Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Unnamed: 0</th>\n",
       "      <th>Purchase Date</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Category</th>\n",
       "      <th>Status</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>₹14,999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>₹14,999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>Amish's Ramachandra Series - Ram, Sita &amp;...</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 10</td>\n",
       "      <td>₹369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>The Richest Man In Babylon</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>₹175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>HOW TO TALK TO ANYONE</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>₹184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Purchase Date                                       Titles  \\\n",
       "0           0  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "1           1  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "2           2  Sat, 29th Aug  Amish's Ramachandra Series - Ram, Sita &...   \n",
       "3           3  Sat, 29th Aug                   The Richest Man In Babylon   \n",
       "4           4  Sat, 29th Aug                        HOW TO TALK TO ANYONE   \n",
       "\n",
       "                Category                                             Status  \\\n",
       "0  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "1  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "2                  Books                                Delivered on Sep 10   \n",
       "3                  Books                                Delivered on Sep 09   \n",
       "4                  Books                                Delivered on Sep 09   \n",
       "\n",
       "    Prices  \n",
       "0  ₹14,999  \n",
       "1  ₹14,999  \n",
       "2     ₹369  \n",
       "3     ₹175  \n",
       "4     ₹184  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Orders_Details.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('Unnamed: 0', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28 entries, 0 to 27\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Purchase Date  28 non-null     object\n",
      " 1   Titles         28 non-null     object\n",
      " 2   Category       26 non-null     object\n",
      " 3   Status         28 non-null     object\n",
      " 4   Prices         28 non-null     object\n",
      "dtypes: object(5)\n",
      "memory usage: 1.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Purchase Date    0\n",
       "Titles           0\n",
       "Category         2\n",
       "Status           0\n",
       "Prices           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "#Number of missing values. Since, the data set is very small and missing values are even smaller,\n",
    "#let's directly look at the missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Purchase Date</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Category</th>\n",
       "      <th>Status</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Wed, 16th May</td>\n",
       "      <td>I Am Malala</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delivered on May 20, 2018</td>\n",
       "      <td>₹203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fri, 20th Apr</td>\n",
       "      <td>The Secret Of The Nagas (English, Paperb...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delivered on Apr 22, 2018</td>\n",
       "      <td>₹220</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Purchase Date                                       Titles Category  \\\n",
       "25  Wed, 16th May                                  I Am Malala      NaN   \n",
       "27  Fri, 20th Apr  The Secret Of The Nagas (English, Paperb...      NaN   \n",
       "\n",
       "                       Status Prices  \n",
       "25  Delivered on May 20, 2018   ₹203  \n",
       "27  Delivered on Apr 22, 2018   ₹220  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Category'].isnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Books                                11\n",
       "Mobiles & Accessories                 6\n",
       "Bags, Wallets & Belts                 2\n",
       "Footwear                              1\n",
       "Health & Personal Care Appliances     1\n",
       "Computers                             1\n",
       "Home Entertainment                    1\n",
       "Wearable Smart Devices                1\n",
       "Beauty and Grooming                   1\n",
       "Audio & Video                         1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Category'].value_counts()\n",
    "\n",
    "#Since, majority of the orders are books, we assume that the missing data\n",
    "#most probably belons to a book category.\n",
    "#Also, this can be manually checked since the dataset is very small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Category'].fillna(value='Books', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Purchase Date    0\n",
       "Titles           0\n",
       "Category         0\n",
       "Status           0\n",
       "Prices           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "#The missing values are dealt with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Books                                13\n",
       "Mobiles & Accessories                 6\n",
       "Bags, Wallets & Belts                 2\n",
       "Footwear                              1\n",
       "Health & Personal Care Appliances     1\n",
       "Computers                             1\n",
       "Home Entertainment                    1\n",
       "Wearable Smart Devices                1\n",
       "Beauty and Grooming                   1\n",
       "Audio & Video                         1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category = df['Category'].value_counts()\n",
    "category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Category vs Number of Orders placed per Category')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize = (12,7))\n",
    "plt.barh(category.index, category.values, tick_label=category.index)\n",
