\n",
       "web       130329\n",
       "Name: device, dtype: int64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#About equal number of mobile and web customers.\n",
    "device = test_results_user['device']\n",
    "device_counts = device.value_counts()\n",
    "device_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mobile', 'web'], dtype=object)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device_list=test_group.device.unique()\n",
    "device_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01987865, 0.01613881],\n",
       "       [0.01994018, 0.01472454]])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#For all device types get the control conversion rate and test conversion rate.\n",
    "#First column for control, second for the test\n",
    "device_conversion_rate = np.zeros((device_list.shape[0],2))\n",
    "\n",
    "test_group_device = test_group.groupby('device') \n",
    "control_group_device = control_group.groupby('device') \n",
    "\n",
    "\n",
    "n=0\n",
    "for device in device_list:\n",
    "    device_group_control=control_group_device.get_group(device) \n",
    "    device_conversion_rate[n,0] = sum(device_group_control['converted'])/device_group_control.shape[0]\n",
    "    \n",
    "    device_group_test=test_group_device.get_group(device) \n",
    "    device_conversion_rate[n,1] = sum(device_group_test['converted'])/device_group_test.shape[0]\n",
    "    \n",
    "    n = n+1\n",
    "device_conversion_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for mobile customers.\n",
    "\n",
    "device_group_control=control_group_device.get_group('mobile')\n",
    "device_group_test = test_group_device.get_group('mobile')\n",
    "\n",
    "control_converted=device_group_control['converted']\n",
    "test_converted = device_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_mobile = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_mobile[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for web customers.\n",
    "\n",
    "device_group_control=control_group_device.get_group('web')\n",
    "device_group_test = test_group_device.get_group('web')\n",
    "\n",
    "control_converted=device_group_control['converted']\n",
    "test_converted = device_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_web = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_web[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the control rate and the test rate\n",
    "#fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)\n",
    "#fig.suptitle('Current and target turnover rate')\n",
    "ax1=sb.distplot(sales_ratio_boot_mobile)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales (mobile = blue, web = orange)')\n",
    "\n",
    "ax1=sb.distplot(sales_ratio_boot_web)\n",
    "sb.despine()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.146055\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.325107\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_mobile, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.03041\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.224879\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_web, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if \"operative system\" affect the sales ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "windows    100976\n",
       "iOS         95465\n",
       "android     74935\n",
       "mac         25085\n",
       "other       16204\n",
       "linux        4135\n",
       "Name: operative_system, dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Small enough to be able to check each category.\n",
    "os = test_results_user['operative_system']\n",
    "os_counts = os.value_counts()\n",
    "os_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['iOS', 'android', 'mac', 'windows', 'other', 'linux'], dtype=object)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the list of operative systems\n",
    "os_list=test_results_user.operative_system.unique()\n",
    "os_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02358742, 0.01999405, 1.28235327],\n",
       "       [0.0163659 , 0.01234799, 1.14141548],\n",
       "       [0.02547965, 0.02121803, 1.25979229],\n",
       "       [0.01869761, 0.01398957, 1.13189335],\n",
       "       [0.01400267, 0.01104101, 1.19284886],\n",
       "       [0.01540553, 0.        , 0.        ]])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for different operative systems. 3rd column is the sales ratio\n",
    "os_conversion_rate = np.zeros((os_list.shape[0],3))\n",
    "\n",
    "test_group_os = test_group.groupby('operative_system') \n",
    "control_group_os = control_group.groupby('operative_system') \n",
    "\n",
    "\n",
    "n=0\n",
    "for os in os_list:\n",
    "    os_group_control=control_group_os.get_group(os) \n",
    "    os_conversion_rate[n,0] = sum(os_group_control['converted'])/os_group_control.shape[0]\n",
    "    \n",
    "    os_group_test=test_group_os.get_group(os) \n",
    "    os_conversion_rate[n,1] = sum(os_group_test['converted'])/os_group_test.shape[0]\n",
