Copy_of_Project_odin.ipynb

{
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   "cell_type": "markdown",
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "Nsc7vcLlSZJ5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WmITUuVvSvTd"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 396
    },
    "id": "UFIesFYQT7VE",
    "outputId": "37d0bd21-82ef-4413-b6fe-de156d22b6ea"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('df1_stock.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JtSMaO_rXOFg",
    "outputId": "bce53ef1-a347-4363-c068-1be0213d9ba7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "In Stock    713788\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L-g7XRgdUEtn",
    "outputId": "d4df5719-7032-49a4-e49d-726271f381f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 713788 entries, 0 to 713787\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   category         713788 non-null  int64  \n",
      " 1   manufacturer     713788 non-null  object \n",
      " 2   part_number      713788 non-null  object \n",
      " 3   sku              713788 non-null  object \n",
      " 4   alt_sku          8242 non-null    object \n",
      " 5   description      713788 non-null  object \n",
      " 6   status           713788 non-null  object \n",
      " 7   qty              713788 non-null  int64  \n",
      " 8   price            713788 non-null  float64\n",
      " 9   sku_create_date  713788 non-null  object \n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 54.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zbdK9CFiUL32",
    "outputId": "371cbdcd-afd5-41ff-de25-36922c80d433"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['category', 'manufacturer', 'part_number', 'sku', 'alt_sku',\n",
       "       'description', 'status', 'qty', 'price', 'sku_create_date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "geRt7p1WU14B"
   },
   "outputs": [],
   "source": [
    "df.drop(['sku', 'alt_sku','description','status'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "id": "ve0sjs44UjiV",
    "outputId": "baa1031e-c3d1-4dc9-d10a-7dcabc17b969"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 713788 entries, 0 to 713787\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   category         713788 non-null  int64  \n",
      " 1   manufacturer     713788 non-null  object \n",
      " 2   part_number      713788 non-null  object \n",
      " 3   qty              713788 non-null  int64  \n",
      " 4   price            713788 non-null  float64\n",
      " 5   sku_create_date  713788 non-null  object \n",
      "dtypes: float64(1), int64(2), object(3)\n",
      "memory usage: 32.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "n0ezYUlDURvb"
   },
   "outputs": [],
   "source": [
    "df = df.astype({\"category\":'category'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "IbiKB-E2Yc2k"
   },
   "outputs": [],
   "source": [
    "df['sku_create_date'] = pd.to_datetime(df['sku_create_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "VnX0OFQKY1WI"
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'sku_create_date': 'date', 'part_number': 'product'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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    "id": "sPk92qexUH8a",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>713788.000000</td>\n",