    "plt.legend(labels = ['Total Orders'])\n",
    "plt.xlabel(\"Number of Orders\")\n",
    "plt.title('Category vs Number of Orders placed per Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.Prices[0]) \n",
    "#The data type of Prices is string. We need to convert it. \n",
    "#Before conversion we need to clear any kind of signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing Regular Expressions to clean data\n",
    "import re\n",
    "new = []\n",
    "for price in df.Prices:\n",
    "    if '+' in price:\n",
    "        price = price.split('+')[0]\n",
    "    new.append(re.findall('[0-9]+', price))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_prices =[]\n",
    "for n in new:\n",
    "    p = ''\n",
    "    for n_char in n:\n",
    "        p+=n_char\n",
    "    new_prices.append(int(p))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns='Prices', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Purchase Date</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Category</th>\n",
       "      <th>Status</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>Amish's Ramachandra Series - Ram, Sita &amp;...</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 10</td>\n",
       "      <td>369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>The Richest Man In Babylon</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>HOW TO TALK TO ANYONE</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Purchase Date                                       Titles  \\\n",
       "0  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "1  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "2  Sat, 29th Aug  Amish's Ramachandra Series - Ram, Sita &...   \n",
       "3  Sat, 29th Aug                   The Richest Man In Babylon   \n",
       "4  Sat, 29th Aug                        HOW TO TALK TO ANYONE   \n",
       "\n",
       "                Category                                             Status  \\\n",
       "0  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "1  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "2                  Books                                Delivered on Sep 10   \n",
       "3                  Books                                Delivered on Sep 09   \n",
       "4                  Books                                Delivered on Sep 09   \n",
       "\n",
       "   Prices  \n",
       "0   14999  \n",
       "1   14999  \n",
       "2     369  \n",
       "3     175  \n",
       "4     184  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Prices'] = new_prices\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_expenditure = df.pivot_table(values=\"Prices\", index=\"Category\", aggfunc='sum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "category_expenditure.sort_values(by='Prices', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Category vs Total Expenditure per Category')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sH+D/d+68+hYZGcmIESMoWrRoriV3v9W8eXNGjx6Ng4MDY8aM4dNPP6VDhw5UqFCBxo0bc+7cuTz3Gzp0KNOnT6dLly7WG0aMGTMmz7HlypWjX79+XLt2jaZNm1r7HB0dTVhYGJs3b8ZsNtOlSxe8vb3/sL9ubm7MmzePt99+m4iICHJycnjooYdYsGABtWrVwtnZmR07duDt7U1OTg7PPPMM165dIz09nTp16mBnZ0ePHj1YvXo1c+bMYerUqZjNZgzDYObMmVSsWJFu3bpx4cIFunTpQsmSJalUqZL1SmVUVBRhYWGsXbsWi8XCiy++SJcuXfKtu2vXruzcufMfcydLkQeZybjbtQMiIiJ/0vTp0+nbty8uLi4kJSXRuXNnduzYccfvHf2Rr7/+mpCQELZs2ZJrCd2/0e/9btSDLDo6mvT0dCZMmFDYpfwpe/fu5caNG3Tq1AmASZMmUapUKevSSVtZLBYGDRpE9+7dad++fUGUKiJ/gq48iYjI365y5coEBQXh4OCAYRhMnjz5T3/gHzt2LIcPHyY6OvpfH5zk/lOnTh3GjRvHokWLrFdeR44c+afmOHnyJAEBAbRv357nn3++gCoVkT9DV55ERERERERsoBtGiIiIiIiI2EDhSURERERExAYKTyIiIiIiIjbQDSPkgXH06FH9OGAByczMzPUjmXJvqK8FQ30tOOptwVBfC456WzDU19s9eOKJJ+7YrvAkDwyTyYS7u3thl/GPFB8fr94WAPW1YKivBUe9LRjqa8FRbwuG+nq7B3nRsj0REREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbmAzDMAq7CBFbnPj+e+o99lhhlyEiIiIiBS0jA4oVK7TDx8fH4+7ufsd2h0KoReQvMdnbg8lU2GWIiIiISEG7T6/vaNmeiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERvohhEiIiIiInJf2bBhAxs3bgQgMzOT+Ph4lixZwvTp0zGZTHh6ejJkyBD27t3LokWLADAMgyNHjrBlyxbMZjMTJ07E3t6eGjVqMGXKFE6dOkVERIT1GN988w3vvvsunp6eNtelW5XLAyM+Ph73evUKuwwRERERKWi/iSiTJk2ibt26rF69mtmzZ1O1alUCAgIYN24c9X7z2fC9997j5s2bjBgxgsGDB+Pr60vr1q0ZOXIkHTt2pG3bttaxW7duZceOHcyaNSvPw+d3q/J/9bK9Q4cO8eijj/Lpp5/m2t6pUyeCg4Pz3W/Dhg3MnDnzju3Dhw/HbDYTHBzM3r17/3Jdt27dYuDAgfj4+DBhwoTfHTtw4EAGDhz4l49VkOLj43nnnXcKuwwREREReUB99913/PDDD/To0YM1a9ZQtWpV0tLSSE1NpUyZMtZx//3vf/n4448ZMmQIAO7u7ly/fh3DMEhLS8PB4f8vuEtPTycmJuYPP2fn5V8dngDc3NzYsmWL9fGpU6e4devWX5orOjqaIkWK3HVNhw8fpkiRImzcuJEDBw5w7dq1PMclJSWRnp7OjRs3uHTp0l0f915zd3e3voFFRERERP6sBQsWMHjwYAAcHBz45ptv6NSpEy4uLpQrV846bsmSJQQFBVk/i/+6VM/Ly4uff/6Zpk2bWseuW7eODh065NrfVv/68FS3bl2SkpK4efMmAJs2baJTp07W5zdt2sRLL71Ez549GTduHBaLBbi9RjIwMJCXXnqJPXv2ANC2bVsyMzOt+1osFsaPH0+vXr3o2bMnhw4dAm6HrB49etC9e3c++OCDO2p68sknOXXqFKGhobRq1YqyZcvmWfu6det49tln6dKlCytWrLBuX7t2LV27dqVLly7ExMTku23r1q306NGDnj17Wq+kHTlyBF9fX/z9/Rk4cCCpqamcP38ePz8/evfuTWBgIMnJyQBMmzaN7t270717d5YuXQpAcHAwAwcOxM/Pj+3btzN8+PA/dSwREREREYCbN29y7tw5mjVrZt32xBNPsGvXLurVq8fChQsByMnJYc+ePXTs2NE6bsqUKSxfvpxt27bRpUsXpk2bZn1u8+bNdO/e/S/VpBtGAM899xzbt2+na9euHDt2jP79+5OUlMS1a9eIiYlh48aNODk5ERERwerVqylRogTFixdn4cKFpKSk0L179zy/aLZ27VrKli1LREQE165do3fv3nzyySd89NFHLFu2DFdXVzZs2HDHfnv27KFChQrs2LGD2NhYLly4QMWKFSlWrJh1TE5ODlu2bGH16tU4ODjQsWNHXn/9ddLS0li0aBGbNm2iSJEiTJs2jcTExDy3xcTEsH79eooXL87o0aP58ssv2bdvH8899xx9+/Zl165d3Lx5k/379+Ph4UFwcDBfffUVN27c4MSJEyQkJLBmzRqysrLw9/e3vrGbNWtGUFCQNSxev37d5mM5OTkV0KssIiIiIg+SjRs3UrduXeLj4zEMg/HjxzNhwgScnJxIT0/HbDYTHx/PhQsXKF++POfPn7fuW7x4cZKSksjKysJsNnPp0iXi4+NJS0vj5s2bXL9+nevXr//pmhSeuP0dp9DQUKpWrUrjxo2t2y9dukTt2rWtH+ibNGnCvn37aNCgAY0aNcJkMvHQQw/h7OycZ/NPnz7NkSNHOHbsGABZWVlcu3aNqKgooqKiuHr1Kq1atcq1z08//cScOXPYsmULhw4d4rXXXsNisbBs2TIqVqxoHffFF1+QlpbGyJEjgdthavPmzdSpU4c6depYg9b48eP55ptv7th27NgxUlJSGDBgAABpaWlcunSJgQMHMn/+fAIDA3F1daV+/fp069aNRYsW0a9fP5ydnRk+fDhnz56lcePGmEwmHB0dadCgAWfPngWgZs2auc7pxx9/tPlYIiIiIiJwexVX/fr1rTduGDJkCDNmzKBIkSKUL1+eyZMnU7JkSS5cuIC7u3uuGzxERkYyc+ZMHBwccHR0JDw8nCpVqnDs2DFq1aqV580gfis+Pj7P7QpPQNWqVUlPTyc2NpYRI0ZYvz9UpUoVzp49S3p6OiVKlODw4cPWYPDdd98BcOXKFdLT0/NcWufm5kbFihUZOHAgGRkZzJs3j5IlS7Jt2zaioqIwDIOOHTvSsWNHKleuDNwOWHZ2dtjZ2eHp6cmHH37IhQsXKF68eK65161bx+TJk3nmmWeA20vgJk+ezOLFizl37hxms5kiRYowbNgwxo4dm+e2hx9+mPfffx9HR0c2bNiAu7s7mzdvxsfHh7Fjx7JgwQLWrFmDm5sbjRo1YsiQIWzZsoX33nuP559/ng0bNhAUFITFYuHrr7/Gx8cHAJPJlKvWKlWq2HwsfUdKRERERAD69euX63G7du1o167dHeO8vLzw8vLKta1x48asWrXqjrH169dn7ty5f7kmhaf/4+3tzccff0zNmjWt4alcuXIMHTqUl19+GTs7O6pVq8aoUaP45JNPyMjI4OWXXyY9PZ2wsLA7AgOAn58fISEh9O7dm9TUVPz9/SlSpAilS5emc+fOlC5dmhYtWlCpUiXrPtWrV6dXr174+/tjZ2dH8+bNady4MQsWLGDMmDEA/Pzzz3z77bdER0db92vUqBGZmZlcuHCB/v3707t3b0wmE23atKFy5cp5bgsKCiIgIIDs7GwqV66Ml5eX9W6BJUqUwNHRkbCwMAzDYPTo0cTExGBnZ8e4cePw8PDg8OHD9OjRA4vFQocOHfDw8Mizt+XKlbP5WCIiIiIi9yv9zpM8MPQ7TyIiIiL/EoUcUfQ7TyIiIiIiIndB4UlERERERMQGCk8iIiIiIiI2UHgSERERERGxgcKTiIiIiIiIDRSeREREREREbKDfeZIHhpGdXei3rRQRERGRv0FGBhQrVthV3EFXnuSBkWmxFHYJ/1jx8fGFXcI/kvpaMNTXgqPeFgz1teCotwXjvujrfRicQOFJRERERETEJgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCkzwwijo6FnYJ/1ju7u6FXcI/kvpqg4yMwq5ARETEZg6FXYCIrUz29mAyFXYZInIvGUZhVyAiImIzXXkSERERERGxgcKTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERvobnsiIlKoLBYL48eP56effsJsNjNo0CAqVarExIkTsbe3p0aNGkyZMgU7u9v/3peTk0O/fv149tln6dmzJ9evX2f06NGkpqZSpkwZJk+ezEMPPcRHH33E4sWLcXZ2xsfHh+7duxfymYqIyINOV57+wKFDhxg+fHiubTNnzmTDhg0Fety2bdvSq1cvAgICrP99//33+Y4/deoUcXFxNs+/fft2kpOT831+w4YN7Ny580/VfDdWr16NxWL5244nIvePTZs2UaZMGVasWMGiRYsIDw/nnXfeYfDgwaxcuRKz2cyePXus45cvX86NGzesjxcsWECjRo1YuXIlAQEBREVFkZKSwuzZs4mNjWXZsmVs3ryZhISEQjg7ERH5J9GVp/vY+++/T9GiRW0a+9lnn+Hi4kKTJk1sGv/hhx8SGhqKq6trns937drV5jrvhQULFtClS5e/9Zgicn/o0KED7du3tz62t7fH3d2d69evYxgGaWlpODjc/utq27Zt2NnZ4enpaR3/ww8/WP+Rq2HDhoSFhZGQkEDdunUpU6YMAI8//jjffvstVapU+RvPTERE/mkUnu7StGnTOHLkCAAvvPACgYGBBAcH4+DgQGJiImazGW9vb3bv3k1SUhJz586lWrVqzJo1i7i4OAzDICgoCC8vL5uOt2HDBj7//HMyMjL48ccf6d+/Py1atGDjxo04Ojri4eFBRkYG0dHR2NvbU7VqVcLCwti8eTPr168nJyeHV199lfj4eMaOHcuKFSuIiYnh+++/Jy0tjVq1ajF16lRiYmJwcXHBzc2NRYsW4ejoSEJCAt7e3gwaNOgvn2NAQAB169blzJkzpKamMnv2bPbv38+VK1cYPnw4c+fOLciXS0TuQyVLlgQgNTWVYcOG8cYbb2AymQgLC2PevHk4OzvTtGlTTp8+zZYtWxg0aBC7du2y7u/u7s6uXbuoV68eu3btIiMjg+rVq/PDDz9w9epVSpYsyYEDB6hRo0YhnaGIiPxTKDzZ4ODBgwQEBFgfX7p0iWHDhrF7924SEhJYs2YNWVlZ+Pv706xZMwAqV67M5MmTeeutt0hISGDRokXMmTOHXbt2UbNmTRISEli1ahWZmZn4+vrSokULSpUqleu4r7zyinWNv52dHUuXLgVuf8BYvHgxFy5cYODAgXTt2hUfHx9cXFx4/PHH6dChAytWrOChhx7i7bffZuPGjTg4OFCqVCnmzZsH3P6wERoaitlsplSpUixZsoScnBw6dux4x3K+xMRENm3ahNlsplWrVgwaNOgvnyNA/fr1mTBhAtHR0XzyyScMGDCAefPmER0dXQCvnojc7+Lj47ly5QrTpk3Dy8uL2rVrExgYSHh4ONWqVePTTz9l7NixFC1alAsXLjBhwgSuXr2Ko6MjOTk5PPPMMyxatIju3bvTsGFDSpcuTWJiIr1796Zfv3489NBDVKpUibS0NOLj4wv7dO9rGRkZ6lEBUF8LjnpbMNTX/Ck82aBZs2a5PtjPnDkTgLNnz9K4cWNMJhOOjo40aNCAs2fPAlCvXj0ASpUqhZubm/XPZrOZ06dPc/z4cWsgy8rKIjEx8Y7wlN+yvbp16wLw8MMPYzabcz2XkpLC5cuXeeONN4Dbb/4WLVpQrVo1atasecdcRYsWJSUlhREjRlCiRAnS09Pv+O7RI488goODAw4ODhQrVsy6/a+c42/3q1ixIlevXr2jJhH5dylfvjwjRozgrVKOUT4AACAASURBVLfeonnz5gCUK1eO+vXr8/DDD5OQkEBiYiLTp08HboetHTt24OLiQs+ePdmzZw+BgYE0a9aM//znP9jb21OnTh22bdvGhg0byMrKok+fPrz44ouUK1euME/1vhcfH4+7u3thl/GPo74WHPW2YKiv5BseFZ7uQq1atdiwYQNBQUFYLBa+/vprfHx8ADCZTPnu5+bmRtOmTQkPDycnJ4e5c+f+qXX4ec1tMpnIycmhbNmyVKxYkblz5+Ls7MzOnTspUaIESUlJ1qtYv443DIO9e/eSlJTE22+/TUpKCtu3b8cwjD883r0+x1/rF5F/n/nz53Pz5k3mzp1rXbo7efJkhg8fjoODA46OjoSHh+e7f82aNRk7diwAFSpUICIiwrpf165dKVq0KH369FFwEhGRu6bwdBfatGnD4cOH6dGjBxaLhQ4dOuDh4fGH+7Vt25bDhw/j7+9Peno67dq1w8nJ6Y5xv122B/Dyyy/nO+djjz1GZGQktWrVYsKECQwYMADDMChZsiSRkZEkJSXlGv/kk08yZswY5s2bx9y5c/H19aVIkSJUrVqVy5cv/4ku3N05/qpx48YMGDCADz/88HdDmYj884SEhBASEnLH9lWrVuW7z9ChQ61/rl69ep5jhwwZwpAhQ+5NkSIiIoDJ+N/LDCL3qfj4eNz/b8mfiPxD/IW/grScpOCotwVDfS046m3BUF/z74F+50lERERERMQGCk8iIiIiIiI2UHgSERERERGxgcKTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgP9zpM8MIzs7L90W2MRuY9lZECxYoVdhYiIiE105UkeGJkWS2GX8I8VHx9f2CX8I6mvNlBwEhGRB4jCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDzJA6Ooo2PBTJyRUTDzioiIiMg/ikNhFyBiK5O9PZhM935iw7j3c4qIiIjIP46uPImIiIiIiNhA4UlERERERMQGCk8iIiIiIiI2UHgSERERERGxgW4YIf96FouF8ePH89NPP2E2mxk0aBAVK1Zk4MCB1KhRA4CePXvi7e1NdHQ0+/fvx2QyERISQv369UlJSWHUqFFkZGRQoUIFpk6dSvHixQv3pERERETknlN4kj906NAh3njjDWrXro1hGGRlZTFlyhRq1apl8xwxMTG4uLjQs2fPAqz0r9m0aRNlypRhxowZXLt2DR8fHwYPHkyfPn145ZVXrONOnDjBN998w5o1a/jpp5947bXX2LRpE3PnzuWFF16ga9euLFy4kNWrVxMUFFR4JyQiIiIiBULL9sQmzZo1IzY2lmXLljFkyBAiIyMLu6R7pkOHDrz++uvWx/b29nz//ffs2bOHXr16MX78eFJTU6lXrx6LFy/GZDKRmJiIi4sLAEeOHKFVq1YAeHp6sn///kI5DxEREREpWApP8qfdvHmTypUrc+LECXr27Env3r3p27cviYmJALz//vu89NJL9OjRgxkzZuTa9+LFi7z00kucPHmSI0eO4Ovri7+/PwMHDiQ1NbUwToeSJUvi5OREamoqw4YN44033qB+/fqMGTOG5cuXU7VqVd59910AHBwciI6O5tVXX+WFF14AIDU1FWdnZ+tcv/zyS6Gch4iIiIgULC3bE5scPHiQgIAAzGYzp06dYsGCBYSEhDBlyhTc3d3ZsWMH06ZNY/DgwWzdupVVq1bh4ODA0KFD2b17NwDnz59n/fr1zJo1ixo1ajB9+nSee+45+vbty65du7h58yZOTk6Fcn5JSUkMHjwYf39/OnXqxM2bNylVqhQAzz33HOHh4daxw4cPp3///vTo0YPGjRvj5OREWloaxYoVIy0tzbqfiIiIiPyzKDyJTZo1a0Z0dDQA586dw8/PD8MwcHd3B6BJkybMmjWLc+fO0aBBAxwdHQFo3LgxZ86cAWDv3r04ODhgb28PwMCBA5k/fz6BgYG4urpSv379QjgzuHr1Kr1796Z///54eHgQHx/P6NGj6d+/P4888ghbtmyhYsWKrF69mgMHDvDqq6+SlZVFVlYW586do0aNGqxcuZJnn32W9evXU61aNeLj4wvlXP6qjIyMB67mB4H6WjDU14Kj3hYM9bXgqLcFQ33Nn8KT/Gm/ftenatWqnDx5krp16xIXF0eNGjVwc3NjyZIlZGVlYW9vT1xcHF26dOHkyZMEBgZSvXp1xowZw7Jly9i8eTM+Pj6MHTuWBQsWsGbNGoYMGfK3n8/8+fPJyMjgk08+4ZNPPgFg0qRJzJgxA0dHR1xcXAgPD6d48eKcOHGCSZMmkZOTQ9++fXnmmWd47LHHGDt2LPv27aNs2bLMmjWLEiVK/O3ncTfi4+OtQVjuHfW1YKivBUe9LRjqa8FRbwuG+kq+4VHhSWzy67I9Ozs70tLSCA4Opm7duoSHh2MYBvb29kRERFC1alW8vLzo2bMnOTk5NGrUiHbt2nHy5EkAnn76abZt28aiRYto3rw5wcHBlChRAkdHR8LCwgrl3EJCQggJCblj+6pVq+7YNmnSpDu2ubi4sHjx4gKpTURERETuHwpP8oeaNm3KgQMH8nxu+fLld2zr06cPffr0ybVt6NCh1j//NiRt2LDhHlUpIiIiIlKwdLc9ERERERERGyg8iYiIiIiI2EDhSURERERExAYKTyIiIiIiIjZQeBIREREREbGBwpOIiIiIiIgNFJ5ERERERERsoN95kgeGkZ0NhnHvJ87IgGLF7v28IiIiIvKPoitP8sDItFgKZmIFJxERERGxgcKTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERsoPImIiIiIiNhA4UlERERERMQGCk8iIiIiIiI2UHgSERERERGxgcKTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERsoPImIiIiIiNhA4UlERERERMQGCk8iIiIiIiI2UHgSERERERGxgcKTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJ3lgFHV0vPtJMjLufg4RERER+VdyKOwCRGxlsrcHk+nuJjGMe1OMiIiIiPzr6MqTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERsoPImIiIiIiNhA4Un+lb799lsCAgIAOH78ON26dcPf35/w8HBycnIAmDp1Kt26dcPX15cjR47k2j8uLo7WrVv/7XWLiIiISOFRePofhw4donnz5gQEBNC7d2/8/Pw4e/ZsgR0vLi6O/v37Wx8vWLCAp556iqysLAAOHjzI4MGD890/ICCAs2fPEhMTw8qVK/Mdd+rUKeLi4myq6datWwwcOBAfHx8mTJiQ55iYmBjat29PQEAAPXv2ZMiQIaSmpuY752/rW7ZsmU11FJRFixYREhJCZmYmAG+++Sbjx49nxYoVODk5sXnzZk6ePMnXX3/N2rVriYyMZMqUKdb9k5KSeP/9962vkYiIiIj8Oyg85aFZs2bExsaybNkyhgwZQmRkZIEd64knnuDUqVPWqx379u2jWbNmHD16FIDDhw/TqlWruz7OZ599xg8//GDT2MOHD1OkSBE2btzIgQMHuHbtWp7jgoKCiI2NZeXKlbi5ubF69Wqb5p83b57NdReEatWqERMTY32cnJxMw4YNAWjYsCFHjhyhQoUKFCtWDLPZTGpqKg4Ot38SLTMzk4kTJxIaGloYpYuIiIhIIdKP5P6BmzdvUrlyZeB2qHjnnXcAyMjIYPr06dSsWZN3332XHTt2UK5cOW7dusXrr7+Og4MD06dPx8HBgVKlSjFz5kycnJzumN/R0ZF69epx6tQpKleuTE5ODt7e3uzZs4ennnqKuLg4pk2bRmpqKhMmTOCXX37h2rVrdO/eHX9//zxrnjVrFnFxcRiGQVBQEA0bNmTjxo04Ojri4eHBzp07OXjwIDk5OXTs2JGgoKBc+z/55JNEREQQGhpKq1atKFu27B/26caNG9SrVw+ArVu38sEHH2BnZ0ejRo0YNWqUddy8efO4ceMGoaGhBAYGMm7cOBwcHLC3tycyMhJXV1ebXpe70b59exISEqyPq1atyuHDh3nqqafYvXs3t27dwsHBATs7O7y8vPjll18IDw8HICwsjFdeeeVvqVNERERE7i8KT3k4ePAgAQEBmM1mTp06xYIFCwA4c+YMM2bMwNXVlfnz57Nt2zbatGnDF198wbp167BYLHTq1AmAHTt28Nxzz9G3b1927drFzZs38wxPAE8//TRfffUV58+f5+mnn6ZFixbMnz+fzMxMfvnlFypXrszx48fp2LEjzz//PMnJyQQEBOQZnj7//HMSEhJYtWoVmZmZ+Pr6Ehsbi4+PDy4uLtSvX5+hQ4eybNkyXF1d2bBhwx1z7NmzhwoVKrBjxw5iY2O5cOECFStWpFixYrnGffDBB3z66adcv36d9PR0XnvtNa5fv05MTAzr16+nePHijB49mi+//NK6z6BBg1i2bBmhoaEsX74cDw8PgoOD+eqrr7hx48bfEkri4+NJTk7m1q1bxMfH069fP6KiorCzs6N27doYhsGCBQsoUqQIc+bM4datW4wfPx4nJyf2799PfHw8ANevX6dv3765wuGDKiMjw3pecu+orwVDfS046m3BUF8LjnpbMNTX/Ck85aFZs2ZER0cDcO7cOfz8/Ni7dy+urq5MmTKFEiVKWJd6nT17lscffxx7e3vs7e157LHHABg4cCDz588nMDAQV1dX6tevn+/xWrRowZw5cyhRogS9evXC2dkZZ2dnvvjiC5566ikAXFxcWLp0KZ999hlOTk75ft/m9OnTHD9+3HozhKysLBITE3ONiYqKIioqiqtXr96xJPCnn35izpw5bNmyhUOHDvHaa69hsVhYtmwZFStWzDU2KCiInj17ArB27VrGjh3LiBEjSElJYcCAAQCkpaVx6dKlPGvt1q0bixYtol+/fjg7OzN8+PB8e3Qvubu74+zsTPHixXF3d+fgwYPMnj0bV1dXwsPD6dSpE9euXeOXX37hscceIzs7G2dnZypVqsTu3but87Ro0YLFixf/LTUXtPj4eNzd3Qu7jH8c9bVgqK8FR70tGOprwVFvC4b6Sr7hUd95+gMuLi7WP4eEhBAREcG0adOoUKEChmFQu3ZtvvvuO3JycjCbzZw4cQKAzZs34+PjQ2xsLHXq1GHNmjX5HqNWrVpcvnyZ06dP4+HhAUDLli1ZvHixNdy8//77PPHEE8ycOZMOHTpgGEaec7m5udG0aVNiY2NZunQpXl5eVKlSBZPJZK1x27ZtREVFsXTpUjZu3MhPP/1k3T8rKws7Ozvs7Ozw9PSkUqVKGIZB8eLFf7dPlSpVwmKxUKVKFR5++GHef/99YmNj6d27Nw0aNMg19tfad+7cSaNGjVi6dCkdOnTgvffe+91jFJTq1aszYMAA/Pz8cHJyonXr1tYriH5+fvj5+dGpUyfc3NwKpT4RERERuT/oylMefl22Z2dnR1paGsHBwRQrVozOnTvj6+tLqVKlcHFx4fLlyzz66KO0bt0aX19fypYti6OjIw4ODjz++OMEBwdTokQJHB0dCQsL48qVK0RERFivav1WjRo1MAwDk8kEgKenJ++++671ylObNm0IDQ1l8+bNlClTBnt7e8xm8x3ztG3blsOHD+Pv7096ejrt2rXDycmJxx57jMjISGrVqkXp0qXp3LkzpUuXpkWLFlSqVMm6f/Xq1enVqxf+/v7Y2dnRvHlzGjduzIIFCxgzZkyuY/26bM/e3p6MjAzGjx9PuXLlCAoKIiAggOzsbCpXroyXl1eu/WrVqsWoUaMYNmwYo0ePJiYmBjs7O8aNG3fXr52tqlSpYg20bdu2pW3btrmet7e3Jyws7Hfn+O1yRBERERH55zMZ+V3CEJv8/PPPbNu2jV69emE2m+nYsSNLly7NFUh+lZWVxcyZMwkODi6ESh988fHxuP/fTSn+Mr3d86TL8wVDfS0Y6mvBUW8LhvpacNTbgqG+5t8DXXm6S2XLluX777/npZdewmQy0b179zyDE9xerta3b9+/uUIREREREbkXFJ7ukp2dHVOnTrVprKOjI+XLly/gikREREREpCDohhEiIiIiIiI2UHgSERERERGxgcKTiIiIiIiIDRSeREREREREbKAbRsgDw8jOvvtbjWdkQLFi96YgEREREflX0ZUneWBkWix3P4mCk4iIiIj8RQpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQiIiIiImIDhScREREREREbKDyJiIiIiIjYQOFJRERERETEBgpPIiIiIiIiNlB4EhERERERsYHCk4iIiIiIiA0UnkRERERERGyg8CQPjKKOjrYNzMgo2EJERERE5F/JobALELGVyd4eTKY/HmgYBV+MiIiIiPzr6MqTiIiIiIiIDRSeREREREREbKDwJCIiIiIiYgOFJxERERERERsoPMk/2rfffktAQECubREREaxcudL6eOHChXTu3JlevXqxe/duAC5dukSvXr3w9/dn1KhR3Lp162+tW0RERETuP78bng4dOsTw4cNzbZs5cyYbNmz4UwdJSEjA19cXgLi4OE6ePAlAixYt/nDfWbNm0bVrV3r16sWNGzfyrLF58+YEBAQQEBCAr68vsbGxf6q+gtC2bVsyMzNzbcvJyWH+/Pn4+/tb6z116tQ9P/bEiRPp0qXLX97/19oXLlzIsWPH7mFlf69FixYREhJifR1SUlLo168fu3btso45deoUW7ZsYc2aNbz//vvMmTOHW7duMWPGDPz8/FixYgVNmzZlyZIlhXUaIiIiInKf+NuvPK1fv57Lly/bNDYjI4N169axYcMGqlatysGDB/Mc16xZM2JjY4mNjWXZsmUsWbKEmzdv3suy74n33nuPa9eusWzZMmJjYxk9ejSvvfYaFovlnh3j1q1bHD16lFq1anHo0KG7mmvAgAHUr1//HlX296tWrRoxMTHWx2lpaQwdOpTOnTtbt509e5annnqKokWLUrRoUapXr86pU6f44Ycf8PT0BKBhw4YcOXLkb69fRERERO4vd/U7T7NmzSIuLg7DMAgKCsLLy4vDhw/zzjvvALfDz/Tp03H8vx83/f777/niiy84fvw4tWvXxmw2M3LkSBITEylTpgxz5syxjgUoVqwYDRo0YPz48Vy+fNmmK1WpqanY2dlhb2/PqVOnmDx5MgBlypQhIiKCEydOMHPmTBwdHfH19eX8+fMcPHiQnJwcOnbsSFBQECdOnCA8PBx7e3uKFi1KeHg4OTk5jBw5kooVK3Lp0iUef/xxJk2axH//+19CQ0PJzMzk+vXrDB48mHbt2uVZ2+rVq9mwYQN2drcza/369Vm3bh2Ojo759m3QoEGUKVMGT09PPD097zgfZ2fnXMfYunUrzZs3x9PTk+XLl9O0aVMAvL29ady4MWfOnKF06dJERUWxbds2du7cSWpqKteuXWPw4MG0b9/eOldwcDDe3t40bNiQCRMm8Msvv3Dt2jW6d+9uvXpWt25dzpw5Q2pqKrNnz6Zy5crMnTuXHTt2kJ2dTc+ePfHz8yM2NpYtW7ZgMpnw9vbm5Zdf5rPPPmPRokU4ODhQuXJlIiMjrb25F9q3b09CQoL1cdWqValatSp79+61bnv00UdZuHAhqampWCwWvv76a3r06IG7uzu7du3Cx8eHnTt3atmeiIiIiPxxeDp48GCu74xcunSJYcOG8fnnn5OQkMCqVavIzMzE19eXFi1acObMGWbMmIGrqyvz589n27ZtdOrUCYDHHnuMVq1a4e3tTaVKlUhPT2f48OFUqVKFgIAA4uPjc13pOHHiBKmpqZw/f56hQ4eSnp6O2WymXLlyedZoMplwdHTkzTffpGTJkrz55ptERERQu3Zt1q5dy3vvvcfTTz9NZmYma9euBaB169YsW7YMV1dX63LEkJAQpkyZgru7Ozt27GDatGmMGTOGCxcusHjxYooXL067du24cuUK586do0+fPjRt2pSjR48SExOTb3jKyMigdOnSubaVLVsWIN++XblyhfXr11OkSBF8fX3vOJ//XVa5du1awsLCqFWrFqGhoSQnJ+Pq6kpGRgadOnWiSZMmREZGsnr1akqXLk16ejpLliwhJSWF7t278+yzz95R98WLF+nYsSPPP/88ycnJBAQE4O/vD9wOgBMmTCA6OppPPvmEli1bsnfvXtauXYvZbGbWrFmcOXOGTz/9lBUrVmAymQgKCqJly5Zs2bKFoKAgOnbsyEcffURqaiqlSpX6o7fkPVWrVi169epF//79qV69Og0aNKBs2bKMHTuW8PBwtmzZQvPmza2vk4iIiIj8e/1heGrWrBnR0dHWxzNnzgTg9OnTHD9+3BqssrKySExMxNXVlSlTplCiRAmSk5Np2LBhvnOXLl2aKlWqAODi4nLHv+6/8cYbvPvuu5QsWZKAgADKly9PQEAAHTt2/N0af3X27FkmTZoEgMVioWbNmgDW/wNERUURFRXF1atXadWqFQCXL1/G3d0dgCZNmjBr1izg9jIwJycnAMqXL09mZibly5dn3rx5rFu3DpPJRFZWVr7nW6pUKVJTU61zAGzfvp3mzZvn27cqVapQpEiR3z2f357vmTNnmDZtGgAmk4mVK1fyxhtv4ODgQJMmTYDby9D27t3LE088QZMmTbCzs8PFxYVSpUqRkpJyR90uLi4sXbqUzz77DCcnp1znWK9ePQAqVqzI1atXOX/+PPXr18fe3p7ixYsTEhLCp59+SmJiIkFBQQDcuHGDH3/8kXHjxrFgwQJWrlyJm5tbvqHzr4iPjwcgOTmZW7duWR8DXLlyhaysLOLj47lx4wanT5/mrbfeIi0tjdDQULKysli3bh3e3t7UqFGDjz76CDc3t1xz/NNkZGT8o8+vsKivBUN9LTjqbcFQXwuOelsw1Nf8/eVle25ubjRt2tS6pG3u3LlUqVKFoKAgduzYgZOTE2PHjsUwjFz7mUwm6zaTyfS7x7BYLDg6OlKpUiW6devGnDlzePPNN22usWbNmkyfPp1KlSpx5MgRrly5AmBdGmY2m9m2bRtRUVEYhkHHjh3p2LEjFSpU4OTJk9StW5e4uDhq1KiRb72zZ8+me/futG7dmvXr17Nx48Z86/Hx8eGdd95h7NixmEwmjh49ytSpU9m2bRshISF59u23y9jyO59frV27luHDh9OrVy8AEhMT6dGjB6+99hpZWVnWczpy5Ai1a9cG4Pjx4wBcvfr/2rvz6Jru/f/jr5OTxBQkkZpKVYIm+JpbaqwaqkXdhgRpD0qrEy2uMQkixqhy0VJTfoipQXBVB6220sE8tKXREi6ChkjaCDLv3x9uz20aabc2p0Gej7Ws5Xz25+z93m97SV757LOTpLS0NFWoUCFf3ZGRkWrUqJGCgoK0e/du7dy5s8Bz9Pb21tq1a5Wbm6ucnBwNHjxYY8aMUa1atbR06VJZLBYtX75cderU0TvvvKOhQ4eqQoUKmjBhgj766CM99dRTBe77VvwSfsuWLatSpUrZX0s3gq+Xl5f8/PxkGIbee+89hYaGysXFRRMnTlT9+vWVk5OjSZMmydXVVbVr19bIkSPz3FJ6t4mLi8vTIxQO+uoY9NVx6K1j0FfHobeOQV9VYHj80+Hp0Ucf1d69exUUFKRr166pY8eOcnNzU48ePRQYGKhy5crJy8sr38MhGjZsqFmzZtlXnH7P9OnTNXr0aFmtVlWuXFmzZs3SzJkztWLFClM1hoWFacyYMcrJyZEkTZ06NU89rq6uKl++vHr06KHy5curVatWqlq1qqZMmaLJkyfLMAxZrVZNmzatwGN06dJFU6dO1aJFi1SlShWlpKQUOHfQoEGaO3euevfuLWdnZzk7O2vhwoVydXX9w74VdD6/yMzM1LZt27Rlyxb7WNWqVeXr66sPP/xQ0o2nz50/f15Vq1bV8OHD9e677yopKUn9+/fXlStXNHHiRFmt1nzHbd++vcLCwrR161a5u7vLarUqMzPzpufo5+enNm3aqG/fvsrNzVXfvn3l6+urhx9+WH379lVmZqYaNGigSpUqqUGDBnr22Wfl7u6uMmXK6JFHHimwd39WtWrVFB0dnWds6NCh9r9bLBaFh4fne1/Dhg1v+amSAAAAuLtZjN8uDeGu9Oijj+r9999XiRIl7GMxMTE6efKkRo4cWYSVmRcXFye//94m+Lu4pG8ZP2FyDPrqGPTVceitY9BXx6G3jkFfC+4BvyQXAAAAAEz4S48qx53j178Y9hf+/v5FUAkAAABwZ2LlCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQnnDHMHJybjyG/I/+pKcXdakAAAC4CxGecMfIyMoyN7FkSccWAgAAgGKJ8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMIT7hjlHBx+eNJ3S6aGgAAIABJREFU6emOLwQAAADFknNRFwCYZbFaJYvl9ycZxt9TDAAAAIodVp4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEzgaXu4q2RmZmrcuHE6e/as3NzcNGHCBCUkJGjWrFkqVaqU2rRpo5dffvmm8+6///6iLh8AAAC3MVaebnOLFy9W69atlZGRYfo9gYGBSkhIUExMjHbs2GHqPbm5uRo7dqz8/f314osvKjMzM9/2Dh066MyZM3nGX3rpJX311VcaPnx4vvfExsZq7NixpusuDNHR0SpdurSio6MVGhqqSZMmKTQ0VPPnz9fatWt18uRJ7d+/P9+8yZMn/611AgAA4M5DeLrNbd26VU888YS2bdt2y+/19/dXhw4dTM09deqUjh8/rpiYGF29elU//PBDnu1OTk7q2bOntmzZYh9LSkrSqVOn9PDDD2vOnDlydXW95RoL24kTJ9S2bVtJkre3tw4ePKhy5cqpevXqkqQmTZro4MGD+ebFx8cXWc0AAAC4MxCebmN79uzRfffdpz59+mj16tWSJJvNZv9Gf+3atZo/f74kac6cOfL399fLL7+slJQUSbKvtkjSjBkzFBAQoICAAK1YsSLfsWrUqCHDMDRp0iSVL19efn5++eb07NlT7777rv315s2b5e/vL4vFokcffVQZGRmKj49X7969NWDAAPuxJen9999X79691bdvX82aNUuSlJqaqhdeeEFPP/20+vTpo127dv3lnvn5+enTTz+VYRg6fPiwMjMzlZ6ervj4eOXk5Cg2NlbXrl3LNy8xMVE5OTl/+fgAAAC4e/GZp9vY+vXrFRAQIG9vb7m6uurrr7++6bwffvhB+/bt04YNG3Tt2jV17tw5z/ZPP/1UCQkJio6OVnZ2toKCgtSiRQs98MAD9jl79uxRqVKl9Mknn+iNN97Qjz/+qHLlyqls2bL2OZUqVVLNmjV14MABNW3aVFu3btWyZcvyHGvu3Ll69dVX1apVKy1evFgnT57UTz/9pPnz52vjxo0qVaqURo0apS+//FJffPGFWrZsqf79+ysxMVF9+/bVxx9/LCenP5/pe/bsqX379qlnz57y8/OTj4+PnnvuOY0aNUpubm6qXLmyMjMzVa9evXzzfrvaVpykp6crLi6uqMu469BXx6CvjkNvHYO+Og69dQz6WjDC023q559/VmxsrJKTkxUVFaW0tDStWrUqzxzDMCTduFWtfv36cnJykpubm+rUqZNnXnx8vJo1ayaLxSIXFxc1bNhQ8fHx9vCUnp6u0aNHa+vWrbpw4YKGDh0qNzc3hYeHq1GjRnn2FRgYqC1btshqtapGjRry8vLKs/348eNq0KCBpBu3yJ08eVJnzpxRcnKyBg8eLEm6evWqzp49q/j4eHXv3l3SjWDm5uam5OTkfPu8Fd9++606deqkxx57TN9++62uXbumM2fOaNWqVSpVqpSGDBmi559/Xmlpafnm3Wy1rbiIi4sr1ufvKPTVMeir49Bbx6CvjkNvHYO+qsDwyG17t6l///vf6tmzpyIjI7Vs2TJFR0fryy+/lJOTky5duiRJ+u677yRJNWvW1DfffKPc3Fxdu3ZNJ06cyLMvHx8fHThwQJKUlZWlQ4cOqUaNGvbtubm5MgxDTk5Oqlevnlq2bKlz586pYsWK+epq166dDh06pE2bNql37975tnt7e+vQoUOSpCNHjkiSqlWrpipVqigyMlJRUVF65pln1LBhQ/n4+Gj//v2SpMTERKWmpsrd3f0v9a1GjRpau3atevfurblz52rs2LGqXLmy+vbtqz59+ujhhx9W7dq1bzoPAAAA+D2sPN2m1q9fr5kzZ9pflypVSp07d1blypUVHh6uKlWq2MONn5+funTpol69eqlixYqqUKFCnn21b99ee/fuVe/evZWVlaUuXbqoXr169u2lS5dWaGioBg0aJBcXF9WpU0ejRo3SG2+8oTfeeCPPvqxWqzp06KAPPvhAYWFh+eqeOHGihg8frmXLlsnT01MlSpSQp6enBgwYIJvNppycHN177716/PHH9cILLyg4OFgffvih0tPTFR4eLmfnv3ZJenp6avny5XnGAgMDFRgY+IfzAAAAgN9jMX659wu4zcXFxcmvbt3fn8Tl/KewPO8Y9NUx6Kvj0FvHoK+OQ28dg74W3ANu2wMAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ5wxzBycm48ivz3/qSnF3WZAAAAuEsRnnDHyMjK+uNJJUs6vhAAAAAUS4QnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHjCHaOEi0vegfT0oikEAAAAxZJzURcAmGWxWiWL5X8DhlF0xQAAAKDYYeUJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCecMe7fPmy2rVrp/j4eA0fPlw2m002m02PPvqohg8fbp93/fp19ejRQ7GxsUVYLQAAAO5UPG2vGElISNCTTz6pevXq2ceaN2+uIUOGmN7HRx99pAYNGqhSpUqOKPGWZWVlacKECSpZsqQkac6cOZKkn3/+Wf369dO4cePsc8PDw2X59dP6AAAAgFtAeCpmatWqpaioqD/9/pUrVyosLOy2CU8RERHq06ePFi9enGd8/vz5euaZZ1SxYkVJ0rJly9S4cWMZPN4cAAAAfxLhCZoxY4YOHDggSerWrZv69++vhIQEhYSEKDs7WxaLRaGhofrxxx8VFxenMWPGqH79+mrRooW6dOmiQYMGqU2bNhowYIBCQkLUs2dPZWdna86cObJarapevbrCw8OVkZGhkJAQXblyRSkpKQoICFBQUJBsNps8PDyUmpqqZcuWyWq1mqo7JiZGnp6eatOmTZ7wdPnyZe3atcu+6rRr1y6dPn1a4eHhOnjwYOE3EAAAAMUC4amYOXHihGw2m/21v7+/EhISFB0drezsbAUFBalFixZ66623ZLPZ1LFjR8XFxSk4OFgxMTHy8/NTWFiYkpOTtWnTJj3yyCNKTU3VV199pf79++u7777TlClT1KVLF61Zs0YVKlTQv/71L23atEn16tVT165d1blzZyUmJspmsykoKEiS1L17d3Xq1OmWzmXjxo2yWCzatWuXPdQtXLhQ27dvV7du3ewhbMOGDTp37pxsNptOnjypo0eP6p577pGfn1/hNRYAAAB3PcJTMfPb2/aWLl2qZs2ayWKxyMXFRQ0bNlR8fLzi4+P14IMPSpL8/Pz0448/5tlP06ZNNXXqVO3Zs0edO3fWhx9+qP3796tRo0ZKTk7WxYsXNWzYMElSenq6WrVqpXbt2mnFihXavn273NzclJ2dbd9fzZo1b/lcVq9erbi4OElSSEiIXnjhBSUlJWn79u0KCAiwb3vuuefs75k7d67atGkjSfbtuPFvRD8KH311DPrqOPTWMeir49Bbx6CvBSM8FXM+Pj6KiYnRgAEDlJWVpUOHDumpp56Sj4+P9u/frw4dOiguLk5eXl6SJIvFIsMw5OTkpPr162vp0qUKDg5WUlKSXn/9dQ0fPlweHh6qXLmyFixYoLJly2rHjh0qXbq0IiMj1ahRIwUFBWn37t3auXOnvY4/+yCHX1aPypQpI29vb/n4+CgpKUlt27ZVuXLl8s13d3dX9erVWXX6jbi4OHriAPTVMeir49Bbx6CvjkNvHYO+FvxDdsJTMde+fXvt3btXvXv3VlZWlrp06aJ69epp9OjRGj9+vCIjI5Wdna2pU6dKkho3bqzRo0crMjJSnTp10rhx4+Tr66vWrVtr8+bNevDBB+Xk5KSQkBANHjxYhmGoTJkymjlzpiwWi8LCwrR161a5u7vLarUqMzOzUM7j16tp27ZtK3DejBkzCuV4AAAAKH4sBo8fwx0iLi5OfnXr/m+AS7fQ8BMmx6CvjkFfHYfeOgZ9dRx66xj0teAe8EtyAQAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJ9wxjJycG48n/+VPenpRlwQAAIBihPCEO0ZGVlbegZIli6YQAAAAFEuEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4wh2jhItL3oH09KIpBAAAAMWSc1EXAJhlsVoli+V/A4ZRdMUAAACg2GHlCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMIT7niXL19Wu3btFB8fr9OnT6tv374KCgrSxIkTlZuba593/fp19ejRQ7GxsUVYLQAAAO5UhKe7zPHjxzV48GDZbDb17NlT8+bNk/E3PdL7nXfeUVZW1t9yrF9kZWVpwoQJKlmypCRp+vTpGjZsmNasWSPDMLRjxw773PDwcFl+/ahzAAAA4BYQnu4iqampGjFihIKDgxUVFaXo6Gj98MMPWrdu3d9y/EWLFuVZ6fk7REREqE+fPqpYsaIk6ejRo3rooYckSW3bttVXX30lSVq2bJkaN24sX1/fv7U+AAAA3D34Jbl3kR07dqh58+a6//77JUlWq1URERFycXHRjBkzdODAAUlSt27d1L9/f40dO1bOzs46f/68MjMz9cQTT+jTTz/VhQsXtGDBAl24cEFvv/22nJycdOnSJfXu3VtPP/20bDabwsLC5OPjo7Vr1yopKUmVK1fWpUuXNHz4cC1YsEBvvPGG9u3bJ8MwNGDAAD3++OOy2Wzy8PBQamqqJkyYoODgYDk7O8tqtWrmzJmqVKnSLZ1vTEyMPD091aZNGy1evFiSZBiGfXWpTJkyunLlinbt2qXTp08rPDxcBw8eLLyGAwAAoFghPN1FLl68qOrVq+cZK1OmjD799FMlJCQoOjpa2dnZCgoKUosWLSRJ9957r6ZMmaIJEyYoISFBS5Ys0bx58/TJJ5/Iz89PiYmJ2rx5s3Jzc9W9e3d16dLlpscOCAjQwoULNWfOHO3cuVMJCQlat26dMjIyFBgYqFatWkmSunfvrk6dOmn16tWqV6+exo4dq/379+vnn3++5fC0ceNGXb9+XR9//LFOnTql1157TZcvX1ZcXJwk6fvvv1dOTo6WLVumS5cuyd/fX+fOndPBgwf12muvydvb+1ZbfNdKT0+39w2Fh746Bn11HHrrGPTVceitY9DXghGe7iJVq1bVd999l2fs7NmzOnr0qJo1ayaLxSIXFxc1bNhQ8fHxkqS6detKksqVK2cPE+XKlVNmZqYkqXHjxnJ1dZUk1a5dW2fOnMmz/5t9nuqHH37Q0aNHZbPZJEnZ2dk6f/68JKlmzZqSpF69emnJkiV67rnnVLZsWQ0fPvyWz3f16tX2v/+yGvb6668rNTVVzZs319q1a9WpUyc98cQT9nljx47VE088obZt297y8e5mcXFx8vPzK+oy7jr01THoq+PQW8egr45Dbx2DvqrA8Mhnnu4i7du31+eff24POFlZWZoxY4bKlStnv2UvKytLhw4dUo0aNSTpDx+gEBcXp5ycHF2/fl0nTpxQjRo15OrqqkuXLklSnrBmsViUm5srb29vNW/eXFFRUVqxYoUef/xxVatWLc/xduzYoaZNm2rFihXq0qWLli5dWig9GDNmjObPn6/evXsrKytLjz32WKHsFwAAAGDl6S7i5uamGTNmKDQ0VIZh6OrVq2rfvr1sNpsuXLhgDxRdunRRvXr1TO0zOztbzz//vH766Se99NJL8vT0VL9+/RQeHq4qVarYH9QgSc2aNdPgwYO1cuVK7d27V0FBQbp27Zo6duwoNze3PPutX7++Ro0apfnz58vJyUnjxo37S+ceFRVl//uqVasKnDdjxoy/dBwAAAAUXxbj73qONe44e/bs0bp16zRnzpyiLkXSf5eQ/3uboSSJS7fQsDzvGPTVMeir49Bbx6CvjkNvHYO+FtwDbtsDAAAAABO4bQ8Fat68uZo3b17UZQAAAAC3BVaeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCXcMIyfnxuPJf/mTnl7UJQEAAKAYITzhjpGRlZV3oGTJoikEAAAAxRLhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8AQAAAAAJhCecMco4eLyvxfp6UVXCAAAAIol56IuADDLYrVKFsuNF4ZRtMUAAACg2GHlCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACbwwAjcsXJychQaGqpTp07JarVq+vTpunLlil588UXdf//9kqS+ffvqiSee0JtvvqnPPvtMzs7OCg4OVoMGDYq2eAAAANxxCE//tWfPHg0bNky1atWSYRjKzs7W1KlT5ePjUyj7/+ijj9SgQQNVqlSpUPZ3M4GBgZo9e7aqVauWZ3zbtm1avXq1JMlqtcrX11ejRo2Sq6urw2qRpCFDhujNN9902P4//fRTSdK6deu0Z88eTZ8+XY8++qieffZZDRw40D7v6NGj2rt3r9avX68LFy5o6NCh2rhxo8PqAgAAwN2J2/Z+pUWLFoqKitKqVas0ZMgQzZw5s9D2vXLlSqWlpRXa/szauXOnoqOj9fbbb2vNmjVauXKlLBaLNm/e7PBjOzI4SVLHjh01efJkSdL58+fl5eWlI0eO6LPPPtPTTz+t4OBgpaWl6cCBA2rdurUsFouqVq2qnJwcJScnO7Q2AAAA3H1YeSpAamqq7r33XknS999/rylTpkiS3N3dNW3aNJUuXVoTJkzQjz/+qJSUFLVt21bDhg3T2LFj9cQTT6ht27aKjY3Ve++9py5duiguLk5jxoxRQECA/vOf/2jMmDHKycnRP/7xD23cuNG+CpSWlqaQkBBduXJFKSkpCggIUFBQkGw2m3x9fXX8+HGlpaVp7ty5uvfeezVnzhx9/vnnqly5slJSUvKdR1RUlEaPHq1y5cpJkiwWi8aNGyfLf39fUvv27eXt7S1vb2/1799fISEhys7OlsViUWhoqHx9ffXvf/9bK1askKurq+6//36Fh4dr69at+vTTT5Wenq5Lly6pX79+2rFjh44fP67Ro0erY8eOatWqlb788ssCa3/rrbf08ccfy9PTU9evX9drr72m5s2b39K/k7Ozs8aMGaOPPvpI8+bNU2JiogICAlS/fn0tXLhQb731lsqWLSt3d3f7e8qUKaMrV67I09Pz1i8MAAAAFFuEp1/ZvXu3bDabMjMz9f3332vRokWSpPHjx2vatGmqVauW1q9fr6VLlyogIECNGjVSQECAMjIy7OHpZh555BH5+fkpLCxMlSpVkr+/v0aOHKnPP/9czZs3z3P73OnTp9W1a1d17txZiYmJstlsCgoKkiQ1aNBAISEhmjNnjrZt26ZHHnlE+/bt04YNG3Tt2jV17tw537ETEhJUo0YNSdKhQ4c0e/ZsZWVlqUqVKpozZ44uXLigmJgYeXh46NVXX5XNZlPHjh0VFxen4OBgLVu2TPPnz9emTZvk5uamadOm6Z133lHp0qV19epVRUZGatu2bVq+fLmio6O1Z88erVy5Uh07dsxTx29rb9u2rT7//HNt2LBBWVlZ6t69+5/+d4uIiNDIkSMVGBiodevW2W+N7NSpkyZPnqwOHTro6tWr9vlXr15V2bJl//TxAAAAUDwRnn6lRYsWmjNnjiTp5MmT6tOnj2JjYxUfH69JkyZJkrKyslSzZk25u7vr22+/1e7du+Xm5qbMzMx8+zMMI9+Ym5ubHnzwQX3xxReKiYnRyy+/nGe7l5eXVqxYoe3bt8vNzU3Z2dn2bXXr1pUkVa5cWUlJSTpx4oTq168vJycnubm5qU6dOvmOV6VKFSUkJMjX11eNGzdWVFSU4uPjFRYWJkny8PCQh4eHJCk+Pl4PPvigJMnPz08//vijzp49q1q1asnNzU2S7LU3bNhQfn5+kqSyZcvKx8dHFotF5cuXV0ZGRr46flt7fHy8/u///k9Wq1VWq1X169cv6J+lQAsWLNDly5fVq1cvXbt2TdnZ2Ro0aJCef/551alTR++++64qV64sDw8PrVixQi1bttTly5d1/fp1JSYmKjEx8ZaPebdKT09XXFxcUZdx16GvjkFfHYfeOgZ9dRx66xj0tWCEpwJ4eXnZ/16zZk1FRESoatWqOnDggC5duqSYmBiVLVtW4eHhOn36tKKjo2UYhlxdXXXp0iVJ0nfffWffh8VisYepwMBALVmyRCkpKfL19c1z3MjISDVq1EhBQUHavXu3du7cWWCNNWvW1MqVK5Wbm6v09HSdOHEi35xnnnlGM2fO1Ny5c+2rLXv37rVvd3L638fefHx8tH//fnXo0EFxcXHy8vJStWrVFB8fr2vXrql06dLau3evatasaT+nP6tWrVqKiopSbm6usrOz8/TKrAEDBmjcuHGaMmWKsrOzNXHiRFWpUkWTJ0+Wi4uLvLy8NHnyZLm5uek///mPwsLClJubq6lTp9qDH26Ii4ujJw5AXx2DvjoOvXUM+uo49NYx6KsKDI+Ep1/55bY9JycnXb16VWPHjlXJkiUVFhZm/4ySJPtT+EaMGKEDBw6oVKlSqlGjhi5evKiAgAAFBwdr69at9sdlS1Ljxo01evRoRUZGqmHDhjp9+rSefvrpfDW0b99eYWFh2rp1q9zd3WW1Wm+6qiXdWB3q0qWLevXqpYoVK6pChQr55nTo0EHZ2dn2Fa6rV6/K19dXERER+eaOHj1a48ePV2RkpP1pg56enho6dKj69esnJycn3XfffRo5cqS2bdv2Z1ps98ADD6hdu3YKDAyUh4eHXFxc5Ox8a5dj6dKlNXfu3Hzj69atyzc2dOhQDR069E/XCwAAAFiMm91bBofKzc1V3759tWzZMvvtcMXN5cuX9cEHH+jpp59WZmamunbtqhUrVqhq1aoFvicuLk5+/739T1y2hYqfMDkGfXUM+uo49NYx6Kvj0FvHoK8F94CVp7/Z2bNnNWTIEPXu3bvYBifpxmetjhw5op49e8pisSggIOB3gxMAAABQ1AhPf7Pq1atry5YtRV1GkXNyctL06dOLugwAAADANH5JLgAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPOGOYeTk3HhEuWFI6elFXQ4AAACKGcIT7hgZWVn/e1GyZNEVAgAAgGKJ8AQAAAAAJhCeAAAAAMAEwhMAAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwgfAEAAAAACYQngAAAADABMITAAAAAJhAeAIAAAAAEwhPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMIT7hjlHBxufGX9PSiLQQAAADFknNRFwCYZbFaJYtFMoyiLgUAAADFECtPAAAAAGAC4QkAAAAATCA8AQAAAIAJhCcAAAAAMIHwBAAAAAAm8LQ93HGysrIUHBysc+fOKTMzUy+99JKqVq2qyZMny2q1ytXVVREREfLy8pIkJScnq0+fPtq6datKlChRxNUDAADgTnVXrjz169dP33zzjSQpMzNTTZs21bJly+zbn3nmGR07dqzQj2uz2RQfH59nbM+ePRo+fLip9ycnJ2vo0KEaNGiQBg4cqNDQUKUX4u80+v7777Vv37584/Xr15fNZpPNZlNAQIDmzp2r3NzcW9p3TEyMduzYUVil/q5///vfcnd315o1a7RkyRJNnjxZU6dO1fjx4xUVFaVOnTppyZIlkqTPP/9cAwcOVFJS0t9SGwAAAO5ed2V4at26tfbv3y9JOnDggFq3bq3PPvtMkpSRkaELFy7I19e3CCu8uaVLl6ply5ZatmyZIiMjVapUKa1bt67Q9r99+3adOHEi33j58uUVFRWlqKgoRUdH6/Lly1q9evUt7dvf318dOnQorFJ/V5cuXfTaa6/ZX1utVs2ePVt+fn6SpJycHPsKk5OTk/7f//t/cnd3/1tqAwAAwN3rrrxtr2XLllqwYIEGDhyonTt3KiAgQLNmzdKVK1d09OhRPfTQQ5KkvXv3as6cObJarapevbrCw8OVkZGhkJAQXblyRSkpKQoICFBQUJBsNps8PDyUmpqq+fPnKzQ0NN8cSZo3b55SUlLk6uqqmTNn5qnr/fff1/Lly+Xk5KSmTZtq5MiRebbfe++9+vDDD1WjRg01adJEY8aMkcViUUJCgoYPH64qVaooISFBXbt21fHjx/Xdd9/pkUce0YgRI7R37169+eabkqT09HRFRETIxcVFL730ktzd3dW8eXNt2rRJLi4uqlevnho0aHDT3lksFj377LMKDg6WzWa7ac3+/v6aN2+eqlWrpvfff18HDhxQ+fLl5eXlpd69e2vKlCn65ptvlJWVpaFDh6pjx4564403tG/fPhmGoQEDBujxxx/X6tWrtXnzZjk5OdnP14wyZcpIktLS0vTqq69q2LBhqli8Utn9AAAWyklEQVSxoiTp4MGDWrVqlT38tWrVytQ+AQAAgD9yV4anunXr6uTJkzIMQ/v27dOIESP08MMP66uvvtL333+vNm3ayDAMjR8/XmvWrFGFChX0r3/9S5s2bVK9evXUtWtXde7cWYmJibLZbPZg1L17d3Xq1ElHjx4tcE7nzp3VtWtXrV69WosWLdKjjz4qSfrpp580f/58bdy4UaVKldKoUaP05Zdf5vnmvm/fvipRooSWLVum1157TU2bNtXEiRMlSWfPnlVkZKTS09PVoUMHxcbGqlSpUmrfvr1GjBih48eP6/XXX1elSpX09ttv64MPPlD37t116dIlbdy4Ua6urjIMQ15eXgUGp194eXkpJSWlwJp79eqlzZs3a8iQIdq0aZNGjhypDz/8UJK0Y8cOpaSkaMOGDbp06ZJWrVolFxcXJSQkaN26dcrIyFBgYKBatWqlmJgYjR8/Xo0aNdKaNWuUnZ0tZ2dzl2RsbKxmzJihxx9/XLVq1VJcXJy++OILrV+/XuPGjVNiYqISExPt8zMzM3Xs2DG5urqavIqKl/T0dMXFxRV1GXcd+uoY9NVx6K1j0FfHobeOQV8LdleGJycnJ/n6+io2Nlb33HOPXF1d1bZtW3322Wc6duyY+vXrp+TkZF28eFHDhg2TdOMiadWqldq1a6cVK1Zo+/btcnNzU3Z2tn2/NWvWlHQjXBQ0p1mzZpKkJk2aaOfOnfbxM2fOKDk5WYMHD5YkXb16VWfPns1T9549e/SPf/xDvXr1UmZmppYsWaJp06ZpzJgxql69usqWLStXV1d5eXnZb0OzWCySpEqVKmnq1KkqXbq0EhMT1aRJE0lStWrVbjkwnDt3TpUrVy6w5ieffFJ9+/ZVQECA0tLSVKdOHXt4OnXqlBo1aiRJuueeezR8+HAtWbJER48elc1mkyRlZ2fr/Pnzmj59uiIjIzVr1iw1atRIhmGYqi8pKUnTp0/XhAkT9PDDD0uStmzZok8//VTR0dE3vUXP1dVVvr6+PDCiAHFxcfbbHlF46Ktj0FfHobeOQV8dh946Bn1VgeHxrgxP0o3btRYtWqSuXbtKkpo2baoFCxbIyclJ7u7uys3NVeXKlbVgwQKVLVtWO3bsUOnSpRUZGalGjRopKChIu3fvzhOAfgkqvzfn22+/VaVKlbR//37Vrl3bPl6tWjVVqVJFkZGRcnFxUUxMTL6LcsWKFTp79qwCAwPl6uqq2rVr6+TJk3mOXZDQ0FB9/PHHcnNz05gxY+xBxMnpfx9rs1gsf/ggiNzcXEVGRqpr164F1uzm5qb69etr+vTp8vf3z/N+b29vffDBB5KkK1euaNiwYQoKClLz5s01efJk5ebmasGCBapWrZr+9a9/adKkSSpRooQGDRqkQ4cO2W+p/D1vv/22UlNTtWDBAi1YsEA5OTk6fvy4qlatqqFDh0qSHnzwQb366qt/uC8AAADArLs2PLVs2VKhoaH2zx25urqqbNmyqlu3rqQboSIkJESDBw+WYRgqU6aMZs6cKYvForCwMG3dulXu7u6yWq3KzMzMs+/27dsXOOfjjz/WihUrVKZMGUVERNif6ufp6akBAwbIZrMpJydH9957rx5//PE8+500aZImTZqkNWvWqGTJkvLw8FBYWJiysrL+8Hx79OihwMBAlStXTl5eXrp48WK+OfXr19fMmTPl4+OjFi1a2Md//vln2Ww2WSwWZWdnq2XLlurVq5csFkuBNQcEBOi5557TtGnT8hyjQ4cO2rVrl/r27aucnBy98soratu2rfbu3augoCBdu3ZNHTt2lJubmx544AH16tVLHh4eqlSpkho2bPiH5yndCIqhoaGm5v7ik08+uaX5AAAAwG9ZDLP3SgFFLC4uTn5160pcsoWO5XnHoK+OQV8dh946Bn11HHrrGPS14B7clY8qBwAAAIDCRngCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADCB8IQ7hpGTc+Mx5enpRV0KAAAAiiHCE+4YGb/8suCSJYu2EAAAABRLhCcAAAAAMIHwBAAAAAAmEJ4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACYQnAAAAADDBYhiGUdRFAGYcPnxYJUqUKOoyAAAAcJfLyMhQo0aN8o0TngAAAADABG7bAwAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAmEJwAAAAAwwbmoCwD+SG5ursLCwvT999/L1dVVU6ZMUY0aNYq6rNva119/rVmzZikqKkqnT5/W2LFjZbFYVLt2bU2cOFFOTk5688039dlnn8nZ2VnBwcFq0KDBLc0tTrKyshQcHKxz584pMzNTL730kmrVqkVfC0FOTo5CQ0N16tQpWa1WTZ8+XYZh0NtCcvnyZfn7+ysyMlLOzs70tZD84x//UNmyZSVJ1apVU+/evTV16lRZrVa1bt1aQ4YMKfBr1+HDh03PLW4WLVqkTz75RFlZWerbt68eeughrtlCEBMTo02bNkm68fjtuLg4RUVFcc3+WQZwm/vwww+NMWPGGIZhGIcOHTJefPHFIq7o9rZ48WKjW7duRkBAgGEYhvHCCy8Yu3fvNgzDMMaPH29s377dOHLkiGGz2Yzc3Fzj3Llzhr+//y3PLU42bNhgTJkyxTAMw0hOTjbatWtHXwvJRx99ZIwdO9YwDMPYvXu38eKLL9LbQpKZmWm8/PLLRufOnY0TJ07Q10KSnp5u9OjRI8/Yk08+aZw+fdrIzc01nnvuOePIkSMFfu26lbnFye7du40XXnjByMnJMdLS0ox58+ZxzTpAWFiYsW7dOq7Zv4Db9nDbO3DggNq0aSNJatSokY4cOVLEFd3e7rvvPs2fP9/++ujRo3rooYckSW3bttVXX32lAwcOqHXr1rJYLKpatapycnKUnJx8S3OLky5duui1116zv7ZarfS1kHTs2FGTJ0+WJJ0/f15eXl70tpBERESoT58+qlixoiT+Lygsx44d0/Xr1zVw4ED169dP+/btU2Zmpu677z5ZLBa1bt1au3btuunXrrS0NNNzi5svvvhCderU0SuvvKIXX3xRjzzyCNdsIfv222914sQJde3alWv2LyA84baXlpYmNzc3+2ur1ars7OwirOj29thjj8nZ+X935BqGIYvFIkkqU6aMrly5kq+nv4zfytzipEyZMnJzc1NaWppeffVVDRs2jL4WImdnZ40ZM0aTJ0/WY489Rm8LQUxMjDw9Pe3f3Ej8X1BYSpYsqUGDBmnZsmWaNGmSxo0bp1KlStm3F9Qvq9VaYA/5OielpKToyJEjmjt3riZNmqSRI0dyzRayRYsW6ZVXXrml65BrNj8+84Tbnpubm65evWp/nZubmycc4Pc5Of3vZyRXr15VuXLl8vX06tWrKlu27C3NLW4uXLigV155RUFBQerevbtef/11+zb6+tdFRERo5MiRCgwMVEZGhn2c3v45GzdulMVi0a5duxQXF6cxY8bk+Yk7ff3zatasqRo1ashisahmzZoqW7asfvrpJ/v2X/qVnp6e72vXzXpY0Nzi9nXO3d1d3t7ecnV1lbe3t0qUKKEff/zRvp1r9q9JTU3VyZMn1aJFC6WlpZm+Drlm82PlCbe9Jk2aKDY2VpJ0+PBh1alTp4grurPUrVtXe/bskSTFxsaqWbNmatKkib744gvl5ubq/Pnzys3Nlaen5y3NLU6SkpI0cOBAjRo1Sr169ZJEXwvL5s2btWjRIklSqVKlZLFYVL9+fXr7F61evVqrVq1SVFSU/Pz8FBERobZt29LXQrBhwwbNmDFDkpSYmKjr16+rdOnSOnPmjAzD0BdffGHv12+/drm5ucnFxcXU3OKmadOm+vzzz2UYhr2vDz/8MNdsIdm3b59atmwpSbd0HXLN5mcxDMMo6iKA3/PLE11++OEHGYahadOmycfHp6jLuq0lJCRoxIgRio6O1qlTpzR+/HhlZWXJ29tbU6ZMkdVq1fz58xUbG6vc3FyNGzdOzZo1u6W5xcmUKVP0/vvvy9vb2z4WEhKiKVOm0Ne/6Nq1axo3bpySkpKUnZ2t559/Xj4+PlyzhchmsyksLExOTk70tRBkZmZq3LhxOn/+vCwWi0aOHCknJydNmzZNOTk5at26tYYPH17g167Dhw+bnlvczJw5U3v27JFhGBo+fLiqVavGNVtIli5dKmdnZw0YMECSbuk65JrNi/AEAAAAACZw2x4AAAAAmEB4AgAAAAATCE8AAAAAYALhCQAAAABMIDwBAAAAgAnF47dZAQDuKDNmzNDRo0d16dIlpaenq3r16vLw8NC8efNuOj8hIUHHjx9X+/btb7r99OnTGjt2rNauXWsfy87OVqNGjdS4ceM8c2fPnq177rmn8E6mABEREfL19ZWPj48+//xzvfTSS/rggw/UtGnTv+X4t2Lt2rV699135eTkpOzsbP3zn//83Uc+F+V5ZGRkKDQ0VDNnztSoUaPk7++ve++9V127dtX69evl5+cnSVq1apVSU1P18ssvq23btrrvvvtksViUk5Oj69eva8qUKapXr57mzJmjHj165PlVBQCKL8ITAOC2M3bsWElSTEyMTp48qZEjR/7u/F27dikhIaHA8FQQT09PRUVF/ek6C0P9+vVVv359SdKKFSvs39zfLrZs2aJ9+/ZpxYoVcnZ21pkzZ/TMM89o69atKl++/E3fU5TnERkZqW7dusliseQZL1OmjMaOHav169fL1dU13/uWL18uZ+cb3xZ99tlneuutt7RgwQI9++yzGjt2rN5+++2/pX4AtzfCEwDgjjJ16lQdPnxYktSjRw8FBgZq6dKlyszMVOPGjVWiRAktXLhQ0o1ViNdff/2WjzFt2jSVKVNGQ4cOVf/+/fX888/rwIEDOnv2rJKSkpSamqqJEyeqcePG2rZtm1auXCknJyc99NBDGj58uObMmaPExEQlJSXpwoULCg4OVqtWrfTee+9p8eLF8vT0VEZGhnx9ffXVV18pJiZGjz/+uH744QeNHDlSERERCgkJsa+U+fv7a8GCBVq7dq2+/fZbXbt2TdOnT9fOnTv1/vvvS5KefPJJPf3003nO44knnlCTJk104sQJeXp6avbs2XJyctLEiRN19uxZ5eTk2FeRunbtqvvvv18lS5bUG2+8Yd/HO++8o4kTJ9qDxX333actW7aofPnyOnbsmGbOnKnc3Fx7T5KSkuznsW7dOkVFReWr8dSpUxo3bpxcXV1VpUoVJSYmavny5dq8ebOioqLk6uqqmjVrKjw8XJs2bdKWLVuUk5OjF198UVu2bNHs2bMlSYGBgVq4cKEqVKggSTIMQ9u2bdPgwYPz/Zt6e3urQYMGmjdv3h+G8XPnztmDobu7uywWi06cOKFatWrd8rUE4O5CeAIA3DE+/vhjXbx4UdHR0crKylKfPn3UokULPffcc0pISNAjjzyiqKgozZ49W15eXnrzzTf1wQcf6LHHHrvp/pKTk2Wz2eyvq1SpopkzZ2rkyJEKCgrS2bNn1bRpU7Vt21YHDhxQmTJlNHv2bB07dkzjxo3TsmXLtHDhQm3YsEElS5bUiBEjtHv3bklSyZIltXTpUu3cuVMrV65Uy5YtNWvWLG3atEnlypXToEGD8tTSoUMH1alTRzNmzPjdHtSpU0djx47VsWPH9PHHH2vt2rUyDEP9+/dX69atVaNGDfvctLQ0+fv7q0mTJpo+fbqio6NlsVhUsWJFTZ8+XcnJyerXr5/effddXblyRa+++qoeeOCBPMe7ePGiqlevnmfMw8NDknTixAkFBwerVq1a2rx5s2JiYjRp0iT7eRw/fvymNUZERGjIkCFq3bq11qxZo+3bt+vy5ctauHChNm3apNKlS2vy5Mlav369nJ2d5eHhofnz5ys3N1fTpk3TlStXdO7cOVWqVMkenCQpPj5e7u7uslqtN+3diBEj1KtXL3Xo0CHftgEDBig9PV2XLl1SmzZtNGrUKPu2Bx54QHv27CE8ASA8AQDuHPHx8WrWrJksFotcXV3VsGFDxcfH55lTqVIlhYeHq3Tp0vrxxx/10EMPFbi/gm7bc3V1lc1mU0hIiGJjY+3jLVq0kCT5+voqMTFR//nPf3T58mU9//zzkm6ElbNnz0qS6tatK+lGIMvIyNDFixfl4eFhX9H47WetzKpZs6Yk6fjx40pISFD//v0lST///LNOnz6dJzyVKFFCTZo0sR9vz549ys7O1uHDh3Xw4EFJUlZWllJTU/Ps+9eqVq2qCxcuyMfHxz4WGxurunXrqlKlSpo/f75KliypK1eu2EPVLwqqMT4+3n7+zZo10/bt23XmzBnVqVNHpUuXto/v379fvr6+9rqcnJzUrVs3vffeezpx4oR69eqV53gpKSl5wtRvlShRQtOmTdOYMWP01FNP5dm2fPlyWa1WzZo1SxcvXpSnp6d92z333KOffvqpwP0CKD542h4A4I7h4+OjAwcOSJIyMzN1+PBh1ahRQxaLRYZhSJLGjx+vGTNmaMaMGapQoYJ9/FakpKRo6dKlGj16tMaPH28fP3LkiCTp2LFjqlq1qu677z5VqVJFkZGRioqK0tNPP60GDRpIUr7P3Hh6euqnn35SSkpKnn39mpOTk3Jzc1WiRAklJycrNzdXP//8s86dO5dnjnTjNrQ6depo5cqVioqK0lNPPaU6derk2V9mZqaOHz8uSTp48KBq1aolb29vPfnkk4qKitLixYvVpUsXlS1bNs++f61nz55asGCBsrOzJd0IsOPHj5fValV4eLiGDx+uiIgI1a5d297rX86joBpr165tv/Xy66+/lnTjdsAffvhB169flyTt27dP999/f75e9uzZU++9954OHTqk1q1b5+vxlStX8p3Dr/3f//2fHnvsMUVGRubbZrFY9M9//lMJCQlat26dfTw1NfV3QxmA4oOVJwDAHaNDhw7au3ev+vTpo8zMTHXr1k2+vr7KysrSkiVL5Ofnp+7du6tXr14qV66cKlSooIsXLxa4v9/etidJo0aN0sKFC/Xiiy+qa9eu+uabb7R69WpJNwJP//79lZ6ervDwcHl5eclms8lmsyknJ0fVq1dXt27dbnosFxcXTZkyRQMHDlT58uVvemtZkyZNNHLkSC1fvlzNmjVTz549VaNGjTyrSb+oV6+emjVrpr59+9o/71WxYsU8cwzD0Ntvv61z586pWrVqGjlypHJzcxUaGqpnnnlGaWlpeuaZZ/IFvV/r0aOHkpKSFBQUJBcXF+Xk5OiNN96Qh4eHnnzySb3wwguqUKGCKlWqZA8uvz2P39Y4evRohYSEaPHixXJzc5Ozs7MqVKigl156Sf369ZPFYlHNmjXVu3dvbdmyJU89VatWlaurqxo3bpyvh97e3rp48aJycnIKvHVPkl5++WV99tlnN93m5OSkqVOnql+/furQoYPuueceff311xo3blyB+wNQfFiMP/MjOQAAipk5c+aoWrVqCggIKOpSTGvbtq0++eQT+8MebhebN29W06ZNVb16da1du1bfffedJk+ebPr9zz33nCZOnJjvs1iS9NZbb8nPz0+PPvpoodSanJyskJAQ+0NIABRv3LYHAAD+VpUrV9Zrr72moKAgvf/++3rhhRdMve/q1avy9/dXgwYNbhqcJGngwIHatm3bn7pd82aWL1+uf/7zn4WyLwB3PlaeAAAAAMAEVp4AAAAAwATCEwAAAACYQHgCAAAAABMITwAAAABgAuEJAAAAAEwgPAEAAACACf8f5AyOezeqG0oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "ax = category_expenditure['Prices'].plot(kind='barh', figsize=(12,8), color='red')\n",