    "    \n",
    "    os_conversion_rate[n,2] = (59/39 * os_conversion_rate[n,1])/os_conversion_rate[n,0]\n",
    "    \n",
    "    n = n+1\n",
    "\n",
    "os_conversion_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### iOS and Mac users buy more and also pay higher prices. Linux users don't buy higher priced product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [{'iOS': 1.28, 'android': 1.14, 'mac': 1.26, 'windows': 1.13, 'other': 1.19, 'linux': 0.0}]\n",
    "df = pd.DataFrame(data) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the control rate and the test rate\n",
    "fig, (ax1, ax2, ax3, ax4, ax5, ax6) = plt.subplots(1, 6, sharey=True)\n",
    "#fig.suptitle('Current and target turnover rate')\n",
    "sb.barplot(df['iOS'], orient = 'v', color = 'red', ax =ax1)\n",
    "ax1.set_ylabel('sales ratio (test/control)')\n",
    "ax1.set_xlabel('iOS')\n",
    "#ax1.set_ylim((0,10)) \n",
    "sb.barplot(df['android'], orient = 'v', color = 'blue', ax = ax2)\n",
    "ax2.set_xlabel('android')\n",
    "\n",
    "\n",
    "sb.barplot(df['mac'], orient = 'v', color = 'red', ax = ax3)\n",
    "ax3.set_xlabel('mac')\n",
    "\n",
    "sb.barplot(df['windows'], orient = 'v', color = 'blue', ax = ax4)\n",
    "ax4.set_xlabel('windows')\n",
    "\n",
    "sb.barplot(df['other'], orient = 'v', color = 'blue', ax = ax5)\n",
    "ax5.set_xlabel('other')\n",
    "\n",
    "sb.barplot(df['linux'], orient = 'v', color = 'blue', ax = ax6)\n",
    "ax6.set_xlabel('linux')\n",
    "\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for iOS customers.\n",
    "\n",
    "os_group_control=control_group_os.get_group('iOS')\n",
    "os_group_test = test_group_os.get_group('iOS')\n",
    "\n",
    "control_converted=os_group_control['converted']\n",
    "test_converted = os_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_ios = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_ios[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for mac customers.\n",
    "\n",
    "os_group_control=control_group_os.get_group('mac')\n",
    "os_group_test = test_group_os.get_group('mac')\n",
    "\n",
    "control_converted=os_group_control['converted']\n",
    "test_converted = os_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_mac = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_mac[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sb.distplot(sales_ratio_boot_ios)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales (ios = blue, mac = orange)')\n",
    "\n",
    "ax1=sb.distplot(sales_ratio_boot_mac)\n",
    "sb.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.168033\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.405765\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_ios, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.060504\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.488743\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_mac, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if \"source\" affect the sales or sales ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "direct_traffic     60357\n",
       "ads-google         59379\n",
       "ads_facebook       53396\n",
       "ads_other          29876\n",
       "seo-google         23175\n",
       "ads-bing           22873\n",
       "seo_facebook       21205\n",
       "friend_referral    20695\n",
       "seo-other           9260\n",
       "ads-yahoo           7583\n",
       "seo-yahoo           6848\n",
       "seo-bing            2153\n",
       "Name: source, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Probably worth going through the difference in click rates by source.\n",
    "source = test_results_user['source']\n",
    "source_counts = source.value_counts()\n",
    "source_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ads_facebook', 'seo-google', 'ads-bing', 'direct_traffic',\n",
       "       'ads-google', 'seo_facebook', 'friend_referral', 'ads_other',\n",
       "       'ads-yahoo', 'seo-yahoo', 'seo-bing', 'seo-other'], dtype=object)"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#list of sources in the order\n",
    "source_list=test_results_user.source.unique()\n",
    "source_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.02366638, 0.016842  , 1.07658687],\n",
       "       [0.01754734, 0.01597919, 1.37762417],\n",
       "       [0.01334794, 0.00955954, 1.08345297],\n",
       "       [0.01354524, 0.01009353, 1.12731068],\n",
       "       [0.02255441, 0.01961422, 1.31560988],\n",
       "       [0.01744488, 0.01357642, 1.17734708],\n",
       "       [0.04172059, 0.03346561, 1.21348846],\n",
       "       [0.01540137, 0.01246224, 1.22412012],\n",
       "       [0.01681196, 0.01121157, 1.00887124],\n",
       "       [0.01948201, 0.01046278, 0.81245749],\n",
       "       [0.02998501, 0.01343101, 0.67762907],\n",
       "       [0.01749024, 0.01245921, 1.07766126]])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source_conversion_rate = np.zeros((source_list.shape[0],3))\n",
    "\n",
    "test_group_source = test_group.groupby('source') \n",
