12_2023_Instacart_Basket_Analysis/03_Scripts/Step8_IC_Customer_profiling_questions.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "f7673b8d",
   "metadata": {},
   "source": [
    "# Project: 12-2023 Instacart Basket Analysis\n",
    "## Author: Nadia Ordonez\n",
    "## Step 8 IC Customer profiling questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcdb603",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [1. Introduction](#1.-Introduction)\n",
    "* [2. Customer profile questions](#2.-Customer-profile-questions)\n",
    "* [3. Importing data](#3.-Importing-data)\n",
    "    * [3.1 Importing libraries](#3.1-Importing-libraries)\n",
    "    * [3.2 Importing data](#3.2-Importing-data)\n",
    "* [4. Distribution of profiles](#4.-Distribution-of-profiles)\n",
    "* [5. Ordering habits](#5.-Ordering-habits)\n",
    "* [6. Regions and departments](#6.-Regions-and-departments)\n",
    "* [7. Recommendations](#7.-Recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cb1625",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e09786",
   "metadata": {},
   "source": [
    "* The marketing and business strategy units at Instacart want to create more-relevant marketing strategies for different products and are, thus, curious about customer profiling in their database. After discussions with the Instacart team, it was agreed to create customer profiles based on age, income, and number of dependents. Number of dependents refers to the number of children per user. In Step 7 IC Customer profiling the following variables were created:  \n",
    "    * \"age_profile\" with labels \"Young adult\", \"Adult\" and \"Retired\".\n",
    "    * \"dependents_profile\" with labels \"No kids\" and \"With kids\".\n",
    "    * \"income_profile\" with labels \"Low income\", \"Middle income\" and \"High income\". \n",
    "* The above classification was combined to generate 18 different customer profiles, ranging from \"Young adult, No kids, Low income\" to \"Retired, Kids, High income\" under the \"customer_profile\" variable. Here, visual will be generated to answer research questions requested by Instacart regarding customer profiles. \n",
    "* NOTE: After deliverations with stakeholders, it was also agreed to evaluate the frequency of customer profiles taking into account the volumen of orders per user, instead of single orders per user. In our current dataframe, a single user_id is repeated several times in the \"user_id\" variable depending on how many products were purchased within single orders. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c229958b",
   "metadata": {},
   "source": [
    "# 2. Customer profile questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cdeb5a6",
   "metadata": {},
   "source": [
    "What is the distribution of customer profiles?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ed359d",
   "metadata": {},
   "source": [
    "Is there a difference in ordering habits among customer profiles?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1538eb7f",
   "metadata": {},
   "source": [
    "Is there a link between customer profiles and regions or departments?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f08d26",
   "metadata": {},
   "source": [
    "# 3. Importing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806c6cb4",
   "metadata": {},
   "source": [
    "## 3.1 Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd860302",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import analytical libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f7e8b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100286fa",
   "metadata": {},
   "source": [
    "## 3.2 Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e6c249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Project folder path into a string to easily retrieve data\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73e4f76",
   "metadata": {},
   "source": [
    "### Customer profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18ba291b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import “orders_products_cust_step7.pkl”\n",
    "#See \"Step 7 IC Customer profiling\" to check for customer profiles details\n",
    "cust_profiling = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared data', 'orders_products_cust_step7.pkl'))"
   ]
  },
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   "execution_count": 5,
   "id": "555b8908",
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   "outputs": [
    {
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      "text/plain": [
       "(30992966, 19)"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#Check df size\n",
    "cust_profiling.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a427098",
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      "text/plain": [
       "   order_id  user_id  days_since_prior_order  department_id  prices  age  \\\n",
       "0   2539329        1                     NaN              7     9.0   31   \n",
       "1   2398795        1                    15.0              7     9.0   31   \n",
       "2    473747        1                    21.0              7     9.0   31   \n",
       "3   2254736        1                    29.0              7     9.0   31   \n",
       "4    431534        1                    28.0              7     9.0   31   \n",
       "\n",
       "   number_of_dependants  income region age_profile dependents_profile  \\\n",
       "0                     3   40423  South       Adult          With kids   \n",
       "1                     3   40423  South       Adult          With kids   \n",
       "2                     3   40423  South       Adult          With kids   \n",
       "3                     3   40423  South       Adult          With kids   \n",
       "4                     3   40423  South       Adult          With kids   \n",
       "\n",
       "  income_profile            customer_profile  mean_price_per_customer_profile  \\\n",
       "0     Low income  Adult_With kids_Low income                         7.116217   \n",
       "1     Low income  Adult_With kids_Low income                         7.116217   \n",
       "2     Low income  Adult_With kids_Low income                         7.116217   \n",
       "3     Low income  Adult_With kids_Low income                         7.116217   \n",
       "4     Low income  Adult_With kids_Low income                         7.116217   \n",
       "\n",
       "   max_price_per_customer_profile  min_price_per_customer_profile  \\\n",
       "0                            25.0                             1.0   \n",
       "1                            25.0                             1.0   \n",
       "2                            25.0                             1.0   \n",
       "3                            25.0                             1.0   \n",
       "4                            25.0                             1.0   \n",
       "\n",
       "   mean_usage_per_customer_profile  max_usage_per_customer_profile  \\\n",
       "0                        11.045855                            30.0   \n",
       "1                        11.045855                            30.0   \n",
       "2                        11.045855                            30.0   \n",
       "3                        11.045855                            30.0   \n",
       "4                        11.045855                            30.0   \n",
       "\n",
       "   min_usage_per_customer_profile  \n",
       "0                             0.0  \n",
       "1                             0.0  \n",
       "2                             0.0  \n",
       "3                             0.0  \n",
       "4                             0.0  "
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check headers\n",
    "cust_profiling.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e16b0e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                             int32\n",
       "user_id                              int32\n",
       "days_since_prior_order             float64\n",
       "department_id                         int8\n",
       "prices                             float64\n",
       "age                                  int32\n",
       "number_of_dependants                 int32\n",
       "income                               int64\n",
       "region                              object\n",
       "age_profile                         object\n",
       "dependents_profile                  object\n",
       "income_profile                      object\n",
       "customer_profile                    object\n",
       "mean_price_per_customer_profile    float64\n",
       "max_price_per_customer_profile     float64\n",
       "min_price_per_customer_profile     float64\n",
       "mean_usage_per_customer_profile    float64\n",
       "max_usage_per_customer_profile     float64\n",
       "min_usage_per_customer_profile     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#See headers\n",
    "cust_profiling.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44ebe25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a subset to avoid RAM issues\n",
    "#Selecting columns to avoid RAM memory issues\n",
    "variables = ['order_id', 'user_id', 'days_since_prior_order', 'department_id', 'prices', 'region', 'customer_profile']\n",
    "cust_profiling = cust_profiling.loc[:, variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d2a80e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 7)"
      ]
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check df size\n",
    "cust_profiling.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bff5c73f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>days_since_prior_order</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
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       "      <td>9.0</td>\n",
       "      <td>South</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
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      "text/plain": [
       "   order_id  user_id  days_since_prior_order  department_id  prices region  \\\n",
       "0   2539329        1                     NaN              7     9.0  South   \n",
       "1   2398795        1                    15.0              7     9.0  South   \n",
       "2    473747        1                    21.0              7     9.0  South   \n",
       "3   2254736        1                    29.0              7     9.0  South   \n",
       "4    431534        1                    28.0              7     9.0  South   \n",
       "\n",
       "             customer_profile  \n",
       "0  Adult_With kids_Low income  \n",
       "1  Adult_With kids_Low income  \n",
       "2  Adult_With kids_Low income  \n",
       "3  Adult_With kids_Low income  \n",
       "4  Adult_With kids_Low income  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check headers\n",
    "cust_profiling.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5752afe6",
   "metadata": {},
   "source": [
    "# 4. Distribution of profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21fd55b",
   "metadata": {},
   "source": [
    "What is the distribution of customer profiles? Which customer profile is the most predominant in our dataframe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "732ec746",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Frequency of customer profiles\n",
    "distribution = cust_profiling['customer_profile'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cbbd06ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "customer_profile\n",
       "Adult_With kids_Middle income          11328954\n",
       "Retired_With kids_Middle income         5442930\n",
       "Adult_No kids_Middle income             3812244\n",
       "Adult_With kids_Low income              2740559\n",
       "Young adult_With kids_Middle income     1980497\n",
       "Retired_No kids_Middle income           1796557\n",
       "Adult_No kids_Low income                 926872\n",
       "Young adult_With kids_Low income         926080\n",
       "Retired_With kids_Low income             694263\n",
       "Young adult_No kids_Middle income        643316\n",
       "Young adult_No kids_Low income           307579\n",
       "Retired_No kids_Low income               213220\n",
       "Adult_With kids_High income               83414\n",
       "Retired_With kids_High income             41653\n",
       "Adult_No kids_High income                 29951\n",
       "Retired_No kids_High income               14143\n",
       "Young adult_With kids_High income          7525\n",
       "Young adult_No kids_High income            3209\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "94caa0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency with a bar chart\n",
    "#Reverse the 'Blues' colormap\n",
    "colors = sns.color_palette(\"Blues\", len(distribution))\n",
    "colors = list(reversed(colors))\n",
    "\n",
    "ax = distribution.plot.bar(color=colors)\n",
    "\n",
    "#Adding main title\n",
    "plt.title(\"Customer Profile Distribution\")\n",
    "\n",
    "#Adding x-axis label\n",
    "plt.xlabel(\"Customer Profiles\")\n",
    "\n",
    "#Adding y-axis label\n",
    "plt.ylabel(\"Total counts\")\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6))))\n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_profile_distribution.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168fc47c",
   "metadata": {},
   "source": [
    "The top three most prevalent customer profiles in our database share a common characteristic—middle income. These profiles include adults, both with and without children, as well as retirees with kids. Notably, the \"Adult with kids and middle income\" profile stands out with an impressive frequency, exceeding 10 million occurrences."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2778c58",
   "metadata": {},
   "source": [
    "# 5. Ordering habits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab2c0db",