       "      <td>713788.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>152.365662</td>\n",
       "      <td>158.543729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1105.661017</td>\n",
       "      <td>553.645433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>28.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>89.000000</td>\n",
       "      <td>99.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60063.000000</td>\n",
       "      <td>69729.030000</td>\n",
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      "text/plain": [
       "                 qty          price\n",
       "count  713788.000000  713788.000000\n",
       "mean      152.365662     158.543729\n",
       "std      1105.661017     553.645433\n",
       "min         1.000000       0.000000\n",
       "25%         8.000000       9.640000\n",
       "50%        26.000000      28.060000\n",
       "75%        89.000000      99.340000\n",
       "max     60063.000000   69729.030000"
      ]
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     "execution_count": 10,
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   "source": [
    "df.describe()"
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   "execution_count": null,
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    "id": "Xs18QefiUlsd",
    "outputId": "0d87a4fc-bac5-4ca4-d8bb-bc189bc334c9"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "category        0\n",
       "manufacturer    0\n",
       "product         0\n",
       "qty             0\n",
       "price           0\n",
       "date            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "vSs75ZCgZms7"
   },
   "outputs": [],
   "source": [
    " df.to_csv('better_imus_stock_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aKAnD5tnTDYC"
   },
   "source": [
    "# Python Project\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 336
    },
    "id": "b8XyZiGTTHnY",
    "outputId": "a080d1ee-d235-413e-ed34-1a2bb0e836d4"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "  category                    manufacturer             product  qty   price  \\\n",
       "0     9920  CASE LOGIC-PERSONAL & PORTABLE             3200943    7   99.00   \n",
       "1      531                OMNITRON SYSTEMS              7206-0    2   59.09   \n",
       "2     9070         ZEBRA ENTERPRISE MCD-A1       SAC5070-800CR    3  500.06   \n",
       "3     9080         ZEBRA ENTERPRISE MCD-A1  KT-CLMPT-RS507-01R  115   86.34   \n",
       "4     9080         ZEBRA ENTERPRISE MCD-A1    KT-PAD-RS507-10R   42   51.80   \n",
       "\n",
       "        date  \n",
       "0 2020-08-17  \n",
       "1 2020-08-17  \n",
       "2 2020-08-17  \n",
       "3 2020-08-17  \n",
       "4 2020-08-17  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8MDp5kcNTIDw"
   },
   "source": [
    "## For data In Stock:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "FYzTtDZSTN5H"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LBd68qLkTOPT"
   },
   "source": [
    "### 1. Which are the top Manufacturers with price, quantity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "GF5BlMV3uw6p"
   },