    "control_group_source = control_group.groupby('source') \n",
    "\n",
    "\n",
    "n=0\n",
    "for source in source_list:\n",
    "    source_group_control=control_group_source.get_group(source) \n",
    "    source_conversion_rate[n,0] = sum(source_group_control['converted'])/source_group_control.shape[0]\n",
    "    \n",
    "    source_group_test=test_group_source.get_group(source) \n",
    "    source_conversion_rate[n,1] = sum(source_group_test['converted'])/source_group_test.shape[0]\n",
    "    \n",
    "    source_conversion_rate[n,2] = (59/39 * source_conversion_rate[n,1])/source_conversion_rate[n,0]\n",
    "   \n",
    "    n = n+1\n",
    "\n",
    "source_conversion_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# intialise data of lists. \n",
    "data2 = {'control rate':source_conversion_rate[:,0], 'sales ratio':source_conversion_rate[:,2],'source': source_list} \n",
    "  \n",
    "# Create DataFrame \n",
    "df2 = pd.DataFrame(data2) \n",
    "sb.lineplot(x = df2['source'], y = df2['control rate'], markers = True)\n",
    "sb.despine()\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],\n",
       " <a list of 12 Text xticklabel objects>)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lineplot(x = df2['source'], y = df2['sales ratio'], markers = True)\n",
    "sb.despine()\n",
    "plt.xticks(rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for ads-google customers.\n",
    "\n",
    "source_group_control=control_group_source.get_group('ads-google')\n",
    "source_group_test = test_group_source.get_group('ads-google')\n",
    "\n",
    "control_converted=source_group_control['converted']\n",
    "test_converted = source_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_adsgoogle = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_adsgoogle[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for seo-google customers.\n",
    "\n",
    "source_group_control=control_group_source.get_group('seo-google')\n",
    "source_group_test = test_group_source.get_group('seo-google')\n",
    "\n",
    "control_converted=source_group_control['converted']\n",
    "test_converted = source_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_seogoogle = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_seogoogle[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for seo-bing customers.\n",
    "\n",
    "source_group_control=control_group_source.get_group('seo-bing')\n",
    "source_group_test = test_group_source.get_group('seo-bing')\n",
    "\n",
    "control_converted=source_group_control['converted']\n",
    "test_converted = source_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_seobing = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_seobing[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for seo-yahoo customers.\n",
    "\n",
    "source_group_control=control_group_source.get_group('seo-yahoo')\n",
    "source_group_test = test_group_source.get_group('seo-yahoo')\n",
    "\n",
    "control_converted=source_group_control['converted']\n",
    "test_converted = source_group_test['converted']\n",
    "\n",
    "sales_ratio_boot_seoyahoo = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_seoyahoo[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEGCAYAAABM7t/CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAgAElEQVR4nO3deXxcZb348c93Jvu+t2nSJd1bSiltoSyyKlDAK1fZBdzwIl7F5arX5Xr9uV1FvVe9F69XQAEREEUWERCo7CAUuu+lbdqm2Zq0zZ5MMpl5fn88Z9o0zSSTZNbM9/165ZXJzJlzvmfmzDfPPOd5vkeMMSillIpfrlgHoJRSaniaqJVSKs5polZKqTiniVoppeKcJmqllIpzKZFY6cqVK82zzz4biVUrpdREJcEeiEiL+tChQ5FYrVJKJSXt+lBKqTiniVoppeKcJmqllIpzmqiVUirOaaJWSqk4p4laKaXinCZqpZSKc5qolVIqzmmiVkqpOBeRKeRKDfTQ6poT7vvwimkxiESpxKQtaqWUinOaqJVSKs5polZKqTiniVoppeKcJmqllIpzmqiVUirOaaJWSqk4p4laKaXinCZqpZSKc5qolVIqzmmiVkqpOKeJWiml4pwWZVJxQ4s3KTU0bVErpVSc00StlFJxThO1UkrFuZD6qEVkH9AB+IB+Y8zySAalJjZjTKxDUCqhjOZk4gXGmEMRi0RNaMYYNtW2sWr7QVq7+3h8fR1fvmQeZ8wsjnVoSsU97fpQUfHKu838Yc0BMlPdnDWrhIY2Dzf9ZjXPbmmMdWhKxb1QE7UBnheRtSJyy1ALiMgtIrJGRNY0NzeHL0KV8HY2drBq20EWV+Zz63mzuOzkcp75/DksqsjnC39YT3VzZ6xDVCquhZqozzbGLAUuBT4jIucOXsAYc5cxZrkxZnlpaWlYg1SJy+P18eTGOkpz0/nQqZW4XQJAfmYqd964jPQUN196ZCN+v/ZbKxVMSInaGFPv/G4CHgdOj2RQauK4/819tHR7ef/iKaSlHH+4leVl8M3LF7C+ppXnt2kXiFLBjJioRSRbRHIDt4GLgS2RDkwlPo/Xx52vVDOnLIfZZTlDLvOhpZVUlWRzx4u7dTSIUkGE0qKeBLwuIhuBt4GnjTHPRjYsNRE8ubGew119nDs3eFeY2yX88/mz2Frfzp7mrihGp1TiGDFRG2OqjTGnOD8nGWP+IxqBqcRmjOHeN/Yxb1IuM0uyh132H06ZQl5GCutqWqIUnVKJRYsyqbAKFFaqa+1he0M7VyyZgogM+5yMVDfvP2UKj6w5QG//FNJT3NEIVamEoeOoVUSsr2nB7RIWVxSEtPyVSyvw+gzb6tsjHJlSiUcTtQo7n9+wsbaN+ZNzyUwLrXW8dFoheRkpbG/siHB0SiUeTdQq7KoPddLV28+SqaG1pgFEhDmTctnd1IFPx1QrdRxN1Crstta3k+Z2MXdS7qieN3dSLh6vnwNHuiMUmVKJSU8mqrDyG9vPPHdyLqnu4O2Aoa7mMrs0B5fAuwc7mDHCSBGlkom2qFVYHTjSTWdvPydNyRv1czPT3FQUZLL3sI6nVmogTdQqrHY2duASmDfKbo+A6cXZ1LX00O/zhzkypRKXJmoVVu82dTCtKIuM1LGNhZ5WlEW/31Df5glzZEolLk3UKmyaO3qpb/WM+iTiQNOLswDYr90fSh2liVqFzeu7bR3yOeNI1LkZqRRlp1GjIz+UOkoTtQqbV3Y2k53mpjw/Y1zrmVqYqUP0lBpAE7UKC7/f8OquQ8yZlItrhNoeI5lSkEm7p5+u3v4wRadUYtNErcJiS30bR7r6mBOk7vRolOdnAtCgJxSVAjRRqzB5Zef4+6cDAl0nDW09416XUhOBJmoVFq/tPsSiijxy0sc/2TU7PYX8zFRtUSvl0EStxs3j9bGhppUzZxaHbZ3l+RnUt2qLWinQRK3CYH1NK30+Pyuqwpuomzt68Xh9YVunUolKE7Uat9V7DyMCp1UVhW2dk/IyMMDeQzrxRSlN1Grc3qo+zMLyPPIzU8O2ztLcdAB2NXWGbZ1KJSpN1Gpcevt9rK9pDWu3B0BJTjoC7D6oV3xRShO1GpeNB9ro7fezYmb4uj0AUt0uirLT2N2sLWqlNFGrcVldfRiA02eEN1EDlOVlsOugJmqlNFGrcVm99wjzJ+dSmJ0W9nWX5aaz91AXXq1NrZKcJmo1Zn39ftbub+GMMI6fHqgsN51+v9GSpyrp6TUT1Zj9bNW79Hh99Pb7h7wG4niV5NiRH9XNXcwuG//UdKUSlbao1ZgFakZPL8qKyPoDiXqftqhVktNErcZs/5FuCrJSyQvj+OmBMtPcFGalsu+w1qZWyS3kRC0ibhFZLyJPRTIglRiMMdQc7mJahFrTAdOLs9mnsxNVkhtNi/rzwPZIBaISS32bh3ZPf8S6PQKqSjRRKxVSohaRSuBy4NeRDUclinX7WwCYVpQd0e3MKM6mvs2jxZlUUgu1Rf1z4F+BoANaReQWEVkjImuam5vDEpyKX2v3t5DqFiaP8/qII5lRYlvserFblcxGTNQi8n6gyRizdrjljDF3GWOWG2OWl5aWhi1AFZ/W1bRQWZiF2zW+6yOOZEaxbbFrFT2VzEJpUZ8NfEBE9gEPAxeKyAMRjUrFte6+frbWt0e8fxpgRolN1NpPrZLZiInaGPN1Y0ylMWYGcB3wojHmxohHpuLWpto2fH7DtOLIJ+r8zFSKstN0iJ5KajqOWo3auhrnRGJh5BM1wPTiLG1Rq6Q2qkRtjHnZGPP+SAWjEsPGA61UlWSTFYYL2YaiqjhbZyeqpKYtajVqGw+0cUplftS2N6Mkm4Y2Dz19OkRPJSdN1GpUGts8NLZ7OGVqQdS2GTihqEP0VLLSRK1GZWNtK0B0E7Vz0lKH6KlkpYlajcrGA62kuISF5XlR2+bRIXraT62SlCZqNSoba1tZUJ5HRqo7atvMy0ilODtNR36opKUXDlAheWh1DX5jWLOvhSVTCyJyoYDhTC/OYr+OpVZJSlvUKmSHOnvp7fdTGaXx0wPNKM7WS3KppKWJWoWstqUHgMrCzKhve7pW0VNJTBO1ClltSzfpKS5Kc9Ojvu1AFb0DOkRPJSFN1CpktS09VBRk4pLIVswb6KHVNTy0uoYdDR0A3P/m/qhtW6l4oYlahcTnNzS0eagoiH63B0BxThoAhzt7Y7J9pWJJE7UKSVOHB5/fMCUG/dMAWWkpZKa6OdzVF5PtKxVLmqhVSOpb7YnEKfmxSdRgW9WaqFUy0kStQlLf6iEtxXW0CyIWirLTtOtDJSVN1Cok9a09lOdnRPVE4mDF2em0dnvp6w966U6lJiRN1GpEgROJU2J0IjGgOCcNgx0mqFQy0UStRrT3UBd9Pn9M+6cBirNtt4tOJVfJRhO1GtHW+jYAphRkxDSO4hw70Uar6Klko4lajWhrfTspLqEsN7aJOjvNTXqKS1vUKuloolYj2lLXxuT8DNyu2J1IBBARW+5UW9QqyWiiVsMyxrClro3yGPdPBxTlpGuLWiUdTdRqWLUtPbR7+mPePx1Qkp3GgSPdeH06RE8lD03UaliBE4mxqvExWGluOv1+o7WpVVLRRK2GtaWuHbdLmJQXHy3qQInV3U2aqFXy0EtxqRMMvMzW89saKc1JJ9UdH//TA4l6T3NnjCNRKnri49On4pIxhrpWT9z0TwOkp7gpz89gd5MmapU8NFGroDo8/XT19sd86vhgs8tyNFGrpKKJWgVV3xb70qZDmVWaw57mTowxsQ5FqagYMVGLSIaIvC0iG0Vkq4h8JxqBqdirb/UAUJ4fP10fYFvU3X0+Gto8sQ5FqagIpUXdC1xojDkFWAKsFJEzIhuWigeN7R6KstNIT3XHOpTjzCrNAdDuD5U0RkzUxgp8IlKdH/3OmQQOtnmYHCfD8gaaXaaJWiWXkPqoRcQtIhuAJmCVMWZ1ZMNSseb1+TnU2cvkOOv2ACjJSSM/M1WH6KmkEVKiNsb4jDFLgErgdBFZNHgZEblFRNaIyJrm5uZwx6mirKm9FwNx2aIWER35oZLKqEZ9GGNagZeBlUM8dpcxZrkxZnlpaWmYwlOx0thuR3zEY4saYLYz8kOpZBDKqI9SESlwbmcC7wN2RDowFVuNbR5S3UJRduwuZjucWWXZHOrso7Vbr0quJr5QWtTlwEsisgl4B9tH/VRkw1Kx1tDuYVJebC9mO5zACUVtVatkMGKtD2PMJuDUKMSi4oQxhsY2DwvL82IdSlBzynIB2NnYybLpRTGORqnI0pmJ6gQdvf109/nitn8aoLIwk9z0FLY3tMc6FKUiThO1OkGjM+MvHkd8BIgI88tzNVGrpKCJWp0gERI1wILyPHY0duD36/wrNbFpolYnaGz3kJeRQlZ6fJcrX1CeR2dvPwda9BqKamLTRK1OcLDdE9f90wGBk53a/aEmuvhuMqmo8/r8NLX3Hh1VES6zah4ZcZk9064e1TrnTc7FJbCtvp2Vi8rHGppScU9b1Oo41c1d+IxJiBZ1RqqbuZNy2VDbFutQlIoobVGr4+xotN0IsUjUobS6AVjxpaM3l0wt4K9bGjHGIHE6OUep8dIWtTrO9oYO3CKU5qTHOpSQLJlaQFuPl32H9YSimrg0Uavj7GhspywvHbcrMVqnp0wtAGDDgZYYR6JU5GjXhzrOjoaOqF96K9XbQV7XPtL7Wkj1deHye/G5M+h3Z9GdXkpX5hS8qUOf3Jw7KZesNDcbalr54KmVUY1bqWjRRK2Oaunqo7HdwxKnlRpJKf3dlLasp7RtI5m9h47e3+/OwC+puP0e3H7v0fs9aYW05MylNW8u+H3gspcHc7uEkyvy9YSimtA0UaujdjR2AJG9mG1KfxeVTa9Q2roBl+mnPWsaTZPeR3t2FT3ppRjXsUPS5esjy9NITk89+V3VTGpZQ/mR1fDzl+CU62DJh6F4FkumFXDv6/vo7feRnhJf13dUKhw0UaujAhNHIjLiw/iZfPgtKppfw+3vo7nwVBqLTqcnoyzoU/zuNDqzp9GZPY3GkjNw+fso6NhFdu9BJr/2U1yv/SdNhUuZk30J4pvHtvp2Tp1WGP7YlYoxTdTqqB2N7ZTkpJGbkRrW9eZ1VnPS3nvI6amnJWcONZMvwpNeMur1+F1pHMk/iXemfZtMTxMz6p9iZu0TXNXyQ85Pz+PAy9fDh74MOcGTv1KJSBO1Omp7QwfzJ4e3BvWsA39i2bbbMeJiV+VVHMlfGJb19mSUsX3mJ9he9XHKjrxD+ju/5JzqO+Fn98LJ18BZt0HZ/LBsS6lY00StAOj3+Xn3YAc3nTE9LOtz+b0s2/ZD5hx4hIbiM2koPiPoyI1xEaGp+HQeKJjEf7VV8+OKN5i5+VHcGx5k35T3U3XV96CoKvzbVSqKNFErAPYd7qa338+C8jx6+/3jWleqt43z1n6OspZ1bJ15M5vm3sbMA4+FKdKhTS/O4q8NJbw466tsmvNZFlbfw/x9v4M7noFZF8Kci8Ad5PqPyz8e0diUGi9N1Ao4diJxfnkuGw+MfahbRu8hLnjnU+R1VvPGKT9i/5TLwhXisKpKsgHYe6iLvMoCNsz/FzypBSztfh12r4K6tbD4WiidF5V4lAonTdQKsCcSU1zC7LKcUSXqgfU50rxtLNj3O1K9Hbw77VpS+rtCr98xTuX5maSnuNh7qItTKu04cG9qLiy5AaaugM1/hNX/BzMvgHmXg1sPfZU4dAq5AuyMxFmlOWMeh5zS3838fQ+Q0t/Fjhk30p4zK8wRDs/tEmYUZ7O3uevEB4tnwzlfhulnQ/VL8Pf/hh6dcq4ShzYrFGC7Pk6rGtvVvF2+XubVPES6t40d02+gM2tqmKM7XrBW+rKUIh7qLKN4z2MUpPqOf9CdBidfDaXzYcMD8PpPYfnNUDgjorEqFQ7aola0dXupb/OwoHwMQ/OMnzm1j5Ld08CuqVfSkR2eUSNjsTDXVtDb3pEVfKHJJ8PZX7CJ+81fQMOmKEWn1NhpolZHa1DPnzz64XMVza9Q0LmbfeWX0pob2xN1VVkeMlw+tnUOk6gBcsvhPV+EvApYdx9s+mNU4lNqrDRRq6MjPkbboq44+BKVza/RVLCEpsJlkQhtVFIE5uX0sG24FnVAWg6c8WkomgmP3QLr7o98gEqNkSZqxY7GDoqy0yjLDf1iAVk99Zy56d/ozJjCvvLLIE6urrIgp5taTzrt3hBOiqZkwOm3wOz3wpOfgy2PRj5ApcZATyYqtjd2MH9y7nGXshp2WJ0xLNh3P25/L7umfvy4inexdpLTT721M4szCztGfoI7Da75HTxwJTz2KUjPhznvi3CUSo2OtqiTnM9v2NnYPqoaH5OOvE1e9372T76EvrTI164ejVnZHjJdPja3Z4f+pLQs+PDDtjbIH26E+g2RC1CpMdBEneT2H+7C4/WzoDy0E4kZvYeYdvAFWnLm0FywJMLRjZ5bbKt6U3sWxoziiRn5cONjkF0CD38YOhojFqNSozViohaRqSLykohsF5GtIvL5aASmomN7g+0eCOlEojFU1T+NX1LYO+X9cdMvPdjivC6a+9I42DvKcq05ZXDdQ3YyzMM3gNcTmQCVGqVQOhf7gS8ZY9aJSC6wVkRWGWO2RTg2FQU7GttxO1PHR1LctoW87v3sLb8sMpXwwmRxnp2duLE9mytCecKaewet4FpYey/cf4W9kkyAFm9SMTJiojbGNAANzu0OEdkOVACaqCeA57cepCQnjcfW1Q27nNvXy7SDq+jMmEJT4dIoRTc2k9O9lKZ52dyRDfSMfgXlp8Dsi2wxp5I5UBH7oYcquY2qj1pEZgCnAquHeOwWEVkjImuam5vDE52KKGMMta09VBSMPO64oullUvs72TflUpD4PrUhYlvVW9qzGHPF1rkrobDKTobp1ONZxVbInzgRyQEeBb5gjGkf/Lgx5i5jzHJjzPLS0tJwxqgipLHdQ1dvPxUFw18jMaP3EJOPvE1T4VK6MiuiFN34nJzXRY/fzcYjYxw66HLD0o/Y3+t+a698rlSMhJSoRSQVm6QfNMZEtgK8ippNtbacaUXh8C3qqQdfwO9Kpbbs/ChEFR6LcrsQDK81BblYQCgyC20fdXut7QZRKkZCGfUhwG+A7caYn0Y+JBUtm2vbcAmUD3PV8dyu/RR17KS+5Cz6U0Y+4RgvclP8zMzy8PrBcSRqgMmLoWI57HoeGjaGJzilRimU74VnAzcBm0UkMBPgG8aYZyIXloqGzXVtlOVmkOoO8v/aGKYd/Bt9Kbk0Fp8Z3eDCYHFeF08eLKbDK+SmjmZQ9SAnfRAOvQsPXQvnfAlGmompo0NUmI3YojbGvG6MEWPMYmPMEudHk3SCM8awua6NisLMoMsUduwgp6eO2rLz8btGOSY5DizO68JnhDebxxl7WrYdstfRALtfCE9wSo1CfJ++VxFT19rDka4+KgqCJGrjZ2rTS/SkldBccEp0gwuTudk9ZLn94+/+AJh0EpQvsX3VXToKREWXJuoktaXOOZEYJFEXt20hs/eQPYEY58PxgklxwYpSb3gSNdguEJfbVtkb1fx0pcYnMT+Batw21baR4hImD3EiUfxeKptfoStjEkfyFsQguvA5Z1If1Z0p1HaF4VDPyId5l0HzDmjQwk0qejRRJ6nNdW3MnZQ75InEmXVPktHXQm3ZBXFbzyNU50zqA+D18QzTG2j6eyCvErb9GXx94VmnUiPQRJ2EAicST67IP+Exl9/Lot130pk5hdacOTGILrxm5/qYlOHjtXB1f7jctgvE0wrVL4dnnUqNQBN1Eqo+1EVrt5dTp51YS3pG3V/I9jRQW3pewremwe7COZP6eKMpDV+4upWLZ9nx1bv/Bp62MK1UqeA0USehtftbAFg2vfC4+8Xfz0l77uZw3kLacmbHIrSIOGdSH619Lra2hPFKNAs+YKeV73w6fOtUKghN1Elo3f4W8jJSmFV6/EzDGQ3PkNtTy5bZt06I1nTA2WW2Lzls3R9gLzBQdS4ceAfaDoRvvUoNQRN1ElpX08LS6YW4XMeSsRgfJ+2+i5bcedQlUE2PUJRkGE4q8PJqOBM1wJyL7GW8tv1Zh+upiNJEnWTaery8e7CT5YO6PaY1PEte9342T7DWdMA5k/pYdziVTm8Y9y01C+ZeCod3w8HN4VuvUoNook4y62ts//TSgYna+Fm0+y5ac2ZTO+nCGEUWWedO6sNrhNXjnU4+2LQzIWcSbHsS/P3hXbdSDk3USWbd/hbcLuGUymMjPqY2riK/q5otsz+VsLMQR7Ks2EuGe5xlT4ficsPCK6D7EOx7LbzrVsoxMT+VKqi1NS0sKM8lO90ZAWH8LNpzJ23ZVRyYfFFsg4ugdDecUdoX/n5qgNIFUDoPdq2Cvu7wr18lPU3USaTf52dDTSvLph3r9qg8+BKFHbvYOusWjLhjGF3knTOpj+qOME0nH0gEFlwB3h69wICKCE3USWTnwQ66+nzH+qeNYdGeO2nPms7+8pWxDS4Kzg33dPKB8qbA1NNg36twZG/416+SWhhnAKh4987eIwAsa/sbrPHDwa0UtW9nT8UVzKx9PMbRRcZqZ5/BjqArSs3jtYNpXFflCf/G5l4Gdevhhe/C1feGf/0qaWmLOoms3nuEiiwfldl+m7XefQ5PaiGH8xfFOrSoCFyd/PWDYZxOPlBmAcy6ALY+BrVrIrABlaw0UScJYwxv7z3CilKn4lvzDmirob707AnfNz3Q4rwu2rwuNodzOvlAsy6E7FJ4/ps6CUaFjSbqJLG7qZPDXX2cUeK1CWTXc5BZyKH8xLx6y1idnNdtr04eidEfACkZcME3oOZN2KF1QFR4aKJOEm9VHwYgo7eZ7VvWQss+9haciXElT2saIC/Fx0kF/ZFL1ACnfgRK5sGqb4HPG7ntqKShiTpJvLX3CPmZqZSlealofpW+lFyaC5bEOqyYiMh08oHcKXDRd+HIHnj77shsQyUVHfUxQT20uubobWMMr+xsZnZZDnnd+8jrrmHf5JUYV3K+/SXmMP0mm/u39LKsoBOAFVVF4d3I3Etg1nvh5dvh5KshpzS861dJRVvUSeBQZx+dvf1UFWc7rekcmgpPjXVYMTMvu4d0l5+N7dmR24gIrLwdvF3wwncitx2VFDRRJ4HqQ7bVeFbaLvK79tFQfBbGFebiRAkk1WVYmNPNpvasyG6odC6suBXWPwB1ayO7LTWhaaJOAnsPdZGbkcI5tXfRl5JNU9GyWIcUc4vzumjoTaepN8L/sM77KuSUwdNfsleEUWoMNFFPcMYYqpu7+EDeHiYfeZv6kvfgT+LWdMDivC4ANke6VZ2RB5f8AOrXw+o7I7stNWFpop7gDnb00tnr5RP9D9OdXkZT4dJYhxQXKjL6KE71RrafOmDRlTDnYnjx+9BaM/LySg2iiXqC29PUyVmurczq3sjWWZ9M6r7pgQLTybd0ZEdmOvngjV3+U3v7qX/RGYtq1EZM1CJyj4g0iciWaASkwmtPUwf/mvYoXRmT2FN5ZazDiSun5nfR5XOzozPC3R8ABVPhvd+yZVDX3R/57akJJZQW9X3AxK+BOQH5/IaKI2+yhJ1snXULfncEZ+MloFPyOkkVP2tac0ZeOBxOvwWqzoNnvwaH90Rnm2pCGHHGgzHmVRGZEflQVLjVtXRzm/yBltTJVFd+MNbhxJ0Mt2FRbjdrWnMwpjV81/RdM0yJ05kXQO078Ng/wSeeA7d2RamRha2PWkRuEZE1IrKmubk5XKtV45C9/28scVWzedYtOtIjiOUFHTT1pbGzPUo1TzIL4ORr7LjqF74bnW2qhBe2RG2MucsYs9wYs7y0VKfLxpwxXNJ8L7VMom76P8Y6mrgVmEK+qj49ehudsgSWfwL+/j+w5dHobVclLB31MUFNqXuWuf49PF14k470GEZhqo/Z2T08H81EDbDyRzD1DPjzZ6Fxc3S3rRKOJuqJqL+Pxe/+N9v9U2mYfkWso4l7pxV0sLkllYbuKH4cUtLgmvshowB+fz201UVv2yrhhDI87/fAm8A8EakVkZsjH5Yal7X3UdRbx0981zG9NDfW0cS95fm2++PZuii3qnMnwfW/h55WeOBD0H1k5OeopDRiojbGXG+MKTfGpBpjKo0xv4lGYGqMPO3wyo9Y61rEnvyzSE9JrgsDjEVlZh8L8r08UZMR/Y1PWQIfftheufzBq+z7p9QgyVmQeCIKDAnb8RR0H+I7vZ/ntPwDzKpZH9u4EsSHpnv4j0257OlwMys3ysWTZrwHrr4P/ngT3P8BuOFRyC6Obgwqrmkf9UTS1QzVL1FXuIJNZhaLcrtiHVHC+MDUXlwYntgfg1Y1wPzL4NoHoWk73LsS2mpjE4eKS5qoJ5KtT4ArhXtSriHb7WNuTk+sI0oYkzL9nF3m5fGajNiV4pi3Em58DDoa4e4LoWZ1jAJR8UYT9URxcBs0bcU/+xKebC5jcV4X7ghdEnCi+uB0D7XdbtYcjuFwxhln2xmLqVlw3+Ww5h4t4qQ0UU8Ifd2w9THILmN74YU0e9yc6oxkUKFZvfcIxd4G0l1+frXF/h0zkxbCLS/BzPPgqS/CHz+iI0KSnJ5MnAhe/gF0H4IzPsMLB7MQDEvytH96tDLchhUFHfz9SB43VUa4DMJw9UAC5l0GVefCC9+DA2/D5f8F8y8nfEVJVKLQRJ3oatfCm/8L086Ekjm8tCmdxYX95KfqZZ/G4qLSFl49ks9rR/I4P9bBiAvS8+Dsz8OGB+EPN0DZQjjpQ5Bdcvyyyz8emxhVVGjXRyLr74U/fwZyJsOCD3C4V9hwJIULy3tjHVnCmpPtoSrLw/NNBfHTNZxfCed8GRZeYcujvnI77Pwr+PpiHZmKEk3Uiexv34Hm7fAPP4fUTF5tTMMgXDBZP8BjJQIXl7ZwwJPB24fiqEaKy21LpJ7/dZh8Mux6Dl75ETRs0pONSUATdaLa8Qy89b+2GP3cSwB4oSGdknQfiwr7YxxcYju7qJ1st4/f7cmMdSgnyiyApR+FM/4ZXKmw9h548xdQvyHWkakI0kSdiFoPwBOfhvJT4OLvA+DxwYsNaVxc0YdLz14pkVcAABg6SURBVDWNS7rLcH5xG8/WpXOwJ04/IiVz4dyvwKKroLMR7jofHv80tNfHOjIVAXF6FKqg+rrsVGO/D666F1JsIaGXG9Pp9rm4vMIT4wAnhkvKWvAbuGdXHLaqA1xuO/38gm/CWbfBlj/BHcvg5dvtcaImDE3UicTvg0c/CQ0b4cq7oXjW0Yeerk2nKM3PilJvDAOcOCale7l8ai8PVmfS1hfnX1FSM+Hi78Fn3oY5F8PLP4Q7lsOG34PfH+voVBhook4UxsBz34Cdz8ClP4Z5lx59qMPjZVV9OpdWekjRdzRsPjW3m85+Fw9Ux3GreqCiKrjmt3ZmY+5keOJWuPsC2PdGrCNT46Qf60RgDKz6Fqz+FZz5WTj9n457+OlNDXh8wlXTtdsjnBYV9nPupF7u3ZWJJ5GGpU87Az75Anzobluo677L4A83wpHqWEemxkgTdbwzBp79mr2+3mmfhIu+d8Iif1pby6zcfpYU6WiPcFq99wjn5x/kUK+bH6/xx3Za+Wi5XLD4GvjsGtuHvftF+MXp8OJ/gFf/oScanZkYz7w98OTnYPMf4YzPwCX/ccL04Z2NHazZ38LXT/bozOIIWJjTzbzsbh5rKOG84rZYhxPccFPSs0vgvH+F7U/Cqz+2dWHe/zM7PV0lBE3U8aq9Hh7+MNSvtzUfimfD2vtOWOw3a3LJdGdwbZWWNI0EEbihsplv7ZzO001FnDc7QSeXZOTDqTdB5emw+RH47T/Y2wuvgLTsE5fXKelxRbs+4tHWJ+BX74FDu+C639sz+UM0l5s8Lp6oyeDK6T0UpCVoAkkA83J6OK2gg780FnG4N8G/tpTOs63r2e+DujW2oFft2zq7Mc5poo4nHQfhTzfDIx+FgmnwTy/aK38E8b/bs/AZuHmOtqYj7fqKZnr9Lu7YPkTrM9G402D++239kOxS2PAQrP4/6IxwxUA1Zpqo44G3B179T7hjKWz7sz35c/Mq2/oJoqbTxUPVmVwzw0NVtK/xl4QqMvq4sKSV3+3JZGvrBOkxzJsCZ33Ozm5srYFXfwS7VoFfT0rHmwlyxCUoT7u9gsdbv4TOgzDvcjtxYcBElqEYA99cn0uqy/D5hToDLVqur2hmQ0ceX1uby2MXtJA6EZo54rKzGyefbE8y7nwa6tfZ8gRTT491dMohJgJ9U8uXLzdr1qwJ+3onjINb7Vn69b+Dfo+t2zDnYnvCMAS/r87g6+vy+M6SDj46e+huj4QaSpZAWtLKufXNfG6d18XXTp6A/yQPboHNfwJPG5x2M1z4TcgsjHVUySLoCRBtUUfLkWrbrbHlMWjcBO50mLwIqs6z/dEh+u3mHr73bgmLcruY66pj9d4IxqxOsLKilw/P7OFXO7NZUtTPyooJVvt70iLbYGjZD2/fCVsfh/d+y44YcbljHV3S0hZ1pPR2wN7XYM8LsPsFaHEyasVyOPkqWHytTdyjsLo5lY+9lkdBqo/vz99HTorWcYi2FVVFeHxw/SuFbG1N4bfvaeXMsglYX2X5x22t679+FWr+bhP4e78VdASSCougL6wm6nDpbIYDq+HAW/b6dnXrwO+1V5OecQ7Mfq+tG10449hzQrluHtDnh1+/m8XPtmZTmtbHv809QEmanvCJhRVVRQAc6RWuebmQmi43Pz+9ncsqJ1jLOsAYaFhv6593H4LCKju0r2whnPaJWEc30WiiDotAYvV5ob3OnilvrYGWffYgBvv1MH8qFM2E0vlQOBPcY+thOtDl4unaDB6qzqSmy82lFR6uLKkhV1vScaGj38WPdk9lV1cm11X18JVFnRSnT9DxyH6fbYTsWgWeVsgtt/WwT74aMvJiHd1EoYl6zPx+OLwL6tbC+gehdb+dNWicIXHpeVAw3VYuK6yy17dzj/0STvXdLp6pTecvBzLY2GLXs7TIy20Lujh/ch9v79OThPGk3w+vdk3l17uySHcZLp/ay7UzPCwt9k7MCzj4fXZUSPVL9nOQmmXHZC/8AMx6L6RlxTrCRKaJOiTG2BZy4yabmOvW2ksc9bbbx1PSIX+aPflXMN3+ziwY92Ybe1z8apOPN4/ksrPLHuhVWR7OLGznzMIOytInYB/oBLKiqojd7W7ufjeLp2rT6ep3kZvSz8Kcbk7K7WZBbjeVGX2cObMo1qGGjzEw5RRY+1tbQ6SnBVIyYOoK29VXudwO8cuaQPsceeNL1CKyEvhvwA382hhz+3DLx32i9nmh7QAc2WtHYzRtt0PmDm6Fvg67jCvFnkCpWGYPuoplsO91O+50nIyBPR1unq9P57m69KMt52mZHs4s7ODMwnbKMzQ5J4pAvzVAV7/wfF0aT1TD1o4sDvXZ9zbb7eO00n6WF3tZWuxlSZGXrEQfcxWoB+Lrh/1vwLvP2hPoBzcfWya3/FijptD5nVtuh/wFfjLydUSJNfZELSJu4F3gIqAWeAe43hizLdhzwpqojXF+/AN+fMduez3g7baz+7zd9qfP+e1pha5DtiZvV7M94ddWA2219rkBqZmQO8UeQHlTnJ+KMXVh+A14/dBvhF4ftPa5aOkVarvdVHeksKPNzZrDaRzutQn/lEIvF0/pZbJpojJTrx4+kRgDzX2pbO/MZGdnFgf6cni33WZntxgWFvRzapGXqhwf03N8lKT7yU8z5KX6yUk1pEicD7AIVrip+4i9ClHjJmjacexcTvugz91AqVn2c5iaZVvmqZnHftzp9rPoTrUX9HWn2oaUO23A7cBjzv3HLTfgeQOXc6WOfh0udyTflHEl6jOBbxtjLnH+/jqAMeaHwZ4z5kT945n2Wm/HJeUwnDjLyLc1DbJKoGCqHXlRWGV/F1XBzmfH/eIvfLyEXr/gM8HXIxim5/hYVuzltBIv507qY0qW3T+doDLxragqoq1PWHc4lbWHU1lzOJXNLSl09Qf/lubCkOKCyyt7+dnp7VGMNgL8Ptt46u041pjydtnbvl77TdfX5/z22lFT/b32ecY36Lff+d1vzyMFGm8RJwNa/4H/pAN+55TCFzYP8/yRVh7kgRAS9VXASmPMJ52/bwJWGGM+O2i5W4BbnD/nATuDrLIEOBRa3HFN9yN+TIR9AN2PeBPt/ThkjFk51AOh9JINleVPyO7GmLuAu0ZcmcgaY8zyELYb13Q/4sdE2AfQ/Yg38bQfoZwZqwWmDvi7EqiPTDhKKaUGCyVRvwPMEZEqEUkDrgOejGxYSimlAkbs+jDG9IvIZ4HnsMPz7jHGbB3HNkfsHkkQuh/xYyLsA+h+xJu42Y+ITHhRSikVPhOh9LlSSk1omqiVUirORSxRi8hKEdkpIrtF5GtDPD5NRF4SkfUisklEgl/FNUZE5B4RaRKRLUEeFxH5H2cfN4nI0mjHGIoQ9uMGJ/5NIvJ3ETkl2jGOZKR9GLDcaSLic8b/x51Q9kNEzheRDSKyVUReiWZ8oQrhmMoXkb+IyEZnP4JMY4wdEZnq5KDtToyfH2KZ+PiMG2PC/oM96bgHmAmkARuBhYOWuQv4tHN7IbAvErGMcz/OBZYCW4I8fhnwV+xY8zOA1bGOeYz7cRZQ6Ny+NB73Y6R9cJZxAy8CzwBXxTrmMb4XBcA2YJrzd1msYx7jfnwD+JFzuxQ4AqTFOu5BMZYDS53budhSGYPzVFx8xiPVoj4d2G2MqTbG9AEPA1cMWsYAgUK2+cTh2GxjzKvYAyyYK4D7jfUWUCAi5dGJLnQj7Ycx5u/GmBbnz7ewY+XjSgjvBcBtwKNAU+QjGpsQ9uPDwGPGmBpn+bjclxD2wwC5IiJAjrNsXF3twhjTYIxZ59zuALYDFYMWi4vPeKQSdQVwYMDftZz4AnwbuFFEarEtoNsiFEskhbKfieZmbAsioYhIBfBB4FexjmWc5gKFIvKyiKwVkY/EOqAx+gWwANsA2wx83pioFOMYExGZAZwKrB70UFx8xiOVqEOZdn49cJ8xphL79eJ3ImGoIRpdIU2vTxQicgE2UX811rGMwc+BrxoTuKJDwkoBlgGXA5cA/y4ic2Mb0phcAmwApgBLgF+ISFxeCkZEcrDfxL5gjBlc+SouPuORqogbyrTzm4GVAMaYN0UkA1sEJS6/6gUxYabXi8hi4NfApcaYw7GOZwyWAw/bb9qUAJeJSL8x5onYhjVqtdjiPF1Al4i8CpyC7T9NJB8Hbje2o3e3iOwF5gNvxzas44lIKjZJP2iMeWyIReLiMx6pFmwo085rgPcCiMgCIANojlA8kfIk8BHnzPAZQJsxpiHWQY2WiEwDHgNuMsYkWkIAwBhTZYyZYYyZAfwJ+OcETNIAfwbOEZEUEckCVmD7ThPNwM/3JGxFzeqYRjSI03/+G2C7MeanQRaLi894RFrUJsi0cxH5LrDGGPMk8CXgbhH5IvarxMec/75xQ0R+D5wPlDh96f8PSAUwxvwK27d+GbAb6Ma2IuJOCPvxLaAY+KXTIu03cVI1LCCEfUgII+2HMWa7iDwLbAL82CsqDTskMRZCeD++B9wnIpux3QdfNcbEW+nTs4GbgM0issG57xvANIivz7hOIVdKqTiXaCfvlFIq6WiiVkqpOKeJWiml4pwmaqWUinOaqJVSKs5FPFGLSIGI/PM4nv8FZzzpeOP4mIj8YhzPf9aZphwWIvKNMT5vn4iUjHPb3xaRL49nHc56ykXkqVE+5754qmwnIjNGqsg3zvUPub9OhbxRvXYTWTiO60QgIqXO8MtRiUaLugAYc6IGvgCMO1GPh4hkAkXGmLowrnbIRO0MrE+Ubzr/Atwd6yCUGi8RidQs7eMYY5qBBhE5ezTPi0ZCuB2Y5dTX/QmAiHxFRN5x6rt+x7kvW0SedurXbhGRa0Xkc9haAS+JyEuDVywit4vINmc9/+nc9w8islpsneu/ObOiBj+vVEQedWJ4J/Ciich5TpwbnOfnOk85H3jZWeY0sTWbN4rI2yKSKyIZInKviGx2nneBs+zHROQxpzW+S0R+HIgbyHS286DTqtsuIr8E1gFTReR6Z31bRORHw73AIuJ2Wm5bnOd80bn/n5z92+js7wn/8ERklhPfWhF5TUTmO/df7axvo9hpzEO5EnjWWX6G8/x1zs9Zzv0iIr9w3qengbLh3r8h4rtZRN4VW6To7sC3IhGZLiIvOM99QezsyuHunyUibzmvx3dFpDPI6/iTAcfmp4Z73Ufhfc5r866IvH+I7R73Dcd53Wc4t290jrMNInKniLiH25CIfG7Aa/qwc1+22PrR7zjH5xXO/UMet0Os8zRnfW86r8+W4Z4/zP1ZIvJHZ11/EPs5PWFi1Wj3eSTDxPMxEXlERP4CPC8iOc4xs85ZNvA6BT6fd4utW/282MbbcK/NcMfSE8ANo9qJKNR8ncGAmrXAxdha1IL9R/EUtrbtlcDdA5bLd37vA0qGWG8RsJNjk3YKnN+FA+77JPBfzu2PAb9wbj8EvMe5PQ07hRTgL8DZzu0cIMW5/T/Ahdja2tXAac79edjZnV8C7nXum4+dPpvhbLMaW8Y1A9gPTHWW6xz0GvmBM5y/pzjrKHXW/yLwj8FeD2wRn1UD/g68FsUD7vs+cJtz+9vAl53bLwBznNsrgBed25uBioHrG7TNKmDtgL+zgAzn9hzsDFSADwGrsDNUpwCtwFXB3r9B25ji7G8RdtbbawPew78AH3VufwJ4YoT7nwKud27fGnj9GXB8ArcA33RupwNrgKoh4noNW3Bo8M/7hlj2Puw/M5fzutQ6x8L5wFOD3w/n7y1OXAuc/Ul17v8l8JERPm/1QPqg4+AHwI2B+7B1Q7IJctwOsc4twFnO7dsHvF7Bjvtg938ZuNO5fxG27Onygcd1qPsM/CzIe/C1IZYd7vNZi/22DPazlufcLsHORhTnvegHljiP/XHA6xnstQl6LGGr720ONYcaYyJWlGk4Fzs/652/c7AH8GvAf4ptPT5ljHlthPW0Ax7g105LLdDfVwn8QWzN2DRg7xDPfR+wUORoYaw8sa3nN4CfisiD2JrAtc7jZ2MPsnlAgzHmHQDjVNoSkfcAdzj37RCR/dhylQAvGGPanOW2