   "metadata": {},
   "source": [
    "Is there a difference in ordering habits among customer profiles?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f153c7",
   "metadata": {},
   "source": [
    "## Expenditure"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efdfdfc",
   "metadata": {},
   "source": [
    "Which customer profiles spent the most in our Instacart app?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bd6ac6e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_732ce thead {\n",
       "  display: none;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_732ce\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_732ce_level0_col0\" class=\"col_heading level0 col0\" >customer_profile</th>\n",
       "      <th id=\"T_732ce_level0_col1\" class=\"col_heading level0 col1\" >prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row0\" class=\"row_heading level0 row0\" >5</th>\n",
       "      <td id=\"T_732ce_row0_col0\" class=\"data row0 col0\" >Adult_With kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row0_col1\" class=\"data row0 col1\" >90148849.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row1\" class=\"row_heading level0 row1\" >11</th>\n",
       "      <td id=\"T_732ce_row1_col0\" class=\"data row1 col0\" >Retired_With kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row1_col1\" class=\"data row1 col1\" >43343749.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_732ce_row2_col0\" class=\"data row2 col0\" >Adult_No kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row2_col1\" class=\"data row2 col1\" >30336012.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_732ce_row3_col0\" class=\"data row3 col0\" >Adult_With kids_Low income</td>\n",
       "      <td id=\"T_732ce_row3_col1\" class=\"data row3 col1\" >19502405.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row4\" class=\"row_heading level0 row4\" >17</th>\n",
       "      <td id=\"T_732ce_row4_col0\" class=\"data row4 col0\" >Young adult_With kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row4_col1\" class=\"data row4 col1\" >15754190.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row5\" class=\"row_heading level0 row5\" >8</th>\n",
       "      <td id=\"T_732ce_row5_col0\" class=\"data row5 col0\" >Retired_No kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row5_col1\" class=\"data row5 col1\" >14301614.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row6\" class=\"row_heading level0 row6\" >16</th>\n",
       "      <td id=\"T_732ce_row6_col0\" class=\"data row6 col0\" >Young adult_With kids_Low income</td>\n",
       "      <td id=\"T_732ce_row6_col1\" class=\"data row6 col1\" >6898146.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row7\" class=\"row_heading level0 row7\" >1</th>\n",
       "      <td id=\"T_732ce_row7_col0\" class=\"data row7 col0\" >Adult_No kids_Low income</td>\n",
       "      <td id=\"T_732ce_row7_col1\" class=\"data row7 col1\" >6590383.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row8\" class=\"row_heading level0 row8\" >14</th>\n",
       "      <td id=\"T_732ce_row8_col0\" class=\"data row8 col0\" >Young adult_No kids_Middle income</td>\n",
       "      <td id=\"T_732ce_row8_col1\" class=\"data row8 col1\" >5116983.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_732ce_row9_col0\" class=\"data row9 col0\" >Retired_With kids_Low income</td>\n",
       "      <td id=\"T_732ce_row9_col1\" class=\"data row9 col1\" >4520494.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row10\" class=\"row_heading level0 row10\" >13</th>\n",
       "      <td id=\"T_732ce_row10_col0\" class=\"data row10 col0\" >Young adult_No kids_Low income</td>\n",
       "      <td id=\"T_732ce_row10_col1\" class=\"data row10 col1\" >2275630.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row11\" class=\"row_heading level0 row11\" >7</th>\n",
       "      <td id=\"T_732ce_row11_col0\" class=\"data row11 col0\" >Retired_No kids_Low income</td>\n",
       "      <td id=\"T_732ce_row11_col1\" class=\"data row11 col1\" >1388650.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row12\" class=\"row_heading level0 row12\" >3</th>\n",
       "      <td id=\"T_732ce_row12_col0\" class=\"data row12 col0\" >Adult_With kids_High income</td>\n",
       "      <td id=\"T_732ce_row12_col1\" class=\"data row12 col1\" >641528.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row13\" class=\"row_heading level0 row13\" >9</th>\n",
       "      <td id=\"T_732ce_row13_col0\" class=\"data row13 col0\" >Retired_With kids_High income</td>\n",
       "      <td id=\"T_732ce_row13_col1\" class=\"data row13 col1\" >320498.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row14\" class=\"row_heading level0 row14\" >0</th>\n",
       "      <td id=\"T_732ce_row14_col0\" class=\"data row14 col0\" >Adult_No kids_High income</td>\n",
       "      <td id=\"T_732ce_row14_col1\" class=\"data row14 col1\" >232846.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row15\" class=\"row_heading level0 row15\" >6</th>\n",
       "      <td id=\"T_732ce_row15_col0\" class=\"data row15 col0\" >Retired_No kids_High income</td>\n",
       "      <td id=\"T_732ce_row15_col1\" class=\"data row15 col1\" >108113.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row16\" class=\"row_heading level0 row16\" >15</th>\n",
       "      <td id=\"T_732ce_row16_col0\" class=\"data row16 col0\" >Young adult_With kids_High income</td>\n",
       "      <td id=\"T_732ce_row16_col1\" class=\"data row16 col1\" >57247.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_732ce_level0_row17\" class=\"row_heading level0 row17\" >12</th>\n",
       "      <td id=\"T_732ce_row17_col0\" class=\"data row17 col0\" >Young adult_No kids_High income</td>\n",
       "      <td id=\"T_732ce_row17_col1\" class=\"data row17 col1\" >25070.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1fdcba5ee90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total sum per customer profile\n",
    "total_prices_table = pd.pivot_table(cust_profiling, values='prices', index='customer_profile', aggfunc='sum').reset_index()\n",
    "\n",
    "#Sort in descending order\n",
    "total_prices_table = total_prices_table.sort_values(by='prices', ascending=False)\n",
    "\n",
    "#Display the sorted table without showing the index\n",
    "total_prices_table.style.set_table_styles([{'selector': 'thead', 'props': [('display', 'none')]}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ef9a4dee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the bar chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.barplot(x='customer_profile', y='prices', data=total_prices_table, palette='viridis', order=total_prices_table.sort_values('prices', ascending=False)['customer_profile'])\n",
    "plt.title(\"Total Sales by Customer Profile\")\n",
    "plt.xlabel(\"Customer Profiles\")\n",
    "plt.ylabel(\"Total Sales\")\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6))))\n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_profile_total_price.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d461da7e",
   "metadata": {},
   "source": [
    "The significance of our top three identified customer profiles extends beyond their frequency; they also contribute substantially to our sales figures. Collectively, these profiles represent a substantial share, contributing to sales exceeding 163 million."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc216f76",
   "metadata": {},
   "source": [
    "## Usage frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "974fec97",
   "metadata": {},
   "source": [
    "Which customer profiles use our Instacart app more frequently?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "571dda71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c327c thead {\n",
       "  display: none;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c327c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c327c_level0_col0\" class=\"col_heading level0 col0\" >customer_profile</th>\n",
       "      <th id=\"T_c327c_level0_col1\" class=\"col_heading level0 col1\" >days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_c327c_row0_col0\" class=\"data row0 col0\" >Adult_No kids_High income</td>\n",
       "      <td id=\"T_c327c_row0_col1\" class=\"data row0 col1\" >9.552416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
       "      <td id=\"T_c327c_row1_col0\" class=\"data row1 col0\" >Retired_No kids_High income</td>\n",
       "      <td id=\"T_c327c_row1_col1\" class=\"data row1 col1\" >9.633782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row2\" class=\"row_heading level0 row2\" >15</th>\n",
       "      <td id=\"T_c327c_row2_col0\" class=\"data row2 col0\" >Young adult_With kids_High income</td>\n",
       "      <td id=\"T_c327c_row2_col1\" class=\"data row2 col1\" >9.690810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row3\" class=\"row_heading level0 row3\" >9</th>\n",
       "      <td id=\"T_c327c_row3_col0\" class=\"data row3 col0\" >Retired_With kids_High income</td>\n",
       "      <td id=\"T_c327c_row3_col1\" class=\"data row3 col1\" >10.280375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
       "      <td id=\"T_c327c_row4_col0\" class=\"data row4 col0\" >Adult_With kids_High income</td>\n",
       "      <td id=\"T_c327c_row4_col1\" class=\"data row4 col1\" >10.363265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row5\" class=\"row_heading level0 row5\" >17</th>\n",
       "      <td id=\"T_c327c_row5_col0\" class=\"data row5 col0\" >Young adult_With kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row5_col1\" class=\"data row5 col1\" >10.649799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row6\" class=\"row_heading level0 row6\" >8</th>\n",
       "      <td id=\"T_c327c_row6_col0\" class=\"data row6 col0\" >Retired_No kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row6_col1\" class=\"data row6 col1\" >10.703718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row7\" class=\"row_heading level0 row7\" >2</th>\n",
       "      <td id=\"T_c327c_row7_col0\" class=\"data row7 col0\" >Adult_No kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row7_col1\" class=\"data row7 col1\" >10.717566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row8\" class=\"row_heading level0 row8\" >11</th>\n",
       "      <td id=\"T_c327c_row8_col0\" class=\"data row8 col0\" >Retired_With kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row8_col1\" class=\"data row8 col1\" >10.768141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row9\" class=\"row_heading level0 row9\" >5</th>\n",
       "      <td id=\"T_c327c_row9_col0\" class=\"data row9 col0\" >Adult_With kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row9_col1\" class=\"data row9 col1\" >10.791625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row10\" class=\"row_heading level0 row10\" >14</th>\n",
       "      <td id=\"T_c327c_row10_col0\" class=\"data row10 col0\" >Young adult_No kids_Middle income</td>\n",
       "      <td id=\"T_c327c_row10_col1\" class=\"data row10 col1\" >10.867579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row11\" class=\"row_heading level0 row11\" >16</th>\n",
       "      <td id=\"T_c327c_row11_col0\" class=\"data row11 col0\" >Young adult_With kids_Low income</td>\n",
       "      <td id=\"T_c327c_row11_col1\" class=\"data row11 col1\" >10.963435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row12\" class=\"row_heading level0 row12\" >1</th>\n",
       "      <td id=\"T_c327c_row12_col0\" class=\"data row12 col0\" >Adult_No kids_Low income</td>\n",
       "      <td id=\"T_c327c_row12_col1\" class=\"data row12 col1\" >11.016815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row13\" class=\"row_heading level0 row13\" >4</th>\n",
       "      <td id=\"T_c327c_row13_col0\" class=\"data row13 col0\" >Adult_With kids_Low income</td>\n",
       "      <td id=\"T_c327c_row13_col1\" class=\"data row13 col1\" >11.045855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row14\" class=\"row_heading level0 row14\" >10</th>\n",
       "      <td id=\"T_c327c_row14_col0\" class=\"data row14 col0\" >Retired_With kids_Low income</td>\n",
       "      <td id=\"T_c327c_row14_col1\" class=\"data row14 col1\" >11.080151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row15\" class=\"row_heading level0 row15\" >13</th>\n",
       "      <td id=\"T_c327c_row15_col0\" class=\"data row15 col0\" >Young adult_No kids_Low income</td>\n",
       "      <td id=\"T_c327c_row15_col1\" class=\"data row15 col1\" >11.137039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row16\" class=\"row_heading level0 row16\" >7</th>\n",
       "      <td id=\"T_c327c_row16_col0\" class=\"data row16 col0\" >Retired_No kids_Low income</td>\n",
       "      <td id=\"T_c327c_row16_col1\" class=\"data row16 col1\" >11.396369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c327c_level0_row17\" class=\"row_heading level0 row17\" >12</th>\n",
       "      <td id=\"T_c327c_row17_col0\" class=\"data row17 col0\" >Young adult_No kids_High income</td>\n",
       "      <td id=\"T_c327c_row17_col1\" class=\"data row17 col1\" >12.125121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1fdbb376110>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average \"days_since_prior_order\" per customer profile \n",
    "usage_table = pd.pivot_table(cust_profiling, values='days_since_prior_order', index='customer_profile', aggfunc='mean').reset_index()\n",
    "\n",
    "#Sort in descending order\n",
    "usage_table = usage_table.sort_values(by='days_since_prior_order')\n",
    "\n",
    "#Display the sorted table without showing the index\n",
    "usage_table.style.set_table_styles([{'selector': 'thead', 'props': [('display', 'none')]}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "224c907f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the bar chart\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.barplot(x='customer_profile', y='days_since_prior_order', data=usage_table, palette='viridis', order=usage_table.sort_values('days_since_prior_order')['customer_profile'])\n",