   "outputs": [],
   "source": [
    "df56 = df.groupby('manufacturer').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fuDSVMb6w5sa"
   },
   "outputs": [],
   "source": [
    "df56.drop(['Total_Price'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oN51YImhxk9u"
   },
   "outputs": [],
   "source": [
    "d57 = df56.sort_values(by=['qty', 'price'],ascending= False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VoSrk__x0mO7"
   },
   "outputs": [],
   "source": [
    "d58 = d57.reset_index()"
   ]
  },
  {
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   "outputs": [
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>STARTECH.COM</td>\n",
       "      <td>16069681</td>\n",
       "      <td>7266737.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DELL CSG SERVICE WARRANTIES</td>\n",
       "      <td>13578833</td>\n",
       "      <td>150917.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TRIPP LITE CONNECTIVITY</td>\n",
       "      <td>8283765</td>\n",
       "      <td>768146.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LOGITECH - COMPUTER ACCESSORIES</td>\n",
       "      <td>8251159</td>\n",
       "      <td>1005042.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JABRA BUSINESS</td>\n",
       "      <td>4829490</td>\n",
       "      <td>392963.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PLANTRONICS INC</td>\n",
       "      <td>3825984</td>\n",
       "      <td>703634.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CONFIG 6</td>\n",
       "      <td>3825896</td>\n",
       "      <td>2140.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BELKIN - CABLES</td>\n",
       "      <td>3508335</td>\n",
       "      <td>262931.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DELL ESG WARRANTIES</td>\n",
       "      <td>3149689</td>\n",
       "      <td>173018.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>EPOS</td>\n",
       "      <td>3127008</td>\n",
       "      <td>1127406.70</td>\n",
       "    </tr>\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2b80cf88-bee4-423d-b106-a29bd714f9e5 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-2b80cf88-bee4-423d-b106-a29bd714f9e5');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                      manufacturer       qty       price\n",
       "0                     STARTECH.COM  16069681  7266737.29\n",
       "1      DELL CSG SERVICE WARRANTIES  13578833   150917.91\n",
       "2          TRIPP LITE CONNECTIVITY   8283765   768146.62\n",
       "3  LOGITECH - COMPUTER ACCESSORIES   8251159  1005042.67\n",
       "4                   JABRA BUSINESS   4829490   392963.51\n",
       "5                  PLANTRONICS INC   3825984   703634.71\n",
       "6                         CONFIG 6   3825896     2140.78\n",
       "7                  BELKIN - CABLES   3508335   262931.76\n",
       "8              DELL ESG WARRANTIES   3149689   173018.32\n",
       "9                             EPOS   3127008  1127406.70"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d58"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dIAT17ki0aWE",