AdM5vmxiwH5j690CnAa8bOzXJJx4zsX+Jx5KNTBTRO4Angaed+5fJCLfx344c7BT+o8SWzXsLOCRAa9FuvP7DewU4D9i64AMVs7xtVlSsRXSlgC+Aft/LvB7Y6va1YvIi879wd6/gU4HXjHGHHHifWTAes/E/hMA+B3w4xDu/0fn9kPAUC34i4HFcqxPOR97bB53DBljzhniucP5o7ElPneJSDU2WYTivdh/wu84708mIxct2wQ8KCJPcOx4uRj4wIBWewa2gRLsuN0UWJmIFAC5xpi/O3c9BAS+FQR7/nD3/7dz/xYRObqd0e6zMeaLI7wOAw33+VwVOL6wSfkHInIutuFUAQS+ke81xgSmma8FZozw2gx3LDVhGyEhi0WiFuCHxpg7T3hAZBl2Xv0PReR5Y8x3g63E2Hoip2Pf2OuAz2JbvXcAPzXGPCki52NbK4O5gDONMT2D7r/dSRqXAW+JyPuAPuCAMaZP7JEz1Jz7oUohBvQOuO0j+GveFeL6TmCMaRF7+axLgM8A12Bbk/dhW+IbReRj2FbcQC6g1RizZIh13ioiK7DlNjeIyBJzfFW9HuwHPuCLwEFspTcXNgkfXd0Q6z/h/RORi7AfArDFcNYPft4wgtVCGE2NBMF+63hu2IVEXsNeEWSwLxtj/hZCDIP/7uf4bsjA6yrAb40xXx8unkEux/5z/AC2ROpJznquNMbsHLigDPjvPOj+e7G1mesZ/it6sON0tPcPXmbEfRaRnwFDddU8bIy5fRTbHfi5uwH7LXaZMcYrIvs49l4M/hxnjrDe4Y6lDOznJ2TR6KPu4PiD+jngE05rDhGpEJEyEZkCdBtjHsC2dpYGeT7O83Kw3SPPYE84BpJNPhA46ffRIDE9j03sgXUtcX7PMsZsNsb8CPtVZT720lSBs7Q7gCkicpqzfK7YkxCv4hzQYmsHT8N+rR+OV2yJxaGsBs4TkRKnf+56IOi188SeLXcZYx4F/p1jr10u9sRFKkN84JxvBHtF5GpnPeIk/MBrsdoY8y3gEMeXegT79XnGgL/zsd82/NhCN4F+xVeB65w+u3KcD9dQ758xxmeMWeL8fAtbEvM8ESl0XucrB2zv79gEj7Nvr49w/1sDnh94fLDngE8H3hcRmSsi2UO8bucMiHPgz1BJGuBqEXGJyCzs5ekGHxv7cN4zsdfkq3LufwG4SkTKnMeKRGS6c/t+5x/dUWJPQk81xrwE/CvHf5O6LZCYReRU5ylDHrfGmI87+3OZsVf+6RBbOW7waxfsuA92/+vYRgQishA4eYjXKug+D2SM+WKQ92Bwkh4uzsHygSYnSV+A/fYb1AivzXDH0lxsl0nIIt6iNsYcFpE3nE72vxpjviK2rOmbznHTCdwIzAZ+IiJ+wAt82lnFXcBfRaTBGDPwP2gu8GexdawF26ID24J+RETqsB/OKk70OeB/na9egUR7K/AF5w3yYa9b91dsyczbnH3pE5FrgTvEnkzowXaj/BL4ldhKYf3YSoC9QRosAXcBm0RkHfBvg16zBhH5OvCSs2/PGGP+PMy6KoB75dhokUBr5N+xSX8/ts95qFbgDcD/icg3sd0XD2OvcfkTEZnjbP8F576BMXaJyB4RmW2M2e28Bo86Sf8ljrVUHsd+09mMTe6BfzjB3r+B26gTkR84+1CPfU/anIc/B9wjIl/BdsF8fIT7vwA8ICJfwnYPBdYz0K+x/3zWOUmtmWPdJeOxE7vfk4BbjTGeQcfGo9hSmhuwJYLfBTDGbHPel+ed99aL/ca0H1gMDC636Xb2MR/7mv7MGNMqIt/DXlhhk7Nf+7Bf0Yc8boeI/2Zspcsu7En1wGsX7Lgf7v7fOp+79dguluPehxH2eaxC/Xw+CPxFRNZg+7t3hLDuYK/NcMfSBdhjMGRaPW8YIpIOvGHirORnvBCRD2K/Jn4zgtvIMcZ0Oi3qx7Elcx8fw3qygB5jjBGR67AnFgdfxzMhiL1Sym+MMVdHaXs5xphO5/bXgHJjzAlX7A5hPW7sSUKP8+3iBWCusddVTUhjeW3EjqK6why7TumIYtFHnTCc1oUm6SCMMY+LSHGEN/NtsecKMrBdVmO9GMAy7MlOwY48+USY4os6p8sqKknacbnzDS8F27L92BjXk4UdapuKbfF/OpGTtGNUr42IlGLPoYWcpEFb1EopFfcSZQacUkolLU3USikV5zRRK6VUnNNErZRScU4TtVJKxbn/DyhNeNpqOIwcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sb.distplot(sales_ratio_boot_adsgoogle)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales (ads-google = blue, seo-google = orange)')\n",
    "\n",
    "ax1=sb.distplot(sales_ratio_boot_seogoogle)\n",
    "sb.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.172344\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.476243\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_adsgoogle, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.114174\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.702902\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_seogoogle, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sb.distplot(sales_ratio_boot_seobing)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales (seo-bing = blue, seo-yahoo = orange)')\n",
    "\n",
    "ax1=sb.distplot(sales_ratio_boot_seoyahoo)\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    0.295693\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.263644\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_seobing, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    0.491872\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.220375\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_seoyahoo, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if cities affect sales or sales ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Some rows do not have city info. Drop these rows.\n",
    "test_group2 = test_group.dropna()\n",
    "control_group2 = control_group.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01949365, 0.01605996, 1.24634604],\n",
       "       [0.01977951, 0.01531328, 1.17122378],\n",
       "       [0.01896082, 0.01440606, 1.14941098],\n",
       "       [0.02132334, 0.01510248, 1.07147093]])"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#probably better to make a mask and then use those value to calculate 4 different values each.\n",
    "#Going through loop takes too much time.\n",
    "region_conversion_rate = np.zeros((4,3))\n",
    "\n",
    "test_east = (test_group2['long'] >= -85)\n",
    "control_east = (control_group2['long'] >= -85)\n",
    "\n",
    "test_west = (test_group2['long']<= -110)\n",
    "control_west = (control_group2['long']<= -110)\n",
    "\n",
    "test_north = (test_group2['long']<-85) & (test_group2['long']> -110) &(test_group2['lat']>=36)\n",
    "control_north = (control_group2['long']<-85) & (control_group2['long']> -110) &(control_group2['lat']>=36)\n",
    "\n",
    "test_south = (test_group2['long']<-85) & (test_group2['long']> -110) &(test_group2['lat']<36)\n",
    "control_south = (control_group2['long']<-85) & (control_group2['long']> -110) &(control_group2['lat']<36)\n",
    "\n",
    "test_east_data = test_group2.loc[test_east]\n",
    "control_east_data = control_group2.loc[control_east]\n",