    "plt.title(\"Average app Usage Frequency by Customer Profile\")\n",
    "plt.xlabel(\"Customer Profile\")\n",
    "plt.ylabel(\"Average days since prior order\")\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_profile_app_usage_frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65af7728",
   "metadata": {},
   "source": [
    "Regarding usage frequency averages, an interesting trend emerges. Customer profiles with higher incomes exhibit a more frequent ordering pattern, with an average order placement interval of approximately 9 days. In contrast, our top three previously identified customer profiles, distinguished by both frequency and sales, tend to place orders at a slightly lower frequency, averaging around 11 days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ababb32",
   "metadata": {},
   "source": [
    "# 6. Regions and departments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3af31f75",
   "metadata": {},
   "source": [
    "Is there a link between customer profiles and regions or departments?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1eceb13",
   "metadata": {},
   "source": [
    "## Regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44bbdf8d",
   "metadata": {},
   "source": [
    "Which customer profiles are more popular in each our our regions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12a7c8f4",
   "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>region</th>\n",
       "      <th>customer_profile</th>\n",
       "      <th>profile_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>2874571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>1376075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>997361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "      <td>689584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Young adult_With kids_Middle income</td>\n",
       "      <td>502812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>West</td>\n",
       "      <td>Retired_With kids_High income</td>\n",
       "      <td>9182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>West</td>\n",
       "      <td>Adult_No kids_High income</td>\n",
       "      <td>5903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>West</td>\n",
       "      <td>Retired_No kids_High income</td>\n",
       "      <td>2462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>West</td>\n",
       "      <td>Young adult_With kids_High income</td>\n",
       "      <td>2195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>West</td>\n",
       "      <td>Young adult_No kids_High income</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     region                     customer_profile  profile_count\n",
       "5   Midwest        Adult_With kids_Middle income        2874571\n",
       "11  Midwest      Retired_With kids_Middle income        1376075\n",
       "2   Midwest          Adult_No kids_Middle income         997361\n",
       "4   Midwest           Adult_With kids_Low income         689584\n",
       "17  Midwest  Young adult_With kids_Middle income         502812\n",
       "..      ...                                  ...            ...\n",
       "63     West        Retired_With kids_High income           9182\n",
       "54     West            Adult_No kids_High income           5903\n",
       "60     West          Retired_No kids_High income           2462\n",
       "69     West    Young adult_With kids_High income           2195\n",
       "66     West      Young adult_No kids_High income            134\n",
       "\n",
       "[72 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group by 'region' and 'customer_profile' and count occurrences\n",
    "region_profile_counts = cust_profiling.groupby(['region', 'customer_profile']).size().reset_index(name='profile_count')\n",
    "\n",
    "#Sort profiles within each region by count in descending order\n",
    "sorted_profiles = region_profile_counts.sort_values(by=['region', 'profile_count'], ascending=[True, False])\n",
    "\n",
    "#Print or display the result\n",
    "sorted_profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fcce678b",
   "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>region</th>\n",
       "      <th>customer_profile</th>\n",
       "      <th>profile_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>2874571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>1376075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>997361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Northeast</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>2002555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Northeast</td>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>977659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Northeast</td>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>637148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>South</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>3531677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>South</td>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>1695436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>South</td>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>1183975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>West</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>2920151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>West</td>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>1393760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>West</td>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>993760</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region                 customer_profile  profile_count\n",
       "5     Midwest    Adult_With kids_Middle income        2874571\n",
       "11    Midwest  Retired_With kids_Middle income        1376075\n",
       "2     Midwest      Adult_No kids_Middle income         997361\n",
       "23  Northeast    Adult_With kids_Middle income        2002555\n",
       "29  Northeast  Retired_With kids_Middle income         977659\n",
       "20  Northeast      Adult_No kids_Middle income         637148\n",
       "41      South    Adult_With kids_Middle income        3531677\n",
       "47      South  Retired_With kids_Middle income        1695436\n",
       "38      South      Adult_No kids_Middle income        1183975\n",
       "59       West    Adult_With kids_Middle income        2920151\n",
       "65       West  Retired_With kids_Middle income        1393760\n",
       "56       West      Adult_No kids_Middle income         993760"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Filter the top 3 profiles per region\n",
    "top_profiles_per_region = sorted_profiles.groupby('region').head(3)\n",
    "top_profiles_per_region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "67ec7923",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Set up the plot\n",
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "\n",
    "#Initialize variables to store tick positions and labels\n",
    "tick_positions = []\n",
    "tick_labels = []\n",
    "\n",
    "#Iterate through unique regions\n",
    "for i, region in enumerate(sorted_profiles['region'].unique()):\n",
    "    #Select data for the current region\n",
    "    region_data = sorted_profiles[sorted_profiles['region'] == region].head(3)\n",
    "\n",
    "    #Plot the data for the current region\n",
    "    plt.bar(np.arange(len(region_data)) + i * 4, region_data['profile_count'], label=region)\n",
    "\n",
    "    #Append tick positions and labels\n",
    "    tick_positions.extend(np.arange(len(region_data)) + i * 4)\n",
    "    tick_labels.extend(region_data['customer_profile'].tolist())\n",
    "\n",
    "#Customize the plot\n",
    "plt.title(\"Top 3 Customer Profiles per US Region\")\n",
    "plt.xlabel(\"Customer Profiles\")\n",
    "plt.ylabel(\"Total counts\")\n",
    "plt.legend(title='US Region')\n",
    "plt.xticks(tick_positions, tick_labels, rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "\n",
    "# Changing y-axis to display values in thousands\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}K\".format(int(x/1e3)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_profiles_regions.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "576c273c",
   "metadata": {},
   "source": [
    "Here, our previously identified top three customer profiles, based on both frequency and sales, continue to stand out as the most common across all four US regions. The graph further reveals that the Southern states, followed by the Western and Midwestern states, exhibit a significant concentration of customer profiles."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdce710",
   "metadata": {},
   "source": [
    "## Departments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f40e275",
   "metadata": {},
   "source": [
    "What are the preferred grocery departments where our top 3 customer profiles spent most of their money?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38541299",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a subset to avoid RAM issues\n",
    "#Selecting columns to avoid RAM memory issues\n",
    "variables = ['order_id', 'user_id', 'department_id', 'prices', 'customer_profile']\n",
    "cust_departments = cust_profiling.loc[:, variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69442ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 5)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "cust_departments.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40c54823",
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>customer_profile</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  department_id  prices            customer_profile\n",
       "0   2539329        1              7     9.0  Adult_With kids_Low income\n",
       "1   2398795        1              7     9.0  Adult_With kids_Low income\n",
       "2    473747        1              7     9.0  Adult_With kids_Low income\n",
       "3   2254736        1              7     9.0  Adult_With kids_Low income\n",
       "4    431534        1              7     9.0  Adult_With kids_Low income"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "cust_departments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7a83f9bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id              int32\n",
       "user_id               int32\n",
       "department_id          int8\n",
       "prices              float64\n",
       "customer_profile     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "cust_departments.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4f5cb3",
   "metadata": {},
   "source": [
    "### Adding department names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9c8bdce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a variable containing the department names\n",
    "#Creating a dictionary\n",
    "#NOTE: Dictionary provided by Instacart team\n",
    "department_dict = {\n",
    "    '1': {'department': 'frozen'},\n",
    "    '2': {'department': 'other'},\n",
    "    '3': {'department': 'bakery'},\n",
    "    '4': {'department': 'produce'},\n",
    "    '5': {'department': 'alcohol'},\n",
    "    '6': {'department': 'international'},\n",
    "    '7': {'department': 'beverages'},\n",
    "    '8': {'department': 'pets'},\n",
    "    '9': {'department': 'dry goods pasta'},\n",
    "    '10': {'department': 'bulk'},\n",
    "    '11': {'department': 'personal care'},\n",
    "    '12': {'department': 'meat seafood'},\n",
    "    '13': {'department': 'pantry'},\n",
    "    '14': {'department': 'breakfast'},\n",
    "    '15': {'department': 'canned goods'},\n",
    "    '16': {'department': 'dairy eggs'},\n",
    "    '17': {'department': 'household'},\n",
    "    '18': {'department': 'babies'},\n",
    "    '19': {'department': 'snacks'},\n",
    "    '20': {'department': 'deli'},\n",
    "    '21': {'department': 'missing'}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "acae38af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'department': 'frozen'},\n",
       " '2': {'department': 'other'},\n",
       " '3': {'department': 'bakery'},\n",
       " '4': {'department': 'produce'},\n",
       " '5': {'department': 'alcohol'},\n",
       " '6': {'department': 'international'},\n",
       " '7': {'department': 'beverages'},\n",
       " '8': {'department': 'pets'},\n",
       " '9': {'department': 'dry goods pasta'},\n",
       " '10': {'department': 'bulk'},\n",
       " '11': {'department': 'personal care'},\n",
       " '12': {'department': 'meat seafood'},\n",
       " '13': {'department': 'pantry'},\n",
       " '14': {'department': 'breakfast'},\n",
       " '15': {'department': 'canned goods'},\n",
       " '16': {'department': 'dairy eggs'},\n",
       " '17': {'department': 'household'},\n",
       " '18': {'department': 'babies'},\n",
       " '19': {'department': 'snacks'},\n",
       " '20': {'department': 'deli'},\n",
       " '21': {'department': 'missing'}}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See dictionary\n",
    "department_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "10699b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert 'department_id' to string to match the keys in the dictionary\n",
    "cust_departments['department_id'] = cust_departments['department_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f10186a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Map 'department_id' to 'department_name' using the dictionary\n",
    "cust_departments['department_name'] = cust_departments['department_id'].map(lambda x: department_dict.get(x, {}).get('department', 'unknown'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "33f938ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>customer_profile</th>\n",
       "      <th>department_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