    "outputId": "389de8b6-dd1f-4285-881f-e01e26156e0f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CCCGEECJP+2C2Fo6JeU5KiqQbCyGEEEKI9NRqFdbW+fTWfTABb0qKTgJeIYQQQgiRbZLSIIQQQggh8jQJeIUQQgghRJ72waQ0vE6r1RAT8wCNJul9T0UIkcMMDY2xti6EgcEH+0+UEEKIf5EP9n+TmJgHmJqaky+fHSqV6n1PRwiRQ3Q6Hc+fxxIT84CCBYu+7+kIIYTIAz7YlAaNJol8+Swl2BUij1GpVOTLZym/vRFCCJFjPtiAF5BgV4g8Sr63hRBC5KQPOuAVQgghhBDiTSTgFf86ixcvwNu7Ea1aNXvfUxFCCCFEHiABr/hXuXfvHmvXrmLlyvUEB+98637u3r1D7dqeaDSaHJydEEIIIT5EEvCKf5Xo6HsUKFAAa2ub9zoPnU5HSkpKto7RarXvaDZCCCGE+Cc+2NeSiZzXrp0Pbdq0Z+fOMG7fvkWjRk357DM/JkwYS2TkSSpUcGXcuMlYWloyatRwIiNPkpiYgKOjE199NYKyZR0AmDBhDKamZty7d4eTJ09QunQZxoyZQPHiJbh79w7t27di//4/MTRMvf38/fvTrFkL7OyKMnz4lyQnJ9GkSR3q12/EyJFjMh0rMTGBxYsD2b9/D3Fxzyhb1pFZs+bj59cPgObNGwAwc+Z8Dh8+xO3bt/j++3EA6ebi79+fihUrc/LkMS5cuMAvv6xFq9Uyc+ZULlw4j5WVFX37DqRRoybKeZqYmHDv3l1OnjzOpEk/UrVq9Vz9mgkhhBDizWSFV6Sxf/9eZs6cz5o1m/j994MMHfoFn332Odu27UanS2HDhrUA1KhRk7VrNxESshsnJ2d++OG7NP3s2bOLXr36sX37XkqUsGfx4vlvHLtq1epMnz6bggULsXv3QUaOHPPGsebNm82FC+dYuHApYWF7+fzzL1Cp1MyfvwSA7dv3sXv3QVxdK2Xp/HfuDGPYsJHs2vUbVlbWDBniR5MmXoSE7GLs2InMmDGZq1evKO13795B9+692bXrAJUquWVpDCGEEELkLgl4RRrt2n2KjY0thQoVpnJlNypUcMXJyRkTExPq1q3PpUsXAGjZsjXm5vkwNjamd+/+REVdJC4uTumnbt36VKjgiqGhIU2benHp0sW3nlNGY6WkpBAaGszgwUMpVKgwBgYGVKxYGWNj47ceq0ULH8qWdcDQ0JDDh//Azq4o3t6tMDQ0xMnJmXr1GrJvX7jSvnbtelSq5IZarcbExOStxxVCCCHEuyMpDSKNV3NnTUxM032Oj3+BVqtl8eIF7NsXzpMnT1CrU9+Z+vTpEywsLACwsbFNc9yLFy/eaj6ZjZWcnERSUiLFi5d4q771KVy4iPL3e/fu8tdfZ/Dyqp9mPs2atdDbXgghhBD/ThLwimzbvXsH//vfb8yatYCiRYsRFxdH8+YN0Ol0bzzW1NQMSM29NTRMDY4fP370VmMVKGCFsbEJt2/folw5pzTH6du4wMzMjISEBOXzo0fpx331uMKFi+DmVoVZsxZkOD/ZIEEIIYT495OUBpFt8fHxGBkZU6BAARISEli06M35uS9ZW1tTqFBhdu7cjlarZdu2rdy+feutxlKr1Xh7t2LevJk8fPgArVbLmTORJCUlYWVljVqt5s6d20r7cuWcOHXqOPfu3SMuLo6VK3/OdK61atXh5s0b7NgRikajQaPRcO7cWa5du5rl8xVCCCHE+ycBr8g2Ly9v7Ozs8PVtQdeu7XFxcc3W8V9/PZI1a1bg7d2Iq1evULFi5bcey99/MGXLOtK3b3datGhIYOBcdLoUTE1N6d69N59/3gcvr/qcOXOaqlVr0LBhU3r27EifPl2pWbNOpvM0N8/HzJnz2LNnF76+XrRq1YzAwLkkJydl63yFEEII8X6pdFn5PfS/wKNHcaSk/D3Ve/euY2dX6j3OSAjxLsn3uBBCiOxQq1XY2lrorZMcXiGEEEKIPCy/pSmmJkZpyhISk3kWm5DBEXmPBLxCCCGEEHmYqYkRnb9elaZs9dQuPOO/E/BKDq8QQgghhMjTJOAVQgghhBB5mgS8QgghhBAiT5OAVwghhBBC5GkS8AohhBBCiDwtT72lQd9rN3LCf+3VHUIIIYQQeUmeCnj1vXYjJ2T11R1794azYsVSdDpISkrEycmZ27dvkZycjEaTzM2bNyhTxgEAJ6fyfPvtaK5du0rXru0ZNGgIn37aRekrKGgRmzdvoGDBQkpfw4ePYsGCOZw+fQqAa9euUKxYcYyNTf7/mBV8+qkvxsbGShnApEnTKVq0GBqNhmXLfiI8fBcmJsao1WqqVKnKwIGDiIw8yfz5swkKWqEcd+VKFF9/PYQNG0LSnatOp2P9+rUEB28GwMjIEGfnCnz++WDy589PdPQ95s6dyYUL51GrVRQvXgJ//wDKlnVUzu/nn5cwdeosatasDaRuI9y6dTNKliydZh5CCCGEEP9Engp436eHDx8yY8ZkgoJWUqSIHTqdjkuXLuDk5AzA3bt36Nu3G8uWrU5zXGhoMB4eVQkLC0kT8ELqtrr+/gEkJyczePBANm5cx1dfDVfq27XzYfz4KUoQ+ZK+MoCJE8eSmJjA0qUrMDfPh0ajITQ0mKSk7G+Vu2RJICdPHmfOnEBsbGzR6XQcOLCP2NinmJmZ8eWX/vj4+DJ+/BQA9u0LJyDAj5Ur12NpaQmAk5Mz27dvUwLeffvCsbeXnbWEEEIIkbMkhzeHPH78EAMDQwoUsAJApVIpwW5GNBoNu3aFMWzYtyQmJnLu3Fm97YyMjHB1rUR0dPRbz+/mzRscOLCP4cO/w9w8HwCGhoa0bt0Gc3PzbPUVHx/P2rWrGDFiFDY2tkDq+dar15DixUuwe/cO8uWzoGPHrsoxDRo0xs2tCps2/aqUubt7cPnyJWJjYwHYvn0bLVq0fOtzFEIIIYTQR1Z4c4ijoxMVKrjQtq037u4eVKrkRrNmLZQAWJ9Dh/5HiRIlKVHCnubNWxIaGsxHH7mkaxcf/5yTJ4/z2Wd+WZrLqFHDlZQGAwMDgoJWcPHiBUqUKKmsrupz7doVevbsrHxOSkrMsJ2xsRElS5bWW3/5chQuLhXTlbu4uCrpGJAaJDdq1JQ9e3ZRrVoNEhIS9K5MCyGEEEL8E7kS8N66dQs/v7+DtWfPnhEXF8eRI0dyY/hcoVarmTTpR65cieLEieMcPLif1atX8Msva7G0LKD3mNDQYJo3T13R9PLyplevLgwa9CUmJqnB6o4doRw9ephbt25QrdrHVKnimaW5ZJTS8CalS5fVm8ObXTqdLsttvby8+eGH73j8+BFeXi2yPZYQQgghxJvkSkpDiRIl