    "ax.set_xlabel(\"Total Expenditure per Category (INR)\")\n",
    "ax.set_ylabel(\"\")\n",
    "for i in ax.patches:\n",
    "    ax.text(i.get_width()+0.8, i.get_y()+0.2, str(i.get_width()))\n",
    "plt.title('Category vs Total Expenditure per Category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Purchase Month'] = df['Purchase Date'].apply(lambda x: x[-3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Purchase Date</th>\n",
       "      <th>Titles</th>\n",
       "      <th>Category</th>\n",
       "      <th>Status</th>\n",
       "      <th>Prices</th>\n",
       "      <th>Purchase Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>14999</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sat, 19th Sep</td>\n",
       "      <td>Realme 7 (Mist Blue, 64 GB)</td>\n",
       "      <td>Mobiles &amp; Accessories</td>\n",
       "      <td>Delivery expected between Today, Sep 24 and Oc...</td>\n",
       "      <td>14999</td>\n",
       "      <td>Sep</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>Amish's Ramachandra Series - Ram, Sita &amp;...</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 10</td>\n",
       "      <td>369</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>The Richest Man In Babylon</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>175</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sat, 29th Aug</td>\n",
       "      <td>HOW TO TALK TO ANYONE</td>\n",
       "      <td>Books</td>\n",
       "      <td>Delivered on Sep 09</td>\n",
       "      <td>184</td>\n",
       "      <td>Aug</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Purchase Date                                       Titles  \\\n",
       "0  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "1  Sat, 19th Sep                  Realme 7 (Mist Blue, 64 GB)   \n",
       "2  Sat, 29th Aug  Amish's Ramachandra Series - Ram, Sita &...   \n",
       "3  Sat, 29th Aug                   The Richest Man In Babylon   \n",
       "4  Sat, 29th Aug                        HOW TO TALK TO ANYONE   \n",
       "\n",
       "                Category                                             Status  \\\n",
       "0  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "1  Mobiles & Accessories  Delivery expected between Today, Sep 24 and Oc...   \n",
       "2                  Books                                Delivered on Sep 10   \n",
       "3                  Books                                Delivered on Sep 09   \n",
       "4                  Books                                Delivered on Sep 09   \n",
       "\n",
       "   Prices Purchase Month  \n",
       "0   14999            Sep  \n",
       "1   14999            Sep  \n",
       "2     369            Aug  \n",
       "3     175            Aug  \n",
       "4     184            Aug  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "months = {\"Jan\":0, \"Feb\":1, \"Mar\":2, \"Apr\":3, \"May\":4, \"Jun\":5, \"Jul\":6, \"Aug\":7, \"Sep\":8, \"Oct\":9, \"Nov\":10, \"Dec\":11}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Purchase Month'] = df['Purchase Month'].map(months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_exp = df.pivot_table(values='Prices', index='Purchase Month', aggfunc=sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def change(row):\n",
    "    for key, value in months.items():\n",
    "        if row == value:\n",
    "            return key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_exp.sort_values(by='Purchase Month', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_exp.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "months_exp['Purchase Month'] = months_exp['Purchase Month'].apply(change)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Purchase Month</th>\n",
       "      <th>Prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan</td>\n",
       "      <td>1249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar</td>\n",
       "      <td>474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May</td>\n",
       "      <td>43324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jun</td>\n",
       "      <td>11349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Aug</td>\n",
       "      <td>2046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sep</td>\n",
       "      <td>29998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Oct</td>\n",
       "      <td>19499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Dec</td>\n",
       "      <td>1712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Purchase Month  Prices\n",
       "0            Jan    1249\n",
       "1            Feb     404\n",
       "2            Mar     474\n",
       "3            Apr     392\n",
       "4            May   43324\n",
       "5            Jun   11349\n",
       "6            Aug    2046\n",
       "7            Sep   29998\n",
       "8            Oct   19499\n",
       "9            Dec    1712"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' Expenditure per Month vs Months')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.bar( months_exp['Purchase Month'], months_exp['Prices'], color='darkblue')\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Expenditure/Month')\n",
    "plt.title(' Expenditure per Month vs Months')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}