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       "      <td>Adult_With kids_Low income</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Low income</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id department_id  prices            customer_profile  \\\n",
       "0   2539329        1             7     9.0  Adult_With kids_Low income   \n",
       "1   2398795        1             7     9.0  Adult_With kids_Low income   \n",
       "2    473747        1             7     9.0  Adult_With kids_Low income   \n",
       "3   2254736        1             7     9.0  Adult_With kids_Low income   \n",
       "4    431534        1             7     9.0  Adult_With kids_Low income   \n",
       "\n",
       "  department_name  \n",
       "0       beverages  \n",
       "1       beverages  \n",
       "2       beverages  \n",
       "3       beverages  \n",
       "4       beverages  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "cust_departments.head()\n",
    "#\"department_name\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93342b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department_name\n",
       "produce            9079276\n",
       "dairy eggs         5191364\n",
       "snacks             2766406\n",
       "beverages          2573822\n",
       "frozen             2123342\n",
       "pantry             1782903\n",
       "bakery             1125026\n",
       "canned goods       1012074\n",
       "deli               1003834\n",
       "dry goods pasta     822136\n",
       "household           699858\n",
       "breakfast           677073\n",
       "meat seafood        674785\n",
       "personal care       424362\n",
       "babies              410392\n",
       "international       255991\n",
       "alcohol             144627\n",
       "pets                 93065\n",
       "missing              64768\n",
       "other                34411\n",
       "bulk                 33451\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting values within new column\n",
    "cust_departments['department_name'].value_counts(dropna = False)\n",
    "#The top 3 favourite grocery departments are produce, dairy eggs, and snacks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c636f3d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">department_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bakery</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beverages</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breakfast</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bulk</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canned goods</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy eggs</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deli</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dry goods pasta</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frozen</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>international</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meat seafood</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pantry</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal care</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pets</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>produce</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snacks</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                department_id    \n",
       "                          min max\n",
       "department_name                  \n",
       "alcohol                     5   5\n",
       "babies                     18  18\n",
       "bakery                      3   3\n",
       "beverages                   7   7\n",
       "breakfast                  14  14\n",
       "bulk                       10  10\n",
       "canned goods               15  15\n",
       "dairy eggs                 16  16\n",
       "deli                       20  20\n",
       "dry goods pasta             9   9\n",
       "frozen                      1   1\n",
       "household                  17  17\n",
       "international               6   6\n",
       "meat seafood               12  12\n",
       "missing                    21  21\n",
       "other                       2   2\n",
       "pantry                     13  13\n",
       "personal care              11  11\n",
       "pets                        8   8\n",
       "produce                     4   4\n",
       "snacks                     19  19"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "cust_departments.groupby('department_name').agg({'department_id' : ['min', 'max']})\n",
    "#nothing odd on the results\n",
    "#department_name was correctly assigned to the df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf360033",
   "metadata": {},
   "source": [
    "### Top customer profiles and grocery departments "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e09e6e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Adult_With kids_Low income', 'Retired_No kids_Low income',\n",
       "       'Adult_With kids_Middle income', 'Retired_No kids_Middle income',\n",
       "       'Retired_With kids_Middle income', 'Retired_With kids_Low income',\n",
       "       'Young adult_With kids_Low income', 'Adult_No kids_Middle income',\n",
       "       'Young adult_No kids_Low income', 'Adult_No kids_Low income',\n",
       "       'Young adult_With kids_Middle income',\n",
       "       'Young adult_No kids_Middle income', 'Adult_With kids_High income',\n",
       "       'Retired_With kids_High income',\n",
       "       'Young adult_With kids_High income', 'Adult_No kids_High income',\n",
       "       'Young adult_No kids_High income', 'Retired_No kids_High income'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See unique values within a variable\n",
    "cust_departments['customer_profile'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a36a2bb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter df to select the top 3 customer profiles see sections 4. Distribution of profiles and 5. Ordering habits  \n",
    "selected_profiles = ['Adult_With kids_Middle income', 'Retired_With kids_Middle income', 'Adult_No kids_Middle income']\n",
    "filtered_df = cust_departments[cust_departments['customer_profile'].isin(selected_profiles)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fd7908b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>customer_profile</th>\n",
       "      <th>department_name</th>\n",
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       "      <th>131</th>\n",
       "      <td>2293453</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
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       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>1973799</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
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       "      <th>133</th>\n",
       "      <td>532817</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2474845</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>86918</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>8.8</td>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_id  user_id department_id  prices               customer_profile  \\\n",
       "131   2293453       19             7     9.0  Adult_With kids_Middle income   \n",
       "132   1973799       19             7     9.0  Adult_With kids_Middle income   \n",
       "133    532817       19             7     9.0  Adult_With kids_Middle income   \n",
       "134   2474845       19            19     1.6  Adult_With kids_Middle income   \n",
       "135     86918       19             4     8.8  Adult_With kids_Middle income   \n",
       "\n",
       "    department_name  \n",
       "131       beverages  \n",
       "132       beverages  \n",
       "133       beverages  \n",
       "134          snacks  \n",
       "135         produce  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72658dd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20584128, 6)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "filtered_df.shape\n",
    "#Nothing odd in the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89053fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Group by customer_profile and department_name, then calculate the sum of prices\n",
    "grouped_df = filtered_df.groupby(['customer_profile', 'department_name'])['prices'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d93989b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>customer_profile</th>\n",
       "      <th>department_name</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>alcohol</td>\n",
       "      <td>149206.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>babies</td>\n",
       "      <td>423959.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>bakery</td>\n",
       "      <td>1134957.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>2366495.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>breakfast</td>\n",
       "      <td>669629.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>2587036.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>personal care</td>\n",
       "      <td>585474.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>pets</td>\n",
       "      <td>132705.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>13195108.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>snacks</td>\n",
       "      <td>1823823.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>63 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   customer_profile department_name      prices\n",
       "0       Adult_No kids_Middle income         alcohol    149206.1\n",
       "1       Adult_No kids_Middle income          babies    423959.7\n",
       "2       Adult_No kids_Middle income          bakery   1134957.1\n",
       "3       Adult_No kids_Middle income       beverages   2366495.6\n",
       "4       Adult_No kids_Middle income       breakfast    669629.9\n",
       "..                              ...             ...         ...\n",
       "58  Retired_With kids_Middle income          pantry   2587036.5\n",
       "59  Retired_With kids_Middle income   personal care    585474.6\n",
       "60  Retired_With kids_Middle income            pets    132705.9\n",
       "61  Retired_With kids_Middle income         produce  13195108.6\n",
       "62  Retired_With kids_Middle income          snacks   1823823.1\n",
       "\n",
       "[63 rows x 3 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "grouped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0f5fc42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_profile</th>\n",
       "      <th>department_name</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>9269569.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>5524601.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>2366495.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>2057379.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>1798690.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>27443681.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>16337015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>7025713.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>6148734.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>5389653.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>13195108.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>7857715.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>3380170.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>2952811.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>2587036.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   customer_profile department_name      prices\n",
       "19      Adult_No kids_Middle income         produce   9269569.7\n",
       "7       Adult_No kids_Middle income      dairy eggs   5524601.7\n",
       "3       Adult_No kids_Middle income       beverages   2366495.6\n",
       "10      Adult_No kids_Middle income          frozen   2057379.7\n",
       "16      Adult_No kids_Middle income          pantry   1798690.0\n",
       "40    Adult_With kids_Middle income         produce  27443681.6\n",
       "28    Adult_With kids_Middle income      dairy eggs  16337015.0\n",
       "24    Adult_With kids_Middle income       beverages   7025713.7\n",
       "31    Adult_With kids_Middle income          frozen   6148734.3\n",
       "37    Adult_With kids_Middle income          pantry   5389653.1\n",
       "61  Retired_With kids_Middle income         produce  13195108.6\n",
       "49  Retired_With kids_Middle income      dairy eggs   7857715.7\n",
       "45  Retired_With kids_Middle income       beverages   3380170.3\n",
       "52  Retired_With kids_Middle income          frozen   2952811.1\n",
       "58  Retired_With kids_Middle income          pantry   2587036.5"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Select the top 5 departments per customer profile based on prices\n",
    "top_5_per_profile = grouped_df.groupby('customer_profile', group_keys=False).apply(lambda x: x.nlargest(5, 'prices'))\n",
    "top_5_per_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c84ef860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>customer_profile</th>\n",
       "      <th>department_name</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>27443681.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>16337015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>7025713.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>6148734.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Adult_With kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>5389653.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>13195108.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>7857715.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>3380170.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>2952811.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Retired_With kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>2587036.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>produce</td>\n",