    "\n",
    "test_west_data = test_group2.loc[test_west]\n",
    "control_west_data = control_group2.loc[control_west]\n",
    "\n",
    "test_north_data = test_group2.loc[test_north]\n",
    "control_north_data = control_group2.loc[control_north]\n",
    "\n",
    "test_south_data = test_group2.loc[test_south]\n",
    "control_south_data = control_group2.loc[control_south]\n",
    "\n",
    "region_conversion_rate[0,0] = sum(control_east_data['converted'])/control_east_data.shape[0]\n",
    "region_conversion_rate[0,1] = sum(test_east_data['converted'])/test_east_data.shape[0]\n",
    "region_conversion_rate[0,2] = (59/39 * region_conversion_rate[0,1])/region_conversion_rate[0,0]\n",
    "    \n",
    "region_conversion_rate[1,0] = sum(control_west_data['converted'])/control_west_data.shape[0]\n",
    "region_conversion_rate[1,1] = sum(test_west_data['converted'])/test_west_data.shape[0]\n",
    "region_conversion_rate[1,2] = (59/39 * region_conversion_rate[1,1])/region_conversion_rate[1,0]\n",
    "\n",
    "\n",
    "region_conversion_rate[2,0] = sum(control_north_data['converted'])/control_north_data.shape[0]\n",
    "region_conversion_rate[2,1] = sum(test_north_data['converted'])/test_north_data.shape[0]\n",
    "region_conversion_rate[2,2] = (59/39 * region_conversion_rate[2,1])/region_conversion_rate[2,0]\n",
    "\n",
    "\n",
    "region_conversion_rate[3,0] = sum(control_south_data['converted'])/control_south_data.shape[0]\n",
    "region_conversion_rate[3,1] = sum(test_south_data['converted'])/test_south_data.shape[0]\n",
    "region_conversion_rate[3,2] = (59/39 * region_conversion_rate[3,1])/region_conversion_rate[3,0]\n",
    "\n",
    "\n",
    "\n",
    "region_conversion_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check if East and South is significnatly different or not.\n",
    "# bootstrap for customers in the East\n",
    "\n",
    "control_converted=control_east_data['converted']\n",
    "test_converted = test_east_data['converted']\n",
    "\n",
    "sales_ratio_boot_east = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_east[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bootstrap for customers in the South\n",
    "\n",
    "control_converted=control_south_data['converted']\n",
    "test_converted = test_south_data['converted']\n",
    "\n",
    "sales_ratio_boot_south = np.zeros((1000,1))\n",
    "\n",
    "for boot_sample in range (1000):\n",
    "    control_sample = control_converted.sample(frac=1, replace=True)\n",
    "    test_sample = test_converted.sample(frac=1, replace=True)\n",
    "\n",
    "    control_rate_boot=sum(control_sample)/control_sample.shape[0]\n",
    "    test_rate_boot=sum(test_sample)/test_sample.shape[0]\n",
    "    \n",
    "    sales_ratio_boot_south[boot_sample]=(59/39 * test_rate_boot)/control_rate_boot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1=sb.distplot(sales_ratio_boot_east)\n",
    "sb.despine()\n",
    "ax1.set_xlabel('test sales/control sales (east = blue, south = orange)')\n",
    "\n",
    "ax1=sb.distplot(sales_ratio_boot_south)\n",
    "sb.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    1.137808\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.373701\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_east, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio    0.918726\n",
      "Name: 25, dtype: float64\n",
      "ratio    1.238606\n",
      "Name: 975, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "sales_ratio_df = pd.DataFrame(sales_ratio_boot_south, columns = ['ratio'])\n",
    "sales_ratio_df.sort_values(by = ['ratio'],inplace = True)\n",
    "sales_ratio_df.reset_index(drop=True, inplace = True)\n",
    "print(sales_ratio_df.iloc[25])\n",
    "print(sales_ratio_df.iloc[975])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "\n",
    "- ### Overall, increasing the price to \\\\$59 decreased the conversion rate but significantly increased the sales. However, this effect depends on the type of customers.\n",
    "\n",
    "- ### Sales from \"mobile\" customers signficantly increased. Sales from \"web\" customers increased but the confidence interval was very close to 1 for sales ratio.\n",
    "\n",
    "- ### Sales increased significantly for \"iOS\" and \"mac\" users, but \"linux\" users did not buy higher priced software.\n",
    "\n",
    "- ### Users with \"friend referral\" has higher conversion rate.\n",
    "\n",
    "- ### Customers coming from \"ads-google\" and \"seo-google\" has higher sales during the test.\n",
    "\n",
    "- ### Customers coming from \"seo-bing\" and \"seo-yahoo\" has lower sales during the test, but need more data to determine it's significance.\n",
    "\n",
    "- ### Sales for customers in the \"east\" increased significantly for the test.\n",
    "\n",
    "- ### Sales for customers in the \"south\" did not significantly increase for the test.\n",
    "\n",
    "## Future directions\n",
    "\n",
    "- ### Check if the effect changes with time"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}