       "      <td>9269569.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>dairy eggs</td>\n",
       "      <td>5524601.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>beverages</td>\n",
       "      <td>2366495.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>frozen</td>\n",
       "      <td>2057379.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Adult_No kids_Middle income</td>\n",
       "      <td>pantry</td>\n",
       "      <td>1798690.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   customer_profile department_name      prices\n",
       "40    Adult_With kids_Middle income         produce  27443681.6\n",
       "28    Adult_With kids_Middle income      dairy eggs  16337015.0\n",
       "24    Adult_With kids_Middle income       beverages   7025713.7\n",
       "31    Adult_With kids_Middle income          frozen   6148734.3\n",
       "37    Adult_With kids_Middle income          pantry   5389653.1\n",
       "61  Retired_With kids_Middle income         produce  13195108.6\n",
       "49  Retired_With kids_Middle income      dairy eggs   7857715.7\n",
       "45  Retired_With kids_Middle income       beverages   3380170.3\n",
       "52  Retired_With kids_Middle income          frozen   2952811.1\n",
       "58  Retired_With kids_Middle income          pantry   2587036.5\n",
       "19      Adult_No kids_Middle income         produce   9269569.7\n",
       "7       Adult_No kids_Middle income      dairy eggs   5524601.7\n",
       "3       Adult_No kids_Middle income       beverages   2366495.6\n",
       "10      Adult_No kids_Middle income          frozen   2057379.7\n",
       "16      Adult_No kids_Middle income          pantry   1798690.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reordering customer profiles\n",
    "desired_order = ['Adult_With kids_Middle income', 'Retired_With kids_Middle income', 'Adult_No kids_Middle income']\n",
    "\n",
    "top_5_per_profile['customer_profile'] = pd.Categorical(top_5_per_profile['customer_profile'], categories=desired_order, ordered=True)\n",
    "top_5_per_profile = top_5_per_profile.sort_values('customer_profile')\n",
    "\n",
    "#See results\n",
    "top_5_per_profile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aea9ad77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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btq3Wrl2b5bKL7D6HctOjRw95enrKz89Pt99+u9LS0jRnzhzVqlUr1/lm3ET1ws/b7t27y9/fP8s6uBinT5/W0qVLddddd8nPzy/L5/Dp06e1evXqS14OgGsbTTyA/4zDhw8rJCQkS5NasmRJeXh4ZLlRXEhISJZ5ZMTyuqlcdnr37i1Jef4Dq2zZsjp48GCW62uHDRumtWvXau3atTleK39hPCPP7MaXKlXKev3gwYOSpDJlyuSY119//SVJeuKJJ5y+TPD09NTAgQMlSYcOHco1H0ny9vbWQw89pE8++UTHjh3TwYMHNXv2bD3wwAPy9vbOcfkZ9eS2XS7MtX79+llyjY2NzZKnh4eHihUrlu08M9bR3r17ddttt+nPP//UW2+9pR9++EFr1661vow4depUnrW7omjRok7Pvby8co2fPn3aqtkYo+Dg4Cw1r169OkvNF9Yrnd82F9aRHYfDoaVLl6pNmzZ6/fXXdeONN6pEiRJ65JFH9M8//1zSdCdOnNBtt92mH3/8US+99JLi4+O1du1azZ07V1LW9ZyZq9v9YvPP4Mr+kt/t4er+UrZsWUlSYmKiS+PzIzY2Vn379tVHH32khg0bqmjRourTp48OHDiQ57T5/Xy9lP1ckrp165Zlvb722msyxujIkSNO88jve/G1117T2rVr9dNPP2nv3r3atWuXOnfunGVcdp+3Hh4eKlGihFPc4XAoJCTkov6/caHDhw8rNTVVb7/9dpb627dvLynr5zCA6w93pwfwn1GsWDH9+OOPMsY4/UPz77//VmpqqooXL+40Prt/uGbEsmuA8mKMkSS5ueX+/WmrVq20ZMkSLVq0yOmoVlhYmMLCwiT9+w/bC134D+iMPJOSkrI06Pv377dqzvhHZ25nGGSMHT58uLp06ZLtmCpVquSaT4YBAwbo1Vdf1bRp03T69Gmlpqbq4YcfznHZmevJbbtcmOucOXMUHh6e53xTU1N1+PBhp+164baeP3++UlJSNHfuXKd55nQjtKt9F/7ixYvL4XDohx9+yPbLkLy+IMmv8PBwTZ06VZK0Y8cOzZ49W6NGjdLZs2f13nvvXfR0y5Yt0/79+xUfH28dfZfkdGOwnORnu19s/pJr+0t+t4er+0toaKhq1qypJUuWuHRHdB8fH0nKcnO57BrK4sWLa+LEiZo4caL27t2rBQsW6JlnntHff/+txYsX57qc/H6+XqyM+bz99ts5/vrHhT/lmd/3Yvny5VWvXr08x2X3eZuamqqDBw86NfLGGB04cMC6UeSlKFKkiNzd3XXfffdp0KBB2Y4pV67cJS8HwLWNI/EA/jNatGihEydOaP78+U7xGTNmWK9ntnTpUuuojySlpaUpNjZWFSpUyPWIdU4ylpPXz8498MADCg4O1lNPPaWkpKR8LyezjNNz//e//znF165dq23btlk1N2rUSIGBgXrvvfesLxsuVKVKFVWqVEmbNm1SvXr1sn0UKlTIpbxCQ0PVvXt3TZ48We+99546depkHWHMTbNmzXLcLpm1adNGHh4e2rlzZ465XmjmzJlOzz/55BNJ5+/ILf37D/bMzZcxRh9++KFLNWdw9Wh3fnXs2FHGGP3555/Z1luzZs18zzOj1rzyrVy5sp577jnVrFlTP/30k8vzz2667NazJL3//vt5zu9itvvF5p/X/nIltkeG559/XkePHtUjjzyS7fv1xIkTWrJkiaTzDa2Pj482b97sNOaLL77IdRlly5bV4MGD1apVK6d1ktP+m9/P14vVuHFjBQUF6ZdffslxG+f0JeeVllHjhZ+3n3/+uVJSUi7LOvDz81OzZs20YcMG1apVK9v6L+ZLZgD2wpF4AP8Zffr00bvvvqu+fftq9+7dqlmzppYvX66xY8eqffv2atmypdP44sWLq3nz5nr++efl7++vyZMna/v27Xn+zNwnn3yiuXPnqkOHDgoPD9exY8f02WefadasWYqMjFTt2rVznT4oKEjz589Xp06dVLt2bQ0YMEC33HKLAgICdPjwYX3//fc6cOCAGjVqlGfNVapU0YMPPqi3335bbm5uateunXbv3q3nn39eYWFheuyxxyRJAQEBGj9+vB544AG1bNlS/fv3V3BwsH7//Xdt2rRJ77zzjqTzjVS7du3Upk0bRUZGqnTp0jpy5Ii2bdumn376SZ999lmeOWV49NFHdfPNN0uSoqOjXZrmueee04IFC9S8eXO98MIL8vPz07vvvpvlGtiIiAi9+OKLGjFihHbt2qW2bduqSJEi+uuvv7RmzRr5+/tr9OjR1ngvLy+NHz9eJ06cUP369bVy5Uq99NJLateunW699VZJ58+Q8PLy0r333qunnnpKp0+f1pQpU3T06FGXa5akmjVratasWYqNjVX58uXl4+NzSQ1dhsaNG+vBBx/U/fffr3Xr1un222+Xv7+/kpKStHz5ctWsWVMDBgzI1zwrVKggX19fzZw5U9WqVVNAQIBKlSqlQ4cOafDgwerevbsqVaokLy8vLVu2TJs3b9YzzzyT4/w2b96c53SNGjVSkSJF9PDDD2vkyJHy9PTUzJkztWnTpjzzdXW7u5JHblzZX67E9sjQvXt3Pf/88xozZoy2b9+ufv36qUKFCjp58qR+/PFHvf/+++rRo4dat25tXZc/bdo0VahQQbVr19aaNWusLx0yJCcnq1mzZurZs6eqVq2qQoUKae3atVq8eLHTmTc1a9bU3LlzNWXKFN10001yc3NTvXr18v35erECAgL09ttvq2/fvjpy5Ii6deumkiVL6uDBg9q0aZMOHjyoKVOmXJZl5VerVq3Upk0bPf300zp+/LgaN26szZs3a+TIkapbt67uu+++y7Kct956S7feeqtuu+02DRgwQBEREfrnn3/0+++/68svv7SuzQdwHSuY++kBwJWX3d1/Dx8+bB5++GETGhpqPDw8THh4uBk+fLj10z8ZJJlBgwaZyZMnmwoVKhhPT09TtWpVM3PmzDyXu2rVKtOiRQsTEhJiPD09jZ+fn6lfv76ZPHmy0x3l83LgwAEzfPhw62ebPD09TalSpUynTp3MjBkznO4Qn3HH97Vr12aZT1pamnnttddM5cqVjaenpylevLjp3bu39VNhmS1atMg0adLE+Pv7Wz/l9tprrzmN2bRpk7n77rtNyZIljaenpwkJCTHNmzc37733nkv5ZBYREWGqVavm8joxxpgVK1aYW265xXh7e5uQkBDz5JNPmg8++CDLT8wZc/6O182aNTOFCxc23t7eJjw83HTr1s3pZ6gy9pPNmzebpk2bGl9fX1O0aFEzYMAAp7tnG2PMl19+aWrXrm18fHxM6dKlzZNPPmm+/vprI8npp8maNGliatSokW3+u3fvNq1btzaFChWyfqbKmJx/3iqndZlxh+zMPxNojDHTpk0zN998s/H39ze+vr6mQoUKpk+fPmbdunV55te3b98sdx7/9NNPTdWqVY2np6d1N/O//vrLREZGmqpVqxp/f38TEBBgatWqZSZMmJDrzyi6Ot3KlStNw4YNjZ+fnylRooR54IEHzE8//ZTlLt3Z/cScMXlv94vNP2Mdubq/GHNp2yMvCQkJplu3biY0NNR4enqawoULm4YNG5px48aZ48ePW+OSk5PNAw88YIKDg42/v7/p1KmT2b17t9Pd6U+fPm0efvhhU6tWLVO4cGHj6+trqlSpYkaOHOn0c5dHjhwx3bp1M0FBQcbhcDit//x+vmaWcRf2cePGOcVzel8kJCSYDh06mKJFixpPT09TunRp06FDB6dxOb1HcpLTsi6U23xPnTplnn76aRMeHm48PT1NaGioGTBggDl69KjTuEu5O31GPCoqypQuXdp4enqaEiVKmEaNGpmXXnrJpVoB2JvDmBzOmwSA/zCHw6FBgwZZR6Bx+W3evFm1a9fWu+++a90YryBERkZqzpw51l3egdywvwAAChqn0wMArqqdO3dqz549evbZZxUaGprl55gAAACQM25sBwC4qsaMGaNWrVrpxIkT+uyzz/K8uzYAAAD+xen0AAAAAADYBEfiAQAAAACwCZp4AAAAAABsgiYeAAAAAACb4O70F0hPT9f+/ftVqFAhORyOgk4HAAAAAHCdM8bon3/+UalSpeTmlvuxdpr4C+zfv19hYWEFnQYAAAAA4D9m3759KlOmTK5jaOIvUKhQIUnnV17hwoULOBsAAAAAwPXu+PHjCgsLs/rR3NDEXyDjFPrChQvTxAMAAAAArhpXLunmxnYAAAAAANgETTwAAAAAADZBEw8AAAAAgE1wTTwAAACQDWOMUlNTlZaWVtCpALA5d3d3eXh4XJafMaeJBwAAAC5w9uxZJSUl6eTJkwWdCoDrhJ+fn0JDQ+Xl5XVJ86GJBwAAADJJT09XYmKi3N3dVapUKXl5eV2Wo2cA/puMMTp79qwOHjyoxMREVapUSW5uF39lO008AAAAkMnZs2eVnp6usLAw+fn5FXQ6AK4Dvr6+8vT01J49e3T27Fn5+Phc9Ly4sR0AAACQjUs5UgYAF7pcnyl8MgEAAAAAYBM08QAAAAAA2ARNPAAAAAAgiwMHDqhVq1by9/dXUFCQJMnhcGj+/PmSpN27d8vhcGjjxo0FluN/EU08AAAA8B/mcDhyfURGRl72ZU6ZMkW1atVS4cKFVbhwYTVs2FBff/31ZV9OfmRuTnHehAkTlJSUpI0bN2rHjh2SpKSkJLVr166AM/tv4+70AAAAwH9YUlKS9XdsbKxeeOEF/frrr1bM19f3si+zTJkyevXVV1WxYkVJ0vTp03XnnXdqw4YNqlGjxmVf3vXg3Llz8vT0vKrz2rlzp2666SZVqlTJioWEhFyWHHDxOBIPAAAA/IeFhIRYj8DAQDkcDqfYJ598ogoVKsjLy0tVqlTRxx9/7DS9w+HQlClT1K5dO/n6+qpcuXL67LPPcl1mp06d1L59e1WuXFmVK1fWyy+/rICAAK1evTrX6RYsWKB69erJx8dHxYsXV5cuXZzyuPBIelBQkGJiYiSd/+nAwYMHKzQ0VD4+PoqIiNArr7wiSYqIiJAk3XXXXXI4HNZz6fxZA3nV//7776tjx47y8/NTtWrVtGrVKv3+++9q2rSp/P391bBhQ+3cudNpui+//FI33XSTfHx8VL58eY0ePVqpqalO833vvfd05513yt/fXy+99FK26yQiIkJjxoxRz549FRAQoFKlSuntt9/OkmN288qttoiICH3++eeaMWOG0xkZeZ2x8Msvv6h9+/YKCAhQcHCw7rvvPh06dMh6fc6cOapZs6Z8fX1VrFgxtWzZUikpKTnOD1nRxAMAAADI1rx58/Too49q2LBh2rp1qx566CHdf//9iouLcxr3/PPPq2vXrtq0aZN69+6te++9V9u2bXNpGWlpaZo1a5ZSUlLUsGHDHMctXLhQXbp0UYcOHbRhwwYtXbpU9erVc7mWSZMmacGCBZo9e7Z+/fVX/e9//7Oa9bVr10qSoqOjlZSUZD13tf4xY8aoT58+2rhxo6pWraqePXvqoYce0vDhw7Vu3TpJ0uDBg63x33zzjXr37q1HHnlEv/zyi95//33FxMTo5ZdfdprvyJEjdeedd2rLli2KiorKsbZx48apVq1a+umnnzR8+HA99thj+vbbb3OdV161rV27Vm3bttXdd9+tpKQkvfXWW3mu46SkJDVp0kR16tTRunXrtHjxYv3111+6++67rdfvvfdeRUVFadu2bYqPj1eXLl1kjMlz3sjEwElycrKRZJKTkws6FQAAABSAU6dOmV9++cWcOnWqoFO56qKjo01gYKD1vFGjRqZ///5OY7p3727at29vPZdkHn74YacxN998sxkwYECuy9q8ebPx9/c37u7uJjAw0CxcuDDX8Q0bNjS9evXK8XVJZt68eU6xwMBAEx0dbYwxZsiQIaZ58+YmPT3d5eldrf+5556znq9atcpIMlOnTrVin376qfHx8bGe33bbbWbs2LFO8/34449NaGio03yHDh2aY70ZwsPDTdu2bZ1iPXr0MO3atct1Xq7Uduedd5q+ffs6jcm8nhITE40ks2HDBmOMMc8//7xp3bq10/h9+/YZSebXX38169evN5LM7t2786zrepTbZ0t++lCOxAMAAADI1rZt29S4cWOnWOPGjbMcZb/wCHrDhg3zPBJfpUoVbdy4UatXr9aAAQPUt29f/fLLLzmO37hxo1q0aJHPCv4VGRmpjRs3qkqVKnrkkUe0ZMmSPKdxtf5atWpZfwcHB0uSatas6RQ7ffq0jh8/Lklav369XnzxRQUEBFiP/v37KykpSSdPnrSmc/VMA1fW/4XzcrW2/Fi/fr3i4uKc6qpataqk89fX165dWy1atFDNmjXVvXt3ffjhhzp69OhFL++/ihvbAQAAAMiRw+Fwem6MyRJzZboLeXl5WTe2q1evntauXau33npL77//frbj87rBnsPhyHJa9rlz56y/b7zxRiUmJurrr7/Wd999p7vvvlstW7bUnDlz8lVHdvVnvklcxmvZxdLT063/jh492uma/gw+Pj7W3/7+/rnmlp+8s5vXxW7bnKSnp6tTp0567bXXsrwWGhoqd3d3ffvtt1q5cqWWLFmit99+WyNGjNCPP/6ocuXKXfRy/2s4Eg8AAAAgW9WqVdPy5cudYitXrlS1atWcYhfekG716tXWEVhXGWN05syZHF+vVauWli5dmuPrJUqUcLrT/m+//eZ0VFuSChcurB49eujDDz9UbGysPv/8cx05ckTS+aY7LS3Nabyr9efXjTfeqF9//VUVK1bM8nBzy3+LdjHr/0rUduONN+rnn39WRERElroyvkRwOBxq3LixRo8erQ0bNsjLy0vz5s276GX+F3EkHgAAAEC2nnzySd1999268cYb1aJFC3355ZeaO3euvvvuO6dxn332merVq6dbb71VM2fO1Jo1azR16tQc5/vss8+qXbt2CgsL0z///KNZs2YpPj5eixcvznGakSNHqkWLFqpQoYLuuecepaam6uuvv9ZTTz0lSWrevLneeecd3XLLLUpPT9fTTz/tdDR8woQJCg0NVZ06deTm5qbPPvtMISEhCgoKknT+buxLly5V48aN5e3trSJFirhcf3698MIL6tixo8LCwtS9e3e5ublp8+bN2rJlS453oc/NihUr9Prrr6tz58769ttv9dlnn2nhwoW5TnMlahs0aJA+/PBD3XvvvXryySdVvHhx/f7775o1a5Y+/PBDrVu3TkuXLlXr1q1VsmRJ/fjjjzp48OAlfynyn3OZr9W3PW5sBwAA8N/Gje0CnWKTJ0825cuXN56enqZy5cpmxowZTq9LMu+++65p1aqV8fb2NuHh4ebTTz/NdTlRUVEmPDzceHl5mRIlSpgWLVqYJUuW5Jnf559/burUqWO8vLxM8eLFTZcuXazX/vzzT9O6dWvj7+9vKlWqZBYtWuR0Y7sPPvjA1KlTx/j7+5vChQubFi1amJ9++smafsGCBaZixYrGw8PDhIeH56v+zDfEu/Bmb8YYExcXZySZo0ePWrHFixebRo0aGV9fX1O4cGHToEED88EHH+Q435yEh4eb0aNHm7vvvtv4+fmZ4OBgM3HixFxzdLW2/N7YzhhjduzYYe666y4TFBRkfH19TdWqVc3QoUNNenq6+eWXX0ybNm1MiRIljLe3t6lcubJ5++2386zxenG5bmznMIb7+Wd2/PhxBQYGKjk5WYULFy7odAAAAHCVnT59WomJiSpXrpzT9cnInsPh0Lx589S5c+eCTuU/KSIiQkOHDtXQoUMLOhXkIbfPlvz0oVwTDwAAAACATdDEAwAAAABgE9zYDgAAAMBF4+rcgrV79+6CTgFXGUfiAQAAAACwCY7EX2E3PTmjoFO4ZOvH9SnoFAAAAAAA4kg8AAAAAAC2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgEN7YDAAAAXHQ1b1p8NW8uPGrUKM2fP18bN250eZqIiAgNHTpUQ4cOvWJ5ZcfhcGjevHnq3LlzjmMiIyN17NgxzZ8/3+X55rUOYmJiNHToUB07duyi53GluLJcV9ZJ06ZNVadOHU2cOFHSpW/j3bt3q1y5ctqwYYPq1KlzUfNAVhyJBwAAAK4zK1eulLu7u9q2bXvVlulwOFxumrdv3y6Hw6Eff/zRKX7zzTfL29tbJ0+etGJnz56Vn5+fPvjgA0lSUlKS2rVrJ+l8k+hwOK5K09yjRw/t2LHjii9HOv+FgcPhULVq1bK8Nnv2bDkcDkVERFixJ554QkuXLr0queVHWFiYkpKSdMMNNxR0KtcVmngAAADgOjNt2jQNGTJEy5cv1969ews6nSyqVq2q0NBQxcXFWbETJ05ow4YNKlmypFauXGnFf/zxR506dUrNmjWTJIWEhMjb2/uq5+zr66uSJUteteX5+/vr77//1qpVq5zi06ZNU9myZZ1iAQEBKlas2FXLzVXu7u4KCQmRhwcngF9ONPEAAADAdSQlJUWzZ8/WgAED1LFjR8XExGQZ8+qrryo4OFiFChVSv379dPr0aafXmzZtmuUU6s6dOysyMjLbZWYcFb7rrruyHCXOSdOmTRUfH289/+GHH1S5cmXdcccdTvH4+HiVLl1alSpVkuR8xL9cuXKSpLp168rhcKhp06ZOy3jjjTcUGhqqYsWKadCgQTp37lyeeWVITExUxYoVNWDAAKWnpysmJkZBQUFOY/Jaj/Hx8WrQoIH8/f0VFBSkxo0ba8+ePS4t38PDQz179tS0adOs2B9//KH4+Hj17NnTaeyoUaOcTldPS0vT448/rqCgIBUrVkxPPfWUjDFO06SkpKhPnz4KCAhQaGioxo8fn2dOycnJevDBB1WyZEkVLlxYzZs316ZNm3Icf+GZEvHx8XI4HFq6dKnq1asnPz8/NWrUSL/++qvTdAsWLFC9evXk4+Oj4sWLq0uXLtZrR48eVZ8+fVSkSBH5+fmpXbt2+u2336zXM7bTV199pSpVqsjPz0/dunVTSkqKpk+froiICBUpUkRDhgxRWlqaNd3Zs2f11FNPqXTp0vL399fNN9/stB9eS2jiAQAAgOtIbGysqlSpoipVqqh3796Kjo52auBmz56tkSNH6uWXX9a6desUGhqqyZMnX9Iy165dK0mKjo5WUlKS9Tw3zZo10/Lly5WamipJiouLU9OmTdWkSROnI/RxcXHWUfgLrVmzRpL03XffKSkpSXPnznWabufOnYqLi9P06dMVExOT7Rca2dm6dasaN26s7t27a8qUKXJzy9o25bUeU1NT1blzZzVp0kSbN2/WqlWr9OCDD8rhcLiUgyT169dPsbGx1uUFMTExatu2rYKDg3Odbvz48Zo2bZqmTp2q5cuX68iRI5o3b57TmCeffFJxcXGaN2+elixZovj4eK1fvz7HeRpj1KFDBx04cECLFi3S+vXrdeONN6pFixY6cuSIyzVJ0ogRIzR+/HitW7dOHh4eioqKsl5buHChunTpog4dOmjDhg1Ww58hMjJS69at04IFC7Rq1SoZY9S+fXunL2hOnjypSZMmadasWVq8eLHi4+PVpUsXLVq0SIsWLdLHH3+sDz74QHPmzLGmuf/++7VixQrNmjVLmzdvVvfu3dW2bVunLwiuFZzXAAAAAFxHpk6dqt69e0uS2rZtqxMnTmjp0qVq2bKlJGnixImKiorSAw88IEl66aWX9N1332U5ipwfJUqUkCQFBQUpJCTEpWmaNm2qlJQUrV27Vg0bNlR8fLyefPJJ3X777brvvvt08uRJeXh4aPXq1XrnnXdyXW6xYsWyLLdIkSJ655135O7urqpVq6pDhw5aunSp+vfvn2teq1atUseOHTV8+HA98cQTOY7Laz0eP35cycnJ6tixoypUqCBJ2V7jnps6deqoQoUKmjNnju677z7FxMTozTff1K5du3KdbuLEiRo+fLi6du0qSXrvvff0zTffWK+fOHFCU6dO1YwZM9SqVStJ0vTp01WmTJkc5xkXF6ctW7bo77//ti5neOONNzR//nzNmTNHDz74oMt1vfzyy2rSpIkk6ZlnnlGHDh10+vRp+fj46OWXX9Y999yj0aNHW+Nr164tSfrtt9+0YMECrVixQo0aNZIkzZw5U2FhYZo/f766d+8uSTp37pymTJlirfdu3brp448/1l9//aWAgABVr15dzZo1U1xcnHr06KGdO3fq008/1R9//KFSpUpJOn+fgcWLFys6Olpjx451ubargSPxAAAAwHXi119/1Zo1a3TPPfdIOn9Kdo8ePZxOyd62bZsaNmzoNN2Fz6+GSpUqqUyZMoqPj9fx48e1YcMGNWnSRMHBwSpXrpxWrFih1atX69SpU2revHm+51+jRg25u7tbz0NDQ/X333/nOs3evXvVsmVLPffcc7k28FLe67Fo0aKKjIxUmzZt1KlTJ7311ltKSkrKdx1RUVGKjo5WQkKCTpw4ofbt2+c6Pjk5WUlJSU65eHh4OB3N3rlzp86ePZsl3ypVquQ43/Xr1+vEiRMqVqyYAgICrEdiYqJ27tyZr5pq1apl/R0aGipJ1rbZuHGjWrRoke1027Ztk4eHh26++WYrVqxYMVWpUkXbtm2zYn5+flYDL0nBwcGKiIhQQECAUyxjmT/99JOMMapcubJTbQkJCfmu7WrgSDwAAABwnZg6dapSU1NVunRpK2aMkaenp44ePaoiRYq4NB83N7cs11Dn53pyVzVt2lRxcXGqVauWKlWqZN04LuOUem9vb4WHh7t0jf2FPD09nZ47HA6lp6fnOk2JEiVUqlQpzZo1S/369VPhwoXzvdzMoqOj9cgjj2jx4sWKjY3Vc889p2+//Va33HKLy/Po1auXnnrqKY0aNUp9+vS5LDeJu3DbuiI9PV2hoaHZXid+4b0C8pJ522RcXpCxbXx9fXOcLqe8jTFOlylkt+1z2x/S09Pl7u6u9evXO33xI8mp8b9WcCQeAAAAuA6kpqZqxowZGj9+vDZu3Gg9Nm3apPDwcM2cOVPS+VO6V69e7TTthc9LlCjhdNQ4LS1NW7duzXX5np6eTjcKc0WzZs20cuVKffvtt043pWvSpIni4+MVHx+f61F4Ly8vK7/LwdfXV1999ZV8fHzUpk0b/fPPPzmOdWU9Sudvujd8+HCtXLlSN9xwgz755JN85VS0aFHdcccdSkhIcLp2PCeBgYEKDQ11yiU1NdXpeveKFSvK09PTaczRo0dz/Qm9G2+8UQcOHJCHh4cqVqzo9ChevHi+aspNrVq1cvy5vOrVqys1NdXppwkPHz6sHTt25PtShczq1q2rtLQ0/f3331lqc/XykKuJJh4AAAC4Dnz11Vc6evSo+vXrpxtuuMHp0a1bN02dOlWS9Oijj2ratGmaNm2aduzYoZEjR+rnn392mlfz5s21cOFCLVy4UNu3b9fAgQN17NixXJcfERGhpUuX6sCBAzp69KhLOTdr1kwpKSmaNm2adY20dL6JX7dunVavXp3jTe0kqWTJkvL19dXixYv1119/KTk52aXl5sbf318LFy6Uh4eH2rVrpxMnTmQ7Lq/1mJiYqOHDh2vVqlXas2ePlixZctHNZkxMjA4dOqSqVau6NP7RRx/Vq6++qnnz5mW7/QICAtSvXz89+eSTWrp0qbZu3arIyMhsb+CXoWXLlmrYsKE6d+6sb775Rrt379bKlSv13HPPad26dfmuKScjR47Up59+qpEjR2rbtm3asmWLXn/9dUnnL8G488471b9/fy1fvlybNm1S7969Vbp0ad15550XvczKlSurV69e6tOnj+bOnavExEStXbtWr732mhYtWnS5SrtsOJ0eAAAAcNH6cX0KOoUcTZ06VS1btlRgYGCW17p27aqxY8fqp59+sm7k9fTTT+v06dPq2rWrBgwY4HTjs6ioKG3atMk6ffuxxx7LtZmWzt8R/fHHH9eHH36o0qVLa/fu3XnmXK5cOYWHh2vPnj1OTXzp0qVVtmxZ7dy5M9flenh4aNKkSXrxxRf1wgsv6LbbbrssPwsWEBCgr7/+Wm3atFH79u319ddfZxmT13r08/PT9u3bNX36dB0+fFihoaEaPHiwHnrooXzn4+vrm+tp5hcaNmyYkpKSrMY8KipKd911l9OXHOPGjdOJEyd0xx13qFChQho2bFiuX4I4HA4tWrRII0aMUFRUlA4ePKiQkBDdfvvted4tPz+aNm2qzz77TGPGjNGrr76qwoUL6/bbb7dej46O1qOPPqqOHTvq7Nmzuv3227Vo0aIsp8vnV3R0tF566SUNGzZMf/75p4oVK6aGDRvmeQ+CguAwF3NBxHXs+PHjCgwMVHJy8iVfAyNJNz054zJkVbCu5f9ZAQAAXG6nT59WYmKiypUrJx8fn4JOB8B1IrfPlvz0oZxODwAAAACATdDEAwAAALjsatSo4fRzXZkfGTfZ+69i3eBScE08AAAAgMtu0aJFOf4s3eW8htqOWDe4FDTxAAAAAC678PDwgk7hmsW6waXgdHoAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAECeIiMj1blz5ysy76ZNm2ro0KFXZN6SFBMTo6CgoDzHORwOzZ8/P1/zjoiI0MSJE3N83ZXa8prHleLKcvNaJ7t375bD4dDGjRslSfHx8XI4HDp27NhF5zVq1CjVqVPnoqe/3vETcwAAAICL9r5Y86otq+wLW/I9TWRkpKZPny5Jcnd3V6lSpdShQweNHTtWRYoUcWkeu3fvVrly5bRhwwanRuqtt96SMSbfOV1OzzzzjL744gtt27bNim3btk3Vq1dX79699fHHH1vxjz/+WFFRUTp69Kh69Oih9u3bW6+NGjVK8+fPtxrPK2nu3Lny9PS84suRzn9hkJCQoFdeeUXPPPOM02vt27fX119/rZEjR2rUqFGSpLVr18rf3/+q5JYfTzzxhIYMGVLQaVyzOBIPAAAAXEfatm2rpKQk7d69Wx999JG+/PJLDRw48JLnGxgYmOvR7LNnz17yMvLSrFkzbd++XQcOHLBi8fHxCgsLU1xcnNPY+Ph4NWjQQAEBAfL19VXJkiWveH7ZKVq0qAoVKnTVlhcWFqbo6Gin2P79+7Vs2TKFhoY6xUuUKCE/P7+rlpurAgICVKxYsYJO45pFEw8AAABcR7y9vRUSEqIyZcqodevW6tGjh5YsWeI0Jjo6WtWqVZOPj4+qVq2qyZMnW6+VK1dOklS3bl05HA41bdpUUtbT6Zs2barBgwfr8ccfV/HixdWqVStJ0i+//KL27dsrICBAwcHBuu+++3To0CFrupSUFPXp00cBAQEKDQ3V+PHjXa7t1ltvlaenp+Lj461YfHy8Bg0apH/++Ue///67U7xZs2aSnE+nj4mJ0ejRo7Vp0yY5HA45HA7FxMRY0x06dEh33XWX/Pz8VKlSJS1YsMDl/KTz6zYwMFDffvuttZ4yn07/999/q1OnTvL19VW5cuU0c+bMLPMYNWqUypYtK29vb5UqVUqPPPKIy8vv2LGjDh8+rBUrVlixmJgYtW7dOssXGReeTv/bb7/p9ttvl4+Pj6pXr27VkNmaNWtUt25d+fj4qF69etqwYUOeOa1cuVK33367fH19FRYWpkceeUQpKSk5jr/wdPqMfe+NN95QaGioihUrpkGDBuncuXPWmDNnzuipp55SWFiYvL29ValSJU2dOtV6PSEhQQ0aNJC3t7dCQ0P1zDPPKDU11Xq9adOmGjJkiIYOHaoiRYooODhYH3zwgVJSUnT//ferUKFCqlChgr7++munXPPa368EmngAAADgOrVr1y4tXrzY6XTuDz/8UCNGjNDLL7+sbdu2aezYsXr++eet0/DXrFkjSfruu++UlJSkuXPn5jj/6dOny8PDQytWrND777+vpKQkNWnSRHXq1NG6deu0ePFi/fXXX7r77rutaZ588knFxcVp3rx5WrJkieLj47V+/XqX6vH391f9+vWdjronJCSoRYsWaty4sRXft2+fdu3aZTXxmfXo0UPDhg1TjRo1lJSUpKSkJPXo0cN6ffTo0br77ru1efNmtW/fXr169dKRI0dcyu+NN97QE088oW+++cb6UuNCkZGR2r17t5YtW6Y5c+Zo8uTJ+vvvv63X58yZowkTJuj999/Xb7/9pvnz56tmTdcv4/Dy8lKvXr2cjsbHxMQoKioq1+nS09PVpUsXubu7a/Xq1Xrvvff09NNPO41JSUlRx44dVaVKFa1fv16jRo3SE088ket8t2zZojZt2qhLly7avHmzYmNjtXz5cg0ePNjlmiQpLi5OO3fuVFxcnKZPn66YmBinL1/69OmjWbNmadKkSdq2bZvee+89BQQESJL+/PNPtW/fXvXr19emTZs0ZcoUTZ06VS+99JLTMqZPn67ixYtrzZo1GjJkiAYMGKDu3burUaNG+umnn9SmTRvdd999OnnypCS5tL9fCVwTDwAAAFxHvvrqKwUEBCgtLU2nT5+WJL355pvW62PGjNH48ePVpUsXSeePvP/yyy96//331bdvX5UoUUKSVKxYMYWEhOS6rIoVK+r111+3nr/wwgu68cYbNXbsWCs2bdo0hYWFaceOHSpVqpSmTp2qGTNmWE3u9OnTVaZMGZfra9q0qebMmSPp/FHQU6dOqW7dumrSpIni4+PVv39/xcXFydvbW40aNcoyva+vrwICAuTh4ZFtfZGRkbr33nslSWPHjtXbb7+tNWvWqG3btrnmNXz4cE2fPl3x8fE5Nt07duzQ119/rdWrV+vmm2+WJE2dOlXVqlWzxuzdu1chISFq2bKlPD09VbZsWTVo0MC1lfP/+vXrp1tvvVVvvfWW1q9fr+TkZHXo0MG6Fj473333nbZt26bdu3db22Ps2LFq166dNWbmzJlKS0vTtGnT5Ofnpxo1auiPP/7QgAEDcpzvuHHj1LNnT+tshEqVKmnSpElq0qSJpkyZIh8fH5dqKlKkiN555x25u7uratWq6tChg5YuXar+/ftrx44dmj17tr799lu1bNlSklS+fHlr2smTJyssLEzvvPOOHA6Hqlatqv379+vpp5/WCy+8IDe388e2a9eureeee07S+e356quvqnjx4urfv7+k8/v3lClTtHnzZt1yyy2aMmVKrvt75cqVXaotv2jiAQAAgOtIs2bNNGXKFJ08eVIfffSRduzYYd0k7ODBg9q3b5/69etnNSaSlJqaqsDAwHwvq169ek7P169fr7i4OOsIaGY7d+7UqVOndPbsWTVs2NCKFy1aVFWqVHF5mc2aNdPYsWO1f/9+xcfH69Zbb5W7u7uaNGmiSZMmSTp/Kv0tt9wiX1/ffNdUq1Yt629/f38VKlTI6Uh5dsaPH6+UlBStW7fOqXm80LZt2+Th4eG03qpWrep0r4Hu3btr4sSJKl++vNq2bav27durU6dO8vBwvXWrVauWKlWqpDlz5iguLk733XdfnjfX27Ztm8qWLev0hUrm7ZQxpnbt2k7X0V845kLr16/X77//7nTZgDFG6enpSkxMdPoCIzc1atSQu7u79Tw0NFRbtpy/+ePGjRutfSCn2ho2bCiHw2HFGjdurBMnTuiPP/5Q2bJlJTlve3d3dxUrVszpC5ng4GBJsvaHvPZ3mngAAAAAefL391fFihUlSZMmTVKzZs00evRojRkzRunp6ZLOn1KfcSQ4Q+YGKT/Lyiw9PV2dOnXSa6+9lmVsaGiofvvtt3wv40KNGzeWl5eX4uPjFRcXZzVu9erVU3Jysnbs2KG4uDhFRkZe1PwvbHYdDoe13nJy2223aeHChZo9e3aWu8JnlnF3/8zN5IXCwsL066+/6ttvv9V3332ngQMHaty4cUpISMjXXe6joqL07rvv6pdffrEukchNdr88cGGeF/PrBOnp6XrooYeyva4/o3l2RW7bJa8va4wxOdaSOZ7dMjLHMsZmLDev/f1K4Zp4AAAA4Do2cuRIvfHGG9q/f7+Cg4NVunRp7dq1SxUrVnR6ZNzQzsvLS5KUlpaW72XdeOON+vnnnxUREZFl/hlfLnh6emr16tXWNEePHtWOHTtcXoavr69uvvlmxcfH6/vvv7duvOfh4aFGjRppxowZ2r17d7bXw2fw8vK6qPpy0qBBAy1evFhjx47VuHHjchxXrVo1paamat26dVbs119/zfKb6r6+vrrjjjs0adIkxcfHa9WqVdZRZ1f17NlTW7Zs0Q033KDq1avnOb569erau3ev9u/fb8VWrVqVZcymTZt06tQpK5Z5W2YnY5+4cH+oWLGita9dqpo1ayo9PV0JCQnZvl69enWtXLnS6UuIlStXqlChQipduvRFLzev/f1KoYkHAAAArmNNmzZVjRo1rOt2R40apVdeeUVvvfWWduzYoS1btig6Otq6br5kyZLy9fW1btKVnJzs8rIGDRqkI0eO6N5779WaNWu0a9cuLVmyRFFRUUpLS1NAQID69eunJ598UkuXLtXWrVsVGRlpXZPsqmbNmmnWrFk6deqUbrzxRiuecUp9RqOfk4iICCUmJmrjxo06dOiQzpw5k6/lZ6dhw4b6+uuv9eKLL2rChAnZjqlSpYratm2r/v3768cff9T69ev1wAMPOB1JjomJ0dSpU7V161bt2rVLH3/8sXx9fRUeHp6vfIoUKaKkpCQtXbrUpfEtW7ZUlSpV1KdPH23atEk//PCDRowY4TSmZ8+ecnNzU79+/fTLL79o0aJFeuONN3Kd79NPP61Vq1Zp0KBB2rhxo3777TctWLDgsv4OfEREhPr27auoqCjNnz9fiYmJio+P1+zZsyVJAwcO1L59+zRkyBBt375dX3zxhUaOHKnHH3883/teZnnt71cKp9MDAAAALir7Qv6Ohl4rHn/8cd1///16+umn9cADD8jPz0/jxo3TU089JX9/f9WsWdO68ZiHh4cmTZqkF198US+88IJuu+02p590y02pUqW0YsUKPf3002rTpo3OnDmj8PBwtW3b1mqWxo0bpxMnTuiOO+5QoUKFNGzYsHx9USCdb+JffPFFtW3b1ula8SZNmui5555TixYt5O3tneP0Xbt21dy5c9WsWTMdO3ZM0dHRF336fWaNGzfWwoUL1b59e7m7u2d7Cnl0dLQeeOABNWnSRMHBwXrppZf0/PPPW68HBQXp1Vdf1eOPP660tDTVrFlTX3755UX9bnrma+3z4ubmpnnz5qlfv35q0KCBIiIiNGnSJKcb+gUEBOjLL7/Uww8/rLp166p69ep67bXX1LVr1xznW6tWLSUkJGjEiBG67bbbZIxRhQoVnH4R4HKYMmWKnn32WQ0cOFCHDx9W2bJl9eyzz0qSSpcurUWLFunJJ59U7dq1VbRoUfXr18+6id3FcmV/vxIc5mIubLiOHT9+XIGBgUpOTlbhwoUveX43PTnjMmRVsNaP61PQKQAAAFw1p0+fVmJiosqVK+fynbMBIC+5fbbkpw/ldHoAAAAAAGyCJh4AAADANeGHH35QQEBAjo//MtYNMnBNPAAAAIBrQr169bRx48aCTuOaxLpBBpp4AAAAANcEX19f6zfu4Yx1gwycTg8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAP9xo0aNUp06dfI1TUREhCZOnHhF8rlU8fHxcjgcOnbsWLav7969Ww6HI9efbMtrHleKK8uNiYlRUFBQrvO5cJtGRkaqc+fOl5TbtbzN/0sK9CfmXnnlFc2dO1fbt2+Xr6+vGjVqpNdee01VqlSxxkRGRmr69OlO0918881avXq19TwiIkJ79uzRp59+qnvuucdpbI0aNfTLL78oOjpakZGRV7QeAAAAXN8av934qi1rxZAVFz3typUrddttt6lVq1ZavHjxZcwqZw6HQ/PmzXO5UYyJidH999+vNm3aOOV47NgxFSlSRHFxcWratOkVyTUsLExJSUkqXrz4FZl/Zrt371a5cuXk7u6uPXv2qHTp0tZrSUlJCgsLU1pamhITExUREaFGjRopKSlJgYGBVzy3/Fq7dq38/f0LOo3/vAI9Ep+QkKBBgwZp9erV+vbbb5WamqrWrVsrJSXFaVzbtm2VlJRkPRYtWpRlXmFhYYqOjnaKrV69WgcOHGBHAwAAwH/KtGnTNGTIEC1fvlx79+4t6HRy5OHhoaVLlyouLu6qLtfd3V0hISHy8Lh6xzRLlSqlGTNmOMWmT5/u1NRLkpeXl0JCQuRwOK5abq4qUaKE/Pz8CjqN/7wCbeIXL16syMhI1ahRQ7Vr11Z0dLT27t2r9evXO43z9vZWSEiI9ShatGiWefXq1UsJCQnat2+fFZs2bZp69ep1Vd+cAAAAQEFKSUnR7NmzNWDAAHXs2FExMTFZxrz66qsKDg5WoUKF1K9fP50+fdrp9aZNm2ro0KFOsc6dO+d4ZmtERIQk6a677pLD4bCe58Xf31/333+/nnnmmVzHbdmyRc2bN5evr6+KFSumBx98UCdOnHBpGZJ06tQpdejQQbfccouOHDmS7en0ixYtUuXKleXr66tmzZpp9+7dTvPYs2ePOnXqpCJFisjf3181atTI9uBiTvr27ZvloGNMTIz69u3rFMvudPqYmBiVLVtWfn5+uuuuu3T48OEs889rm17IGKPXX39d5cuXl6+vr2rXrq05c+bkOs2Fp9M7HA599NFHuuuuu+Tn56dKlSppwYIFTtP8/PPP6tChgwoXLqxChQrptttu086dOyVJ6enpevHFF1WmTBl5e3urTp06TmdlZGyn2bNn67bbbpOvr6/q16+vHTt2aO3atapXr54CAgLUtm1bHTx40Gm50dHRqlatmnx8fFS1alVNnjw519rs5Jq6Jj45OVmSsjTp8fHxKlmypCpXrqz+/fvr77//zjJtcHCw2rRpY516f/LkScXGxioqKirXZZ45c0bHjx93ekhSamqq9UhPT5d0fifLLp6WlpZj3MNN1iPju7TMMY9MWyA/cccFcXdH7nE3h3PcLY+4e6Z4brUaY7Ksr4y4McbluKQs8bS0tGzXe07xS9lO1ERN1ERN1ERN1ERNmeMZy8rIMfPfV0vmHLLLJaf4rFmzVKVKFVWuXFm9e/dWdHS00tPTrddjY2M1cuRIvfTSS1q7dq1CQkKcGpzslpNdXpn/XrNmjaTzB9H279+vNWvWuJS7dP7a7S1btuizzz7LsjxjjFJSUtS2bVsVKVJEa9as0WeffabvvvtOgwcPznX+Gc+Tk5PVunVrnT17Vt99952KFCmSZfzevXvVpUsXtWvXThs2bFC/fv2sLxYyxgwaNEhnzpxRQkKCNm/erFdffVUBAQEubSdJ6tSpk44ePaoffvhBxhj98MMPOnLkiDp27Jhj7tL5s4ujoqI0YMAAbdiwQU2bNtVLL73kNE3GNn355ZezbNPs1qkxRiNGjFB0dLQmT56srVu3aujQoerdu7cSEhJyrCm7PEePHq3u3btr06ZNateunXr16mV9yfDHH3/o9ttvl4+Pj5YuXap169YpKipK586dkzFGEydO1Pjx4zVu3Dht3rxZrVu31h133KEdO3Y4LWPkyJEaMWKE1q9fLw8PD91777166qmnNHHiRH3//ffauXOnnn/+eWuaDz74QCNGjNBLL72kX375RWPHjtXzzz+vmJiYfL+fLnc8Q3afb666Zg5RG2P0+OOP69Zbb9UNN9xgxdu1a6fu3bsrPDxciYmJev7559W8eXOtX79e3t7eTvOIiorSsGHDNGLECM2ZM0cVKlTI8wYdr7zyikaPHp0lvmHDBus0/BIlSqhChQpKTEx0+oanTJkyKlOmjHbs2GF9ASFJ5cuXV8mSJbV161Z1q/rv6Sbxe07rQEq67qzkK0/3f0+PWbTzlE6eM05jJWnO9pPy83SofQVfK3YuzejzX08p2N9NTcN9rHjymXR9vfO0ygV5qEEpLyt+4ESa4veeUfXinrqhhKcV33U0VWuSzqpeiJfKF/l3N9h68Jy2Hjyn28K8FRLgLklat26dU02nTp2yxletWlVBQUHasGGD9T9FSapVq5a8vLy0bt06p5rq1auns2fPavPmzVbM3d1d9evXV3JysrZv327FM74RPHTokHbt2mXFAwMDVa1aNe3fv19//PGHFb+U7URN1ERN1ERN1ERN1JS5Jh8fH508eVLu7u7y8vLS6dOnnXK8GtLS0pyOprq5ucnPz0+pqak6c+aMFXd3d5evr6/OnTuns2fP6sMPP1T37t115swZtW3bVidOnNDChQvVrFkzSdLEiRMVFRWl3r17Ky0tTcOHD9eSJUt09uxZSeePWmd84ZKSkiIfHx95eHgoLS1N586dy3Lpa0pKinWKtY+Pj0JCQpSenu40zuFwyN/f36mmjBpKlSqlwYMH69lnn1WrVq2cpjt37pymTZumU6dOafLkyQoMDFTNmjX15ptvqmvXrnrhhRdUsmRJeXl5OW2njGX8+eef6t27tyIiIjRt2jQr34xtefr0aaWkpGjSpEmKiIjQ+PHj5e7urjJlyuinn37ShAkTlJKSoqCgIO3du1edOnVS+fLlJUkhISHy9/dXampqrtvp5MmTks5/QdS7d29NnTpVdevW1QcffKC7777bat7Onj2rlJQUa17nzp2TJE2YMEEtW7bUkCFDJEkDBw7UqlWrtHjxYmtdvfnmm4qMjNQDDzyglJQUa5ueOXNG6enpcnNz07lz56xtmpKSogkTJujbb7+1+qXu3bsrISFB77//vho3bpxtTZnzzBAZGalu3brp7NmzGjFihN555x2tWLFCd9xxhyZNmqTChQvro48+kqenp7y8vFSlShWdOnVKKSkpeuONNzR06FB169ZNnp6eGjVqlJYtW6Y33nhDb775prWdhgwZoltvvVWS9NBDD+n+++/Xd999Z+Xeu3dvzZw501rPY8aM0csvv6w2bdrI4XCoS5cu2rp1q6ZMmaJu3brl6/2UwcPDQz4+Pjpz5oz15aGkLPteBm9vb3l6eurUqVPWl4QZ20NSls89V89ekSSHudpfKeZg0KBBWrhwoZYvX64yZcrkOC4pKUnh4eGaNWuWunTpIul8wUOHDtXgwYNVpkwZ65uobt26afDgwQoKCtLEiROzPf3nzJkzThvt+PHjCgsL0+HDh1W4cGFJ5zewm5ub0tPTrZWeOZ6Wlub0zUnmeKNnZ1rxtHTJyPkouySl/v8s8xN3SHLPFDdGSjM5x90c/x5ll6R0c/6RU9zdIWVchrPi5Z451uru7i6Hw+G0I2fEJWX5n11OcQ8PDxljnOIOh0Pu7u5Z1ntO8UvZTtRETdRETdRETdRETRk1nTx5Unv37lW5cuXk4+Mjh8Nh5X3rO7fqalk+eHmWWOZcsov/+uuvqlmzpvbt26fg4GA5HA4NGjRIR48etZqcokWL6q233tJ9991nTf/YY48pPj5eGzdulDFGzZo1U+3ata1Tpx0Ohzp37qygoCDrlPBy5crp0UcftU67d3Nz09y5c3O8sd2FucfExOixxx7TsWPHdPToUVWoUEGvvPKK7r77bhUtWlRxcXFq0qSJHn/8cW3cuFHLli2z5pNx87v4+HjdfvvtWeYfHx+v5s2bq0yZMqpfv75mz55t7T/S+dO0y5cvr59++kl16tRRly5dFBQUZDX6kvTFF1/orrvu0pEjRxQUFKSPPvpIAwcOVIMGDdSiRQt17dpVtWvXznV7XLgsT09PNWzYUDt27FDlypW1cuVKpaam6sYbb9SuXbsUERFh5X7kyBEVKVJEdevWVefOnfXCCy9Y8580aZJGjhypo0ePWtt04sSJ6tu3r7XcjG26YcMGSdL999+vY8eOad68eVq7dq1uvvnmLPcOO3v2rOrWret0E/HMNUVERGTZ5rNnz7YaY0kKCgrSpEmT1LdvX7Vv314lSpRwuqQjY90cP35cQUFB1nbOiD/22GPavHmzli5daq27H3/8UfXr15ckxcXFqUWLFvrrr79UokQJSedPnX/iiSd0+PBhHTx4UMHBwfL19ZWb27+NUWpqqgIDA3XgwIFst1NO2+9yxk+fPm3d6PDCS74zvixKTk62+tCcXBNH4ocMGaIFCxbo+++/z7WBl6TQ0FCFh4frt99+y/Kah4eH7rvvPo0cOVI//vij5s2bl+eyvb29sxzRz5jXhSs248P9Qpk/EC6Mp6ZnjWcXy2/c5DOe0Zy7Gk8z/z8zyWk95FRrTvcdyE/c4XBkG89pvec3ntt2cjXH/MapiZokasopx/zGqYmaJGrKKcf8xqkp97iHh4ccDof1yJj/1ZbTMnOLT5s2TampqU7/pjbGyNPT02p8s5vPhX9nrJvM8YwjwxeOze15brln/rtIkSIaPny4XnzxRXXq1CnLmOyWI53fXtnFM/7boUMHff7559q2bZtq1qyZ43yNMTkuIyPev39/tW3bVgsXLtSSJUv06quvavz48dYR8pxqzfzfG264QVWrVlXPnj1VrVo11axZ07ouP6d9LrvcclufOf0387iMxnLhwoVZbqzn7e2d5zbM/Lqnp2eWPDLm7+vrm2MO2W3DC9d5xnMvLy+n8dnF0tPTnZb94Ycf6uabb3ZabsYXhxfmklOdlzue+e8LP6/y8xlToNfEG2M0ePBgzZ07V8uWLVO5cuXynObw4cPat2+fQkNDs309KipKCQkJuvPOO50+pAAAAIDrWWpqqmbMmKHx48dr48aN1mPTpk0KDw+3jsRXq1Yty5HWC5+XKFFCSUlJ1vO0tDRt3bo11+V7enpe0iUHQ4YMkZubm9566y2nePXq1bVx40anU7hXrFghNzc3Va5cOdd5vvrqq+rbt69atGihX375Jcdx1atXz3OdSOd/Eevhhx/W3LlzNWzYMH344YeulOYkKipK8fHxed67Kz+5ubJNL5ynt7e39u7dq4oVKzo9wsLCXKwkb7Vq1dIPP/xgfQGUWeHChVWqVCktX+58xsnKlStVrVq1i15mcHCwSpcurV27dmWpzZV+0w4K9Ej8oEGD9Mknn+iLL75QoUKFrFMbAgMD5evrqxMnTmjUqFHq2rWrQkNDtXv3bj377LMqXry47rrrrmznWa1aNR06dIifPgAAAMB/yldffaWjR4+qX79+WX5jvFu3bpo6daoGDx6sRx99VH379lW9evV06623aubMmfr555+ta70lqXnz5nr88ce1cOFCVahQQRMmTHC6W3p2IiIitHTpUjVu3Fje3t75PqDm4+Oj0aNHa9CgQU7xXr16aeTIkerbt69GjRqlgwcPasiQIbrvvvsUHByc53zfeOMNpaWlqXnz5oqPj1fVqlWzjHn44Yc1fvx4Pf7443rooYe0fv36LHf1Hzp0qNq1a6fKlSvr6NGjWrZs2UU1m/3791f37t0VFBTk0vhHHnlEjRo10uuvv67OnTtryZIlTndwl+TSNs2sUKFCeuKJJ/TYY48pPT1dt956q44fP66VK1cqICAgyx3zL9bgwYP19ttv65577tHw4cMVGBio1atXq0GDBqpSpYqefPJJjRw50rqXWXR0tDZu3Gh94XSxRo0apUceeUSFCxdWu3btdObMGa1bt05Hjx7V448/fllqK0gF2sRPmTJF0vmfsMgsOjpakZGRcnd315YtWzRjxgwdO3ZMoaGhatasmWJjY1WoUKEc51usWLErmTYAAAD+o1YMWVHQKeRo6tSpatmyZZYGXpK6du2qsWPH6qefflKPHj20c+dOPf300zp9+rS6du2qAQMG6JtvvrHGR0VFadOmTerTp488PDz02GOPWTfGy0lGE/zhhx+qdOnSWX6izRV9+/bV+PHjnY6a+/n56ZtvvtGjjz6q+vXry8/PT127dtWbb77p8nwnTJjg1Mh7eXk5vV62bFl9/vnneuyxxzR58mQ1aNBAY8eOdTpanpaWpkGDBumPP/5Q4cKF1bZtW02YMCHfNXp4eKh48eIuj7/lllv00UcfaeTIkRo1apRatmyp5557TmPGjLHGuLJNLzRmzBiVLFlSr7zyinbt2qWgoCDdeOONevbZZ/NdU06KFSumZcuW6cknn1STJk3k7u6uOnXqqHHjxpLOf0Fx/PhxDRs2TH///beqV6+uBQsWqFKlSpe03AceeEB+fn4aN26cnnrqKfn7+6tmzZpZfjbRrq6ZG9tdK44fP67AwECXbijgipuenHEZsipY68f1KegUAAAArprTp08rMTHRurEdAFwOuX225KcPvaZ+Jx4AAAAAAOSMJh4AAADAZVejRg0FBARk+7jUa56vJQ8//HCOdT788MMFnR6uQ9fET8wBAAAAuL4sWrQo27uSS3LphnR28eKLL+qJJ57I9rXLcXkucCGaeAAAAACXXXh4eEGncFWULFlSJUuWLOg08B/C6fQAAABANrj/M4DL6XJ9ptDEAwAAAJl4enpKkk6ePFnAmQC4nmR8pmR8xlwsTqcHAAAAMnF3d1dQUJD+/vtvSed/p9zhcBRwVgDsyhijkydP6u+//1ZQUJDc3d0vaX408QAAAMAFQkJCJMlq5AHgUgUFBVmfLZeCJh4AAAC4gMPhUGhoqEqWLJnjHdYBwFWenp6XfAQ+A008AAAAkAN3d/fL9g9vALgcuLEdAAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBNFGgT/8orr6h+/foqVKiQSpYsqc6dO+vXX391GmOM0ahRo1SqVCn5+vqqadOm+vnnn53GREREyOFwaNasWVmWUaNGDTkcDsXExFzJUgAAAAAAuOIKtIlPSEjQoEGDtHr1an377bdKTU1V69atlZKSYo15/fXX9eabb+qdd97R2rVrFRISolatWumff/5xmldYWJiio6OdYqtXr9aBAwfk7+9/VeoBAAAAAOBKKtAmfvHixYqMjFSNGjVUu3ZtRUdHa+/evVq/fr2k80fhJ06cqBEjRqhLly664YYbNH36dJ08eVKffPKJ07x69eqlhIQE7du3z4pNmzZNvXr1koeHx1WtCwAAAACAK+Ga6m6Tk5MlSUWLFpUkJSYm6sCBA2rdurU1xtvbW02aNNHKlSv10EMPWfHg4GC1adNG06dP13PPPaeTJ08qNjZWCQkJmjFjRo7LPHPmjM6cOWM9P378uCQpNTVVqampkiQ3Nze5ubkpPT1d6enp1tiMeFpamowx2cY9Mn1NkpYuGckpJkmp/z/L/MQdktwzxY2R0kzOcTfH+UeGdHP+kVPc3SE5/j+empqaY63u7u5yOBzWusocl6S0tDSX4h4eHjLGOMUdDofc3d2zrPec4peynaiJmqiJmqiJmqiJmqiJmqiJmgqqpsxj8nLNNPHGGD3++OO69dZbdcMNN0iSDhw4IOl8g55ZcHCw9uzZk2UeUVFRGjZsmEaMGKE5c+aoQoUKqlOnTq7LfeWVVzR69Ogs8Q0bNlin4ZcoUUIVKlRQYmKiDh48aI0pU6aMypQpox07dlhfQEhS+fLlVbJkSW3dulXdqvpZ8fg9p3UgJV13VvKVp/u/nfOinad08pxxGitJc7aflJ+nQ+0r+Fqxc2lGn/96SsH+bmoa7mPFk8+k6+udp1UuyEMNSnlZ8QMn0hS/94yqF/fUDSU8rfiuo6lak3RW9UK8VL7Iv7vB1oPntPXgOd0W5q2QgPNvgnXr1jnVdOrUKWt81apVFRQUpA0bNji9KWrVqiUvLy+tW7fOqaZ69erp7Nmz2rx5sxVzd3dX/fr1lZycrO3bt1txX19f1a5dW4cOHdKuXbuseGBgoKpVq6b9+/frjz/+sOKXsp2oiZqoiZqoiZqoiZqoiZqoiZoKqqaIiAi5ymHy0/JfQYMGDdLChQu1fPlylSlTRpK0cuVKNW7cWPv371doaKg1tn///tq3b58WL14s6XzBQ4cO1eDBg1WmTBnFxsZq5MiR6tatmwYPHqygoCBNnDhRkZGRWZab3ZH4sLAwHT58WIULF5Z0ad+6NHp2phW365H4FS/35NsxaqImaqImaqImaqImaqImaqKmK1RTSkqKgoKClJycbPWhObkmjsQPGTJECxYs0Pfff2818JIUEhIi6fwR+cxN/N9//53l6Lx0fuPcd999GjlypH788UfNmzcvz2V7e3vL29s723ldeC19xoa4UMbOkl08NT1rPLtYfuMmn/GM5tzVeJr5/5lJTushp1pzuu9AfuIOhyPbeE7rPb/x3LaTqznmN05N1CRRU0455jdOTdQkUVNOOeY3Tk3UJFFTTjnmN05N1CRdek0OhyPbcdkp0BvbGWM0ePBgzZ07V8uWLVO5cuWcXi9XrpxCQkL07bffWrGzZ88qISFBjRo1ynaeUVFRSkhI0J133qkiRYpc0fwBAAAAALiaCvRI/KBBg/TJJ5/oiy++UKFChaxr4AMDA+Xr6yuHw6GhQ4dq7NixqlSpkipVqqSxY8fKz89PPXv2zHae1apV06FDh+Tn55ft6wAAAAAA2FWBNvFTpkyRJDVt2tQpHh0dbV2//tRTT+nUqVMaOHCgjh49qptvvllLlixRoUKFcpxvsWLFrlTKAAAAAAAUmGvmxnbXiuPHjyswMNClGwq44qYnc/55O7tYP65PQacAAAAAANet/PShBXpNPAAAAAAAcB1NPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE14FHQCAOzvpidnFHQKl2z9uD4FnQIAAACQJ47EAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATl9zEHz9+XPPnz9e2bdsuRz4AAAAAACAH+W7i7777br3zzjuSpFOnTqlevXq6++67VatWLX3++eeXPUEAAAAAAHBevpv477//Xrfddpskad68eTLG6NixY5o0aZJeeumly54gAAAAAAA4L99NfHJysooWLSpJWrx4sbp27So/Pz916NBBv/3222VPEAAAAAAAnJfvJj4sLEyrVq1SSkqKFi9erNatW0uSjh49Kh8fn8ueIAAAAAAAOM8jvxMMHTpUvXr1UkBAgMqWLaumTZtKOn+afc2aNS93fgAAAAAA4P/lu4kfOHCgGjRooH379qlVq1Zyczt/ML98+fJcEw8AAAAAwBWU7yZekurVq6datWopMTFRFSpUkIeHhzp06HC5cwMAAAAAAJnk+5r4kydPql+/fvLz81ONGjW0d+9eSdIjjzyiV1999bInCAAAAAAAzst3Ez98+HBt2rRJ8fHxTjeya9mypWJjYy9rcgAAAAAA4F/5Pp1+/vz5io2N1S233CKHw2HFq1evrp07d17W5AAAAAAAwL/yfST+4MGDKlmyZJZ4SkqKU1MPAAAAAAAur3w38fXr19fChQut5xmN+4cffqiGDRtevswAAAAAAICTfJ9O/8orr6ht27b65ZdflJqaqrfeeks///yzVq1apYSEhCuRIwAAAAAA0EUciW/UqJFWrFihkydPqkKFClqyZImCg4O1atUq3XTTTVciRwAAAAAAoIv8nfiaNWtq+vTplzsXAAAAAACQC5ea+OPHj7s8w8KFC190MgAAAAAAIGcuNfFBQUF53nneGCOHw6G0tLTLkhgAAAAAAHDmUhMfFxd3pfMAAAAAAAB5cKmJb9KkyRVZ+Pfff69x48Zp/fr1SkpK0rx589S5c2fr9cjIyCzX3t98881avXq19TwiIkJ79uzRp59+qnvuucdpbI0aNfTLL78oOjpakZGRV6QGAAAAAACulou6sZ0knTx5Unv37tXZs2ed4rVq1XJ5HikpKapdu7buv/9+de3aNdsxbdu2VXR0tPXcy8sry5iwsDBFR0c7NfGrV6/WgQMH5O/v73I+AAAAAABcy/LdxB88eFD333+/vv7662xfz8818e3atVO7du1yHePt7a2QkJBcx/Tq1UsTJkzQvn37FBYWJkmaNm2aevXqpRkzZricDwAAAAAA17J8N/FDhw7V0aNHtXr1ajVr1kzz5s3TX3/9pZdeeknjx4+/7AnGx8erZMmSCgoKUpMmTfTyyy+rZMmSTmOCg4PVpk0bTZ8+Xc8995xOnjyp2NhYJSQk5NnEnzlzRmfOnLGeZ9yJPzU1VampqZIkNzc3ubm5KT09Xenp6dbYjHhaWpqMMdnGPdz+XVZaumQkp5gkpf7/LPMTd0hyzxQ3RkozOcfdHOcfGdLN+UdOcXeHlHEvw9TU1BxrdXd3l8PhsNZV5riU9UudnOIeHh4yxjjFHQ6H3N3ds6z3nOKXsp2o6dJqkq7Mvif9+77JKX653k+Z1831up2oiZqoiZqoiZqoiZqo6dqsKfOYvOS7iV+2bJm++OIL1a9fX25ubgoPD1erVq1UuHBhvfLKK+rQoUN+Z5mjdu3aqXv37goPD1diYqKef/55NW/eXOvXr5e3t7fT2KioKA0bNkwjRozQnDlzVKFCBdWpUyfPZbzyyisaPXp0lviGDRusU/FLlCihChUqKDExUQcPHrTGlClTRmXKlNGOHTuUnJxsxcuXL6+SJUtq69at6lbVz4rH7zmtAynpurOSrzzd/+1GFu08pZPnjNNYSZqz/aT8PB1qX8HXip1LM/r811MK9ndT03AfK558Jl1f7zytckEealDq30sODpxIU/zeM6pe3FM3lPC04ruOpmpN0lnVC/FS+SL/7gZbD57T1oPndFuYt0ICzr8J1q1b51TTqVOnrPFVq1ZVUFCQNmzY4PSmqFWrlry8vLRu3TqnmurVq6ezZ89q8+bNVszd3V3169dXcnKytm/fbsV9fX1Vu3ZtHTp0SLt27bLigYGBqlatmvbv368//vjDil/KdqKmS6tJ0hXZ9yRpzf6z2nUsVa3L+yjQ+98O/HK/nzKvg+t1O1ETNVETNVETNVETNVHTtVlTRESEXOUw+Wn5df534Ddv3qyIiAhFRERo5syZaty4sRITE1WjRg2dPHkyP7P7NxGHI8uN7S6UlJSk8PBwzZo1S126dJF0vtihQ4dq8ODBKlOmjGJjYzVy5Eh169ZNgwcPVlBQkCZOnJjjje2yOxIfFhamw4cPW795fynfujR6dqYVt+uR+BUv9+TbMWrKtaYGz8y0/ZH41a/0dKrpetxO1ERN1ERN1ERN1ERN1HRt1pSSkqKgoCAlJydbfWhO8n0kvkqVKvr1118VERGhOnXq6P3331dERITee+89hYaG5nd2+RIaGqrw8HD99ttvWV7z8PDQfffdp5EjR+rHH3/UvHnzXJqnt7d3lqP6GfPz8HBePRkb4kIZO0t28dT0rPHsYvmNm3zGMxokV+Np5v9nJjmth5xqvXBdXUzc4XBkG89pvec3ntt2cjXH/Mb/SzVdiX3Plfjlej/9V7YTNVFTbnFqoiZqoqbc4tRETdR05WrKuETVFRd1TXxSUpIkaeTIkWrTpo1mzpwpLy8vxcTE5Hd2+XL48GHt27cvxy8LoqKi9MYbb6hHjx4qUqTIFc0FAAAAAICrLd9NfK9evay/69atq927d2v79u0qW7asihcvnq95nThxQr///rv1PDExURs3blTRokVVtGhRjRo1Sl27dlVoaKh2796tZ599VsWLF9ddd92V7fyqVaumQ4cOyc/PL9vXAQAAAACws4v+nfgMXl5eqly5sgICAvI97bp169SsWTPr+eOPPy5J6tu3r6ZMmaItW7ZoxowZOnbsmEJDQ9WsWTPFxsaqUKFCOc6zWLFi+S8CAAAAAAAbcLmJX7RokQ4fPqz77rvPir388ssaM2aMUlNT1bx5c8XGxubrNPamTZvmeiv9b775Js957N69O9fXjx075nI+AAAAAABcy7JekZ+DN954w/oNdUlauXKlXnjhBT3//POaPXu29u3bpzFjxlyRJAEAAAAAQD6a+K1bt6pRo0bW8zlz5qhVq1YaMWKEunTpovHjx+vLL7+8IkkCAAAAAIB8NPH//POP0/Xmy5cvV/Pmza3nNWrU0P79+y9vdgAAAAAAwOJyE1+qVClt27ZN0vm7ym/atEmNGze2Xj98+DB3hQcAAAAA4ApyuYnv1q2bhg4dqo8//lj9+/dXSEiIbrnlFuv1devWqUqVKlckSQAAAAAAkI+7048cOVL79+/XI488opCQEP3vf/+Tu7u79fqnn36qTp06XZEkAQAAAABAPpp4Pz8/ffzxxzm+HhcXd1kSAgAAAAAA2XP5dHoAAAAAAFCwaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZcujv9pEmTXJ7hI488ctHJAAAAAACAnLnUxE+YMMGlmTkcDpp4AAAAAACuEJea+MTExCudBwAAAAAAyAPXxAMAAAAAYBMuHYm/0B9//KEFCxZo7969Onv2rNNrb7755mVJDAAAAAAAOMt3E7906VLdcccdKleunH799VfdcMMN2r17t4wxuvHGG69EjgAAAAAAQBdxOv3w4cM1bNgwbd26VT4+Pvr888+1b98+NWnSRN27d78SOQIAAAAAAF1EE79t2zb17dtXkuTh4aFTp04pICBAL774ol577bXLniAAAAAAADgv3028v7+/zpw5I0kqVaqUdu7cab126NChy5cZAAAAAABwku9r4m+55RatWLFC1atXV4cOHTRs2DBt2bJFc+fO1S233HIlcgQAAAAAALqIJv7NN9/UiRMnJEmjRo3SiRMnFBsbq4oVK2rChAmXPUEAAAAAAHBevpv48uXLW3/7+flp8uTJlzUhAAAAAACQvXxfE1++fHkdPnw4S/zYsWNODT4AAAAAALi88t3E7969W2lpaVniZ86c0Z9//nlZkgIAAAAAAFm5fDr9ggULrL+/+eYbBQYGWs/T0tK0dOlSRUREXNbkAAAAAADAv1xu4jt37ixJcjgc1u/EZ/D09FRERITGjx9/WZMDAAAAAAD/crmJT09PlySVK1dOa9euVfHixa9YUgAAAAAAIKt8350+MTHxSuQBAAAAAADykO8b20lSQkKCOnXqpIoVK6pSpUq644479MMPP1zu3AAAAAAAQCb5buL/97//qWXLlvLz89MjjzyiwYMHy9fXVy1atNAnn3xyJXIEAAAAAAC6iNPpX375Zb3++ut67LHHrNijjz6qN998U2PGjFHPnj0va4IoeHtfrFnQKVwWZV/YUtApAAAAAMAlyfeR+F27dqlTp05Z4nfccQfXywMAAAAAcAXlu4kPCwvT0qVLs8SXLl2qsLCwy5IUAAAAAADIyuXT6aOiovTWW29p2LBheuSRR7Rx40Y1atRIDodDy5cvV0xMjN56660rmSsAAAAAAP9pLjfx06dP16uvvqoBAwYoJCRE48eP1+zZsyVJ1apVU2xsrO68884rligAAAAAAP91Ljfxxhjr77vuukt33XXXFUkIAAAAAABkL1/XxDscjiuVBwAAAAAAyEO+fmKucuXKeTbyR44cuaSEAAAAAABA9vLVxI8ePVqBgYFXKhcAAAAAAJCLfDXx99xzj0qWLHmlcgEAAAAAALlw+Zp4rocHAAAAAKBgudzEZ747PQAAAAAAuPpcPp0+PT39SuYBAAAAAADykK+fmAMAAAAAAAWHJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCYKtIn//vvv1alTJ5UqVUoOh0Pz5893et0Yo1GjRqlUqVLy9fVV06ZN9fPPPzuNiYiIkMPh0KxZs7LMv0aNGnI4HIqJibmCVQAAAAAAcHUUaBOfkpKi2rVr65133sn29ddff11vvvmm3nnnHa1du1YhISFq1aqV/vnnH6dxYWFhio6OdoqtXr1aBw4ckL+//xXLHwAAAACAq6lAm/h27drppZdeUpcuXbK8ZozRxIkTNWLECHXp0kU33HCDpk+frpMnT+qTTz5xGturVy8lJCRo3759VmzatGnq1auXPDw8rngdAAAAAABcDddsh5uYmKgDBw6odevWVszb21tNmjTRypUr9dBDD1nx4OBgtWnTRtOnT9dzzz2nkydPKjY2VgkJCZoxY0auyzlz5ozOnDljPT9+/LgkKTU1VampqZIkNzc3ubm5KT09Xenp6dbYjHhaWpqMMdnGPTJ9TZKWLhnJKSZJqf8/y/zEHZLcM8WNkdJMznE3x/lHhnRz/pFT3N0hOf4/nu7wlMOkyaF0pTs8dH4p5zlMqhwySnd4OuXoMKmSjEyW+DlJDhmH867nZs7JZIkbuZlUGbnJONxdiKfLzaQp3eGuzN9PZeSe23bKHHd3d5fD4bC2f+a4JKWlpbkU9/DwkDHGKe5wOOTu7p5lX8opfin73tWsSboy+5707/smp/jlej9lXjfX63aiJmqiJmqiJmqiJmqipmuzpsxj8nLNNvEHDhyQdL5Bzyw4OFh79uzJMj4qKkrDhg3TiBEjNGfOHFWoUEF16tTJczmvvPKKRo8enSW+YcMG61T8EiVKqEKFCkpMTNTBgwetMWXKlFGZMmW0Y8cOJScnW/Hy5curZMmS2rp1q7pV9bPi8XtO60BKuu6s5CtP93+7kUU7T+nkOeM0VpLmbD8pP0+H2lfwtWLn0ow+//WUgv3d1DTcx4onn0nX1ztPq1yQhxqU8rLiB06kKX7vGVUv7qkbSvzbUO86mqo1SWdVL8RL5Yv8uxtsPXhOWw+e021h3goJOP8mSHTvoRKHVqvwid/1Z6l2OusZaI0P/Wup/E4laU/ZLk6NfNifX8oj9aQSw3s41VRuT6xSPfy0r3QnK+Zmzqncnlid8g1RUnALK+51Lllhf36pfwLK62DxW6y436kkhf61VEeDbtDRoFpWvNCJ31Xy0GodKlZf/wRUtOJFjm1W0WObc91Op06dsuJVq1ZVUFCQNmzY4PRGr1Wrlry8vLRu3TqnmurVq6ezZ89q8+bNVszd3V3169dXcnKytm/fbsV9fX1Vu3ZtHTp0SLt27bLigYGBqlatmvbv368//vjDil/Kvnc1a5J0RfY9SVqz/6x2HUtV6/I+CvT+twO/3O+nzOvget1O1ERN1ERN1ERN1ERN1HRt1hQRESFXOUx+Wv4ryOFwaN68eercubMkaeXKlWrcuLH279+v0NBQa1z//v21b98+LV68WNL5YocOHarBgwerTJkyio2N1ciRI9WtWzcNHjxYQUFBmjhxoiIjI7NdbnZH4sPCwnT48GEVLlxY0qV969Lo2ZlW3K5H4mMDJl4XR+JLj9jIN35XqKYGz8y0/ZH41a/0dKrpetxO1ERN1ERN1ERN1ERN1HRt1pSSkqKgoCAlJydbfWhOrtkj8SEhIZLOH5HP3MT//fffWY7OS+c3zH333aeRI0fqxx9/1Lx581xajre3t7y9vbOd34XX02dsiAtl7CzZxVPTs8azi+U3bvIZz2iQXI2nmf+fmc432BncTGrWwReMycyRbdxkG3fkGE+Xw2QtKqe4m0mTlJYlntt2yk5O91PIT9zhcGQbz2lfym/8WqrpSux7rsQv1/vpv7KdqImacotTEzVREzXlFqcmaqKmK1dTxiWqrrhmfye+XLlyCgkJ0bfffmvFzp49q4SEBDVq1CjbaaKiopSQkKA777xTRYoUuVqpAgAAAABwVRTokfgTJ07o999/t54nJiZq48aNKlq0qMqWLauhQ4dq7NixqlSpkipVqqSxY8fKz89PPXv2zHZ+1apV06FDh+Tn55ft6wAAAAAA2FmBNvHr1q1Ts2bNrOePP/64JKlv376KiYnRU089pVOnTmngwIE6evSobr75Zi1ZskSFChXKcZ7FihW74nkDAAAAAFAQCrSJb9q0aa630nc4HBo1apRGjRqV45jdu3fnuoxjx45dXHIAAAAAAFxjrtlr4gEAAAAAgDOaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJj4JOALhaGr/duKBTuGQrhqwo6BQAAAAAFCCOxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEzTxAAAAAADYBE08AAAAAAA2QRMPAAAAAIBN0MQDAAAAAGATNPEAAAAAANgETTwAAAAAADZBEw8AAAAAgE3QxAMAAAAAYBM08QAAAAAA2ARNPAAAAAAANkETDwAAAACATdDEAwAAAABgEx4FnQAAXAv2vlizoFO4LMq+sKWgUwAAAMAVxJF4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGzCo6ATAADADm56ckZBp3DJ1o/rU9ApAACAS8SReAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABswqOgEwAAAMC14aYnZxR0Cpds/bg+BZ0CAFxRHIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGyCJh4AAAAAAJugiQcAAAAAwCZo4gEAAAAAsAmaeAAAAAAAbIImHgAAAAAAm6CJBwAAAADAJmjiAQAAAACwCZp4AAAAAABsgiYeAAAAAACboIkHAAAAAMAmPAo6AQDA5dP47cYFncIlWzFkRUGnAAAAcM3iSDwAAAAAADZBEw8AAAAAgE1wOj0AAACA/4ybnpxR0ClcsvXj+hR0CihAHIkHAAAAAMAmaOIBAAAAALAJmngAAAAAAGzimm7iR40aJYfD4fQICQmxXm/atKkcDodeffXVLNO2b99eDodDo0aNuooZAwAAAABw5VzTTbwk1ahRQ0lJSdZjy5YtTq+HhYUpOjraKbZ//34tW7ZMoaGhVzNVAAAAAACuqGu+iffw8FBISIj1KFGihNPrHTt21OHDh7VixQorFhMTo9atW6tkyZJXO10AAAAAAK6Ya/4n5n777TeVKlVK3t7euvnmmzV27FiVL1/eet3Ly0u9evVSdHS0GjduLOl8E//666+7dCr9mTNndObMGev58ePHJUmpqalKTU2VJLm5ucnNzU3p6elKT0+3xmbE09LSZIzJNu6R6WuStHTJSE4xSUr9/1nmJ+6Q5J4pboyUZnKOuznOPzKkm/OPnOLuDsnx//F0h6ccJk0OpSvd4aHzSznPYVLlkFG6w9MpR4dJlWRkssTPSXLIOJx3PTdzTiZL3MjNpOr/2rvz6CqqdO/jv5OTgYwEAgRkSJAxjAFiA4kIt2VwaBvaARRkeAXUpgFtHJBri4ix0aYVuTRK65XgAGovwSsq3YoDitAIRBDBJExBQIMIQmRMSM7z/pFOkUMSCBAIBd/PWlkr56ldVXvX2bVPPVV16pgCZB5vBeI+BVihfB6vSp6fKq67V155PMfrXmiFMlm58cAT6lhgRf3hdOIeeeQtUUczU6EKy40HKEABnuN195lPPvmc+Il98mR9r2Tc6y1qY/H8JeOSVFhYWKF4YGBgUV1LxIu33bnoe9Lx/aa8eGXtT/59uHL7Xun95tztT4GewHPS906Mn8v96cQ+JpXf97xeb6mxubz42YzlZub0HbeO5ZLk8/mqZIw4n+8TbTq7Nrl9LPcGyNk+F/P7RJvOrk2BAe4eywv/U5eL/X261NpUssypXNBJfOfOnfXKK6+oefPm+vHHH5Wamqrk5GRt2LBBMTExTrnhw4fryiuv1PTp05Wenq7c3Fxdf/31FUrip0yZoscee6xUfM2aNQoPD5ck1a5dW02aNFF2drZ++uknp0yDBg3UoEEDbdy4Ubm5uU788ssvV506dbR+/Xrd3DLMiS/57qh2HfKpb7NQBXmP74mLthzR4WPmV1aS3so8rLAgj65rEurEjhWa5mcdUWx4gHrEVXPiuXk+/XPLUTWODtSvLgt24rsOFmrJ9jy1qhWkNrWPJwBb9xVoZU6+kuoG6/Iax7vB+p+Oaf1Px9StYYjqRhTtBNneAaq9Z4WiDm7W95ddq/yg6k75ej9+rLAjOfqu0Y1+iUfD799VYMFhZccN8GtT4+/eVEFgmHbUv8GJBdgxNf7uTR0Jrauc2KudePCxXDX8/l0diLhcP9Xq4sTDjuSo3o8fa190G+2LbufEIw9uVp09K7Qn5godiGjqxGvsX6ea+9epa/WuqhN8/O6Mrw58pW1Ht+nXNX6tyMBIJ75s/zL9eOxHXVfzOgUGHN82i39erCO+I/ptrd/6tWnhnoUKDQhVr5q9nFiBr0AL9y5UnaA6SolOceIHCg5o8b7FiqsWp46RHZ347vzd+iL3C7UIa6GE8AQnvu3INn118CslRiQqPjReq1evllSxvnfkyBEn3rJlS0VHR2vNmjV+g1e7du0UHBzsLLdYUlKS8vPztW7dOifm9Xp1xRVXKDc3V5mZmU48NLSof56LvidJK3/I19b9Bep9eTVVDzn+SVjZ+1PJvlrZfe/HOt11OPT413vO5f50XaDOSd8rlnEoQxmHM87p/nTkyJEK97327dtrz5492rp1qxOvXr26EhIS9MMPP2jnzp3Ht/tZjOVHjhxx+pRbx3JJ2rNnT5WMEefzfaJNZ9cmt4/lPeKqOdvhYn6faNPZtenmlmGuHstX/pBfNP0if58utTbFx8erojx2Oil/FTt06JCaNGmiBx98UOPGjVOPHj2UmJioZ599Vp06ddLYsWP16aefqlatWvrrX/+qxMRE9evX76TJfFlX4hs2bKi9e/cqKipK0tmddUn+77lO3K1n/N6MePaiuBJ/e40arr8S//HdHxdtrwvsLOavHprr+qs3b0U9WyLq3ivxQ2tEuv5K/Bejv7ggz6CnPDyvqE0uHcsl6d9Tbr/kr3TQppO3KemBV1w9lnsDpGVPDPRr08X4PtGms2tTysPzXD2WF/qk1VOHXPTv06XWpkOHDik6Olq5ublOHlqeC/pK/InCw8PVtm1bbdq0qdS0O+64QzNnztS3336rlStXVniZISEhCgkJKRUPDAxUYOAJieZ/3ogTFXeWsuIFvtLxsmKnG7fTjBcPAhWNF9p/FqaiBLtYgBWULnxCmZI8ZcatzLin3LhPHivdqPLiAVYoqbBUvFCFTpsqEi8op62nEzfZacV98slXRpuK4yf2yZP1vbKcOP+ZxD0eT5nxc9H3KhKvrP2prD5cWX2v/P2m8ven4n5V2X3vROdyfyqvj5UXL29sPt34qfanE/uO28ZySU67q2KMOF/vU0XqeLrxS6lNbh/LC3yl23sxvk+06ezaVLL/uHEsL3axv0+nquPpxi/0NpW8CHIqF/yD7UrKy8tTRkZGmU+dHzhwoL755hu1adNGrVq1qoLaAQAAAABwbl3QV+Lvv/9+3XDDDWrUqJF2796t1NRU/fLLLxo6dGipsjVq1FBOTo6CgoLKWBIAAAAAAO53QSfxO3fu1G233aY9e/aodu3a6tKli1asWKG4uLgyy0dHR5/fCgIAAAAAcB5d0En8G2+8cdLpS5YsOen0tWvXVl5lAAAAAACoYq76TjwAAAAAAJcykngAAAAAAFyCJB4AAAAAAJcgiQcAAAAAwCVI4gEAAAAAcAmSeAAAAAAAXIIkHgAAAAAAlyCJBwAAAADAJUjiAQAAAABwCZJ4AAAAAABcgiQeAAAAAACXIIkHAAAAAMAlSOIBAAAAAHAJkngAAAAAAFyCJB4AAAAAAJcgiQcAAAAAwCVI4gEAAAAAcAmSeAAAAAAAXIIkHgAAAAAAlyCJBwAAAADAJUjiAQAAAABwCZJ4AAAAAABcgiQeAAAAAACXIIkHAAAAAMAlSOIBAAAAAHAJkngAAAAAAFyCJB4AAAAAAJcIrOoKAACA82P75LZVXYVK0WjiN1VdBQAAqgxX4gEAAAAAcAmSeAAAAAAAXIIkHgAAAAAAlyCJBwAAAADAJUjiAQAAAABwCZJ4AAAAAABcgiQeAAAAAACXIIkHAAAAAMAlSOIBAAAAAHCJwKquAAAAAFBZtk9uW9VVqBSNJn5T1VUAcIHiSjwAAAAAAC5BEg8AAAAAgEuQxAMAAAAA4BIk8QAAAAAAuARJPAAAAAAALkESDwAAAACAS/ATcwAAAADgIvyU4qWNK/EAAAAAALgESTwAAAAAAC5BEg8AAAAAgEuQxAMAAAAA4BI82A4AALhKyoyUqq7CWVs2ZllVVwEA4FJciQcAAAAAwCVI4gEAAAAAcAmSeAAAAAAAXIIkHgAAAAAAlyCJBwAAAADAJUjiAQAAAABwCX5iDgAAALjA8FOKAMrDlXgAAAAAAFyCJB4AAAAAAJcgiQcAAAAAwCVI4gEAAAAAcAkebAcAAAAAOO94gOOZ4Uo8AAAAAAAuQRIPAAAAAIBLkMQDAAAAAOASJPEAAAAAALgESTwAAAAAAC5BEg8AAAAAgEuQxAMAAAAA4BIk8QAAAAAAuARJPAAAAAAALkESDwAAAACAS5DEAwAAAADgEiTxAAAAAAC4BEk8AAAAAAAuQRIPAAAAAIBLkMQDAAAAAOASJPEAAAAAALgESTwAAAAAAC5BEg8AAAAAgEuQxAMAAAAA4BIk8QAAAAAAuARJPAAAAAAALkESDwAAAACAS5DEAwAAAADgEiTxAAAAAAC4BEk8AAAAAAAuQRIPAAAAAIBLkMQDAAAAAOASJPEAAAAAALgESTwAAAAAAC5BEg8AAAAAgEuQxAMAAAAA4BIk8QAAAAAAuARJPAAAAAAALkESDwAAAACAS7giiX/uuefUuHFjVatWTZ06ddLSpUudaT169JDH49GTTz5Zar7rrrtOHo9HkyZNOo+1BQAAAADg3Ljgk/g333xT9957rx5++GGtWbNG3bp107XXXqvt27c7ZRo2bKi0tDS/+X744Qd98sknqlev3vmuMgAAAAAA58QFn8Q/88wzGj58uEaMGKGEhAQ9++yzatiwoZ5//nmnzG9+8xvt3btXy5Ytc2Jz5sxR7969VadOnaqoNgAAAAAAlS6wqitwMvn5+UpPT9dDDz3kF+/du7eWL1/uvA4ODtagQYOUlpamlJQUSUVJ/F/+8pdT3kqfl5envLw853Vubq4k6eeff1ZBQYEkKSAgQAEBAfL5fPL5fE7Z4nhhYaHMrMy459gRJ17ok0xS4AmnTgr+s8jTiXskeUvEzaRCKz8e4Cn6K+azor/y4l6P5PlPPDcvQB4rlEc++TyBKlpLEY8VyCOTzxPkV0ePFUgyWan4MUkemce/6wXYMVmpuCnACmQKkHm8FYj7FGCF8nm8Knl+qrjudsTk8Ryve6EVymTyyltmPPCEOhZYUX84nbhHHnlL1NHMVKjCcuMBClCA53jdfeaTTz4n/vPPPxdtrwr0vZJxr7eojcV9umRckgoLCysUDwwMLKpribjH41Fh3pFz0vek4/tNefHK2p9yg0tOqNy+V3q/OYf709Fz0/dOjJe331TG/pSbm1vhvuf1ekuNzeXFz2YsNzNnPHfrWC5Jvxw114/lPk+gdPR4aTeO5VLRccaFNpZ7vV758o64eiz3BhQdt/yn9u4dyyUVHHH3WC7JOW4pdiGM5ZLkOXbE1WN5oU86EFTo/rFcHmc8d+tYLkn79++vlLH80KFDzvpP5YJO4vfs2aPCwkLFxsb6xWNjY7Vr1y6/2PDhw3XllVdq+vTpSk9PV25urq6//vpTJvFTpkzRY489VireuHHjs67/xaJdVVcAjpgHY6q6Chct+vmFI3p8dFVX4aLVtqorAAfj+bnDeH7hiBlPPz9X2lR1BeCo8WCNSl3egQMHVL169ZOWuaCT+GIlz8ZIRWcnToy1a9dOzZo101tvvaVPP/1UgwcPVlCQ/9mmskyYMEHjxo1zXvt8Pv3888+KiYkptQ6cG7/88osaNmyoHTt2KCoqqqqrA5wT9HNcCujnuBTQz3EpoJ+ff2amAwcO6LLLLjtl2Qs6ia9Vq5a8Xm+pq+67d+8udXVeku644w7NnDlT3377rVauXFmhdYSEhCgkJMQvFh0dfcZ1xpmLiopikMBFj36OSwH9HJcC+jkuBfTz8+tUV+CLXdAPtgsODlanTp20ePFiv/jixYuVnJxcqvzAgQP1zTffqE2bNmrVqtX5qiYAAAAAAOfFBX0lXpLGjRunwYMHKykpSV27dtULL7yg7du36+677y5VtkaNGsrJyanQbfQAAAAAALjNBZ/EDxgwQHv37tXkyZOVk5OjNm3aaNGiRYqLiyuzPLfCu09ISIgeffTRUl9rAC4m9HNcCujnuBTQz3EpoJ9f2DxWkWfYAwAAAACAKndBfyceAAAAAAAcRxIPAAAAAIBLkMQDAAAAAOASJPG4IMTHx+vZZ5+t6mrgEtCjRw/de++9FS6/ZMkSeTwe7d+//5zVCThbp9uvgUuNmenOO+9UzZo15fF4tHbt2qquEgCcMZJ4ADiJ5ORk5eTkqHr16lVdFQDAGfrXv/6lOXPm6L333nN+7QhAxU2aNEmJiYlVXQ38xwX/E3Nwj/z8fAUHB1d1NYBKFRwcrLp165Y7vbCwUB6PRwEBnBMFSuIzAReSLVu2qF69ekpOTi5zOv0VqBzHjh1TUFBQVVfjosdRJ8rVo0cPjR49WqNHj1Z0dLRiYmL0pz/9ScW/ShgfH6/U1FQNGzZM1atX18iRIyVJ8+fPV+vWrRUSEqL4+Hg9/fTTfsvdvXu3brjhBoWGhqpx48aaO3eu3/Rt27aVutVt//798ng8WrJkiRPbsGGDrr/+ekVFRSkyMlLdunXTli1bnOlpaWlKSEhQtWrV1LJlSz333HOVvIVwoTt06JCGDBmiiIgI1atXr1RflKTXXntNSUlJioyMVN26dTVw4EDt3r3bmX7i7fRz5sxRdHS03nvvPbVq1UohISFaunSpgoKCtGvXLr9l33fffbrqqqvKrV9ubq7uvPNO1alTR1FRUfr1r3+tr7/+2q9Mamqq6tSpo8jISI0YMUIPPfSQ35nwgoICjR071tlHx48fr6FDh6pfv35Ombfeektt27ZVaGioYmJi1LNnTx06dOg0tiTcoKCgoNzxOj8/Xw8++KDq16+v8PBwde7c2RlPc3NzFRoaqn/9619+y1uwYIHCw8N18OBBSdL333+vAQMGqEaNGoqJiVHfvn21bds2p/ywYcPUr18/TZkyRZdddpmaN28u6dT7mCQtXLhQzZo1U2hoqP7rv/5LL7/8cqmvsSxfvlxXXXWVQkND1bBhQ40dO9avHz/33HNq1qyZqlWrptjYWN18882VtWnhcsOGDdOYMWO0fft2eTwexcfHO8c448aNU61atdSrVy9J0meffaZf/epXCgkJUb169fTQQw+poKBA0vHjkxP/evTo4azrVP00Pj5ef/7zn3XHHXcoMjJSjRo10gsvvHBetwcuPqc6Zq/osc7HH3+spKQkhYWFKTk5WVlZWZKKjn0ee+wxff31106/nzNnjiTJ4/Fo1qxZ6tu3r8LDw5WamqqmTZvqr3/9q18d169fr4CAAL9jdZwFA8rRvXt3i4iIsHvuuccyMzPttddes7CwMHvhhRfMzCwuLs6ioqJs6tSptmnTJtu0aZOtXr3aAgICbPLkyZaVlWVpaWkWGhpqaWlpznKvvfZaa9OmjS1fvtxWr15tycnJFhoaatOmTTMzs+zsbJNka9ascebZt2+fSbJPP/3UzMx27txpNWvWtBtvvNFWrVplWVlZNnv2bMvMzDQzsxdeeMHq1atn8+fPt61bt9r8+fOtZs2aNmfOnPOx6XCB+P3vf28NGjSwDz/80NatW2e/+c1vnD5d7KWXXrJFixbZli1b7N///rd16dLFrr32Wmf6p59+apJs3759ZmaWlpZmQUFBlpycbMuWLbPMzEw7ePCgNW/e3P7yl7848x07dszq1Kljs2fPLrNuPp/PUlJS7IYbbrBVq1bZxo0b7b777rOYmBjbu3evmZm99tprVq1aNZs9e7ZlZWXZY489ZlFRUda+fXtnOampqVazZk1bsGCBZWRk2N13321RUVHWt29fMzP74YcfLDAw0J555hnLzs62devW2cyZM+3AgQOVs5FxQTjVeD1w4EBLTk62zz//3DZv3mxTp061kJAQ27hxo5mZ3XTTTXb77bf7LfOmm26y2267zczMDh06ZM2aNbM77rjD1q1bZ99++60NHDjQWrRoYXl5eWZmNnToUIuIiLDBgwfb+vXr7ZtvvjGzU+9j2dnZFhQUZPfff79lZmba66+/bvXr1/fb79atW2cRERE2bdo027hxoy1btsw6dOhgw4YNMzOzVatWmdfrtXnz5tm2bdvsq6++sunTp5+7DQ5X2b9/v02ePNkaNGhgOTk5tnv3bmefeeCBBywzM9MyMjJs586dFhYWZqNGjbKMjAx7++23rVatWvboo4+amVlBQYHl5OQ4f2vWrLGYmBh75JFHzOzU/dSs6NipZs2aNnPmTNu0aZNNmTLFAgICLCMjoyo2DS4Sp/oMqOixTufOnW3JkiW2YcMG69atmyUnJ5uZ2eHDh+2+++6z1q1bO/3/8OHDZmYmyerUqWMvvfSSbdmyxbZt22ZPPPGEtWrVyq+Of/zjH+2qq646T1vk4kcSj3J1797dEhISzOfzObHx48dbQkKCmRV9EPXr189vnoEDB1qvXr38Yg888ICzI2dlZZkkW7FihTM9IyPDJJ1WEj9hwgRr3Lix5efnl1n3hg0b2rx58/xijz/+uHXt2rXiGwCuduDAAQsODrY33njDie3du9dCQ0P9kvgTrVy50iQ5SW5ZSbwkW7t2rd98Tz31lLNvmJn93//9n0VERNjBgwfLXM/HH39sUVFRdvToUb94kyZN7O9//7uZmXXu3Nn+8Ic/+E1PSUnxS+JjY2Nt6tSpzuuCggJr1KiRk8Snp6ebJNu2bVu5bYb7nWy83rx5s3k8Hvv+++/95rn66qttwoQJZma2YMECi4iIsEOHDpmZWW5urlWrVs3ef/99Mys6AGzRooXf8vPy8iw0NNQ++OADMytK4mNjY52kvjwn7mPjx4+3Nm3a+JV5+OGH/fa7wYMH25133ulXZunSpRYQEGBHjhyx+fPnW1RUlP3yyy8V2l649EybNs3i4uKc1927d7fExES/Mv/93/9dqp/PnDnTIiIirLCw0K/skSNHrHPnzvab3/zGmXaqfmpWdOxU8oSZz+ezOnXq2PPPP18p7cSl6VTH7Ccq71jno48+csq8//77Jsnpu48++qjf8UcxSXbvvff6xX744Qfzer325ZdfmplZfn6+1a5dm4tplYjb6XFSXbp0kcfjcV537dpVmzZtUmFhoSQpKSnJr3xGRoZSUlL8YikpKc48GRkZCgwM9JuvZcuWio6OPq16rV27Vt26dSvzOzc//fSTduzYoeHDhysiIsL5S01N5RaeS8iWLVuUn5+vrl27OrGaNWuqRYsWfuXWrFmjvn37Ki4uTpGRkc5tkdu3by932cHBwWrXrp1fbNiwYdq8ebNWrFghSZo9e7b69++v8PDwMpeRnp6ugwcPKiYmxq+fZmdnO/00KytLv/rVr/zmK/k6NzdXP/74o1/M6/WqU6dOzuv27dvr6quvVtu2bXXLLbfoxRdf1L59+8ptG9yrvPF69erVMjM1b97cr6999tlnTl+7/vrrFRgYqIULF0oq+lpUZGSkevfuLamov27evFmRkZHO/DVr1tTRo0f9xtW2bduW+l7xqfaxrKwsXXHFFX7znNjv09PTNWfOHL/69+nTRz6fT9nZ2erVq5fi4uJ0+eWXa/DgwZo7d64OHz5cCVsVF7OyjmG6du3qtx+lpKTo4MGD2rlzp1/Z4cOH68CBA5o3b57zTJRT9dNiJT8/PB6P6tatW+orJsDpOtkxe0WPdUr2zXr16klShfrmiftSvXr1dP3112v27NmSpPfee09Hjx7VLbfcckZtQ2k82A5n5cQExcz8BpDi2In/n1impOIPw5LzHTt2zK9MaGhoufP7fD5J0osvvqjOnTv7TfN6veXOh4tLyf5TnkOHDql3797q3bu3XnvtNdWuXVvbt29Xnz59lJ+fX+58oaGhpfpwnTp1dMMNNygtLU2XX365Fi1a5PcMhxP5fD7Vq1evzDIlT2qdbH+qSBmv16vFixdr+fLl+vDDDzVjxgw9/PDD+vLLL9W4ceNy64eLi9frVXp6eqkxMCIiQlLRiambb75Z8+bN06233qp58+ZpwIABCgwsOkzw+Xzq1KlTqWeYSFLt2rWd/0/8TKjIPnaqz43i9d91110aO3ZsqfU3atRIwcHB+uqrr7RkyRJ9+OGHmjhxoiZNmqRVq1ad9kliXDpO5ximZDw1NVX/+te/tHLlSkVGRjrxU/XTYidegPB4PM6xC1DZjh49WuFjnZJ9s7jPV6RvlnXBYsSIERo8eLCmTZumtLQ0DRgwQGFhYWfZGhQjicdJFV9VLPm6WbNm5SbDrVq10hdffOEXW758uZo3by6v16uEhAQVFBRo9erVzpWWrKwsv4cXFR8Q5uTkqEOHDpJU6vdc27Vrp5dffrnMJ2DGxsaqfv362rp1qwYNGnTabcbFoWnTpgoKCtKKFSucg6d9+/Zp48aN6t69uyQpMzNTe/bs0ZNPPqmGDRtKklavXn3G6xwxYoRuvfVWNWjQQE2aNCl1V0pJHTt21K5duxQYGKj4+Pgyy7Ro0UIrV67U4MGDnVjJ+lWvXl2xsbFauXKlunXrJknOGfeSD7/zeDxKSUlRSkqKJk6cqLi4OL399tsaN27cGbcVF57yxusOHTqosLBQu3fvdvpJWQYNGqTevXtrw4YN+vTTT/X444870zp27Kg333zTeQhjRVVkH2vZsqUWLVrkFzuxTMeOHbVhwwY1bdq03HUFBgaqZ8+e6tmzpx599FFFR0frk08+0Y033ljh+uLS1qpVK82fP98vmV++fLkiIyNVv359SUV3qUyePFn//Oc/1aRJE7/5K9JPgXOlvM+AyjrWCQ4Odu7ErYjrrrtO4eHhev755/XPf/5Tn3/++WmvE+Xjdnqc1I4dOzRu3DhlZWXp9ddf14wZM3TPPfeUW/6+++7Txx9/rMcff1wbN27Uyy+/rL/97W+6//77JRUlJddcc41GjhypL7/8Uunp6RoxYoTflfXQ0FB16dJFTz75pL799lt9/vnn+tOf/uS3ntGjR+uXX37RrbfeqtWrV2vTpk169dVXnadoTpo0SVOmTNH06dO1ceNGffPNN0pLS9MzzzxzDrYSLkQREREaPny4HnjgAX388cdav369hg0b5vdTcMVX8GbMmKGtW7dq4cKFfonL6erTp4+qV6+u1NRU/b//9/9OWrZnz57q2rWr+vXrpw8++EDbtm3T8uXL9ac//cn5cB0zZoxeeuklvfzyy9q0aZNSU1O1bt06vytCY8aM0ZQpU/TOO+8oKytL99xzj/bt2+eU+fLLL/XnP/9Zq1ev1vbt27VgwQL99NNPSkhIOON24sJU3njdvHlzDRo0SEOGDNGCBQuUnZ2tVatW6amnnvJLnrt3767Y2FgNGjRI8fHx6tKlizNt0KBBqlWrlvr27aulS5cqOztbn332me65555StxmXVJF97K677lJmZqbGjx+vjRs36h//+IffU48lafz48fr3v/+tP/zhD1q7dq02bdqkhQsXasyYMZKKbtX8n//5H61du1bfffedXnnlFfl8vlJfnwFOZtSoUdqxY4fGjBmjzMxMvfPOO3r00Uc1btw4BQQEaP369RoyZIjGjx+v1q1ba9euXdq1a5d+/vlnSafup8C5VN5nQGUd68THxys7O1tr167Vnj17lJeXd9LyXq9Xw4YN04QJE9S0aVO/rzeiElTJN/HhCt27d7dRo0Y5T7uuUaOGPfTQQ85DM+Li4pyH0ZX01ltvWatWrSwoKMgaNWrk99AtM7OcnBy7/vrrLSQkxBo1amSvvPJKqWV9++231qVLFwsNDbXExET78MMP/R5sZ2b29ddfW+/evS0sLMwiIyOtW7dutmXLFmf63LlzLTEx0YKDg61GjRp21VVX2YIFCyp1G+HCduDAAbv99tstLCzMYmNj7S9/+Yt1797d78F28+bNs/j4eAsJCbGuXbvawoUL/R6sWNaD7apXr17uOh955BHzer32ww8/nLJ+v/zyi40ZM8Yuu+wyCwoKsoYNG9qgQYNs+/btTpnJkydbrVq1LCIiwu644w4bO3asdenSxZl+7NgxGz16tLOPjh8/3m655Ra79dZbzaxoX+rTp4/Vrl3bQkJCrHnz5jZjxoyKb0S4wqnG6/z8fJs4caLFx8dbUFCQ1a1b1373u9/ZunXr/JbzwAMPmCSbOHFiqXXk5OTYkCFDrFatWhYSEmKXX365jRw50nJzc82s6MF2xQ9ULOlU+5iZ2TvvvGNNmza1kJAQ69Gjhz3//PN+D1QyK3oQU69evSwiIsLCw8OtXbt29sQTT5hZ0cPDunfvbjVq1LDQ0FBr166dvfnmm2e7WXERKevBdmU95HTJkiV2xRVXWHBwsNWtW9fGjx9vx44dM7PjDzY98a979+7O/Cfrp2ZlHzu1b9/eeQI+cCZO9Rlwusc6ZmZr1qwxSZadnW1mZkePHrWbbrrJoqOjTZLzy1OS7O233y6zXlu2bDFJfr/eg8rhMavAF0dxSerRo4cSExP17LPPVnVVANcYOXKkfvzxR+cBYZWtV69eqlu3rl599dUyp/t8PiUkJKh///5ndVcBUJWeeOIJzZo1Szt27KjqqgDABe9CPWZftmyZevTooZ07dyo2Nraqq3NR4TvxAFAJcnNztWrVKs2dO1fvvPNOpSzz8OHDmjVrlvr06SOv16vXX39dH330kRYvXuyU+e677/Thhx+qe/fuysvL09/+9jdlZ2dr4MCBlVIH4Hx47rnndMUVVygmJkbLli3T1KlTNXr06KquFgDgDOTl5WnHjh165JFH1L9/fxL4c4AkHgAqQd++fbVy5Urddddd6tWrV6Us0+PxaNGiRUpNTVVeXp5atGih+fPnq2fPnk6ZgIAAzZkzR/fff7/MTG3atNFHH33Ed97hKsXPfPj555/VqFEj3XfffZowYUJVVwsAcAZef/11DR8+XImJieXeOYizw+30AAAAAAC4BE+nBwAAAADAJUjiAQAAAABwCZJ4AAAAAABcgiQeAAAAAACXIIkHAAAAAMAlSOIBAMBpmzRpkhITE6u6GgAAXHJI4gEAOEO7du3SPffco6ZNm6patWqKjY3VlVdeqVmzZunw4cNVXb1LSnx8vJ599tmqroZj2LBh6tevX1VXAwBwEQqs6goAAOBGW7duVUpKiqKjo/XnP/9Zbdu2VUFBgTZu3KjZs2frsssu029/+9sy5z127JiCgoLOc42l/Px8BQcHn/f1nksXY5sAADgZrsQDAHAGRo0apcDAQK1evVr9+/dXQkKC2rZtq5tuuknvv/++brjhBqesx+PRrFmz1LdvX4WHhys1NVWS9Pzzz6tJkyYKDg5WixYt9Oqrr/qtY//+/brzzjsVGxuratWqqU2bNnrvvfec6cuXL9dVV12l0NBQNWzYUGPHjtWhQ4ec6fHx8UpNTdWwYcNUvXp1jRw5Ur/+9a81evRov/Xs3btXISEh+uSTT8pt75NPPqnY2FhFRkZq+PDhOnr0aKkyaWlpSkhIULVq1dSyZUs999xzzrRt27bJ4/HojTfeUHJysqpVq6bWrVtryZIlTpnCwkINHz5cjRs3VmhoqFq0aKHp06f7raP4CveUKVN02WWXqXnz5urRo4e+++47/fGPf5TH45HH45EkzZkzR9HR0XrvvffUokULhYWF6eabb9ahQ4f08ssvKz4+XjVq1NCYMWNUWFjorCM/P18PPvig6tevr/DwcHXu3NmvnsXL/eCDD5SQkKCIiAhdc801ysnJkVT0VYOXX35Z77zzjlOfkvMDAHBWDAAAnJY9e/aYx+OxKVOmVKi8JKtTp4699NJLtmXLFtu2bZstWLDAgoKCbObMmZaVlWVPP/20eb1e++STT8zMrLCw0Lp06WKtW7e2Dz/80LZs2WLvvvuuLVq0yMzM1q1bZxERETZt2jTbuHGjLVu2zDp06GDDhg1z1hsXF2dRUVE2depU27Rpk23atMnmzp1rNWrUsKNHjzrlpk+fbvHx8ebz+cqs/5tvvmnBwcH24osvWmZmpj388MMWGRlp7du3d8q88MILVq9ePZs/f75t3brV5s+fbzVr1rQ5c+aYmVl2drZJsgYNGthbb71l3377rY0YMcIiIyNtz549ZmaWn59vEydOtJUrV9rWrVvttddes7CwMHvzzTed9QwdOtQiIiJs8ODBtn79evvmm29s79691qBBA5s8ebLl5ORYTk6OmZmlpaVZUFCQ9erVy7766iv77LPPLCYmxnr37m39+/e3DRs22LvvvmvBwcH2xhtvOOsYOHCgJScn2+eff26bN2+2qVOnWkhIiG3cuNFvuT179rRVq1ZZenq6JSQk2MCBA83M7MCBA9a/f3+75pprnPrk5eVVqK8AAHAqJPEAAJymFStWmCRbsGCBXzwmJsbCw8MtPDzcHnzwQScuye69916/ssnJyTZy5Ei/2C233GLXXXedmZl98MEHFhAQYFlZWWXWYfDgwXbnnXf6xZYuXWoBAQF25MgRMytK4vv16+dX5ujRo1azZk2/xDgxMdEmTZpUbnu7du1qd999t1+sc+fOfkl8w4YNbd68eX5lHn/8cevatauZHU/in3zySWf6sWPHrEGDBvbUU0+Vu+5Ro0bZTTfd5LweOnSoxcbGlkqK4+LibNq0aX6xtLQ0k2SbN292YnfddZeFhYXZgQMHnFifPn3srrvuMjOzzZs3m8fjse+//95vWVdffbVNmDCh3OXOnDnTYmNj/erZt2/fctsFAMCZ4nZ6AADOUPFt28VWrlyptWvXqnXr1srLy/OblpSU5Pc6IyNDKSkpfrGUlBRlZGRIktauXasGDRqoefPmZa47PT1dc+bMUUREhPPXp08f+Xw+ZWdnl7vekJAQ3X777Zo9e7aznq+//lrDhg0rt50ZGRnq2rWrX6zk659++kk7duzQ8OHD/eqTmpqqLVu2lDtfYGCgkpKSnDZL0qxZs5SUlKTatWsrIiJCL774orZv3+63jLZt21b4e/BhYWFq0qSJ8zo2Nlbx8fGKiIjwi+3evVuS9NVXX8nM1Lx5c7+2fPbZZ35tOXG59erVc5YBAMC5xIPtAAA4TU2bNpXH41FmZqZf/PLLL5ckhYaGlponPDy8VOzEkwBm5sTKWkZJPp9Pd911l8aOHVtqWqNGjU663hEjRigxMVE7d+7U7NmzdfXVVysuLu6k6ztVXSTpxRdfVOfOnf2meb3eU85f3OZ//OMf+uMf/6inn35aXbt2VWRkpKZOnaovv/zSr3xZbSrPiQ8Q9Hg8ZcaK2+Dz+eT1epWenl6q7iUT/7KWYWYVrhcAAGeKK/EAAJymmJgY9erVS3/729/8HiR3OhISEvTFF1/4xZYvX66EhARJUrt27bRz505t3LixzPk7duyoDRs2qGnTpqX+TnWVum3btkpKStKLL76oefPm6Y477jhlXVesWOEXK/k6NjZW9evX19atW0vVpXHjxuXOV1BQoPT0dLVs2VKStHTpUiUnJ2vUqFHq0KGDmjZtWupKfnmCg4P9Hk53pjp06KDCwkLt3r27VFvq1q1b4eVUVn0AADgRV+IBADgDzz33nFJSUpSUlKRJkyapXbt2CggI0KpVq5SZmalOnTqddP4HHnhA/fv3V8eOHXX11Vfr3Xff1YIFC/TRRx9Jkrp3766rrrpKN910k5555hk1bdpUmZmZ8ng8uuaaazR+/Hh16dJFf/jDHzRy5EiFh4crIyNDixcv1owZM05Z/xEjRmj06NEKCwvT7373u5OWveeeezR06FAlJSXpyiuv1Ny5c7VhwwbnzgOp6InsY8eOVVRUlK699lrl5eVp9erV2rdvn8aNG+eUmzlzppo1a6aEhARNmzZN+/btc04iNG3aVK+88oo++OADNW7cWK+++qpWrVpV6kRAWeLj4/X555/r1ltvVUhIiGrVqnXKecrSvHlzDRo0SEOGDNHTTz+tDh06aM+ePfrkk0/Utm1bXXfddRVaTnx8vD744ANlZWUpJiZG1atXr5KfFQQAXHy4Eg8AwBlo0qSJ1qxZo549e2rChAlq3769kpKSNGPGDN1///16/PHHTzp/v379NH36dE2dOlWtW7fW3//+d6WlpalHjx5Omfnz5+uKK67QbbfdplatWunBBx90ru62a9dOn332mTZt2qRu3bqpQ4cOeuSRR1SvXr0K1f+2225TYGCgBg4cqGrVqp207IABAzRx4kSNHz9enTp10nfffaff//73fmVGjBih//3f/9WcOXPUtm1bde/eXXPmzCmVgD/55JN66qmn1L59ey1dulTvvPOOk3DffffduvHGGzVgwAB17txZe/fu1ahRoyrUnsmTJ2vbtm1q0qSJateuXaF5ypOWlqYhQ4bovvvuU4sWLfTb3/5WX375pRo2bFjhZYwcOVItWrRwvt+/bNmys6oTAADFPMYXuAAAuOTs2LFD8fHxWrVqlTp27HjO17dt2zY1btxYa9asUWJi4jlfHwAAFytupwcA4BJy7Ngx5eTk6KGHHlKXLl3OSwIPAAAqD7fTAwBwCVm2bJni4uKUnp6uWbNmVXV1AADAaeJ2egAAAAAAXIIr8QAAAAAAuARJPAAAAAAALkESDwAAAACAS5DEAwAAAADgEiTxAAAAAAC4BEk8AAAAAAAuQRIPAAAAAIBLkMQDAAAAAOAS/x9w/8ACqOxToAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot using seaborn\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(x='department_name', y='prices', hue='customer_profile', data=top_5_per_profile)\n",
    "plt.title('Top 5 Grocery departments sales per Customer Profile')\n",
    "plt.xlabel('Grocery department')\n",
    "plt.ylabel('Total Sales')\n",
    "\n",
    "# Access the current axis (ax) after creating the plot\n",
    "ax = plt.gca()\n",
    "\n",
    "# Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6))))\n",
    "\n",
    "# Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Modify legend title\n",
    "plt.legend(title='Top 3 customer profiles')\n",
    "\n",
    "# Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_profiles_departments.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "# Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce20363",
   "metadata": {},
   "source": [
    "Our top three customer profiles, identified by their overall frequency, regional frequency, and total sales, exhibit similar spending habits across various grocery departments. Predominantly, they allocate their expenditures to the following grocery categories: produce, dairy-eggs, beverages, frozen, and pantry. Notably, the customer profile \"Adult with kids and middle income\" stands out for its substantial spending on products from the produce department."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f59c5fa",
   "metadata": {},
   "source": [
    "# 7. Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf01c0c",
   "metadata": {},
   "source": [
    "In summary, our top three middle-income customer profiles, encompassing adults with and without kids, and retirees with kids, reveal the standout influence of the \"Adult with kids and middle income\" category, surpassing 10 million occurrences and contributing substantially to our $163 million in sales. Despite a slightly less frequent ordering pattern, these profiles exhibit prevalence nationwide, with the Southern states leading in customer counts.\n",
    "\n",
    "In a targeted marketing campaign aimed at amplifying engagement with our key customer base, prioritizing the \"Adult with kids and middle income\" group is essential. Tailoring messages and promotions specifically to meet their preferences could further enhance our market share.\n",
    "\n",
    "Examining spending habits, our top three profiles consistently allocate expenditures across key grocery departments—produce, dairy-eggs, beverages, frozen, and pantry. Particularly noteworthy is the robust spending on produce items by the \"Adult with kids and middle income\" profile. Therefore, a focused approach to promoting and placing products from these departments in marketing materials and the Instacart platform is advised. Special deals, discounts, or loyalty programs for produce items may prove highly effective.\n",
    "\n",
    "In conclusion, our top three customer profiles not only shape our demographic landscape but also wield a significant influence on sales and ordering patterns, emphasizing their enduring impact on our business metrics. Leveraging our defined profiles allows for crafting personalized and targeted messaging, showcasing different aspects of Instacart's service that resonate with each group based on their unique needs and preferences."
   ]
  }
 ],
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   "file_extension": ".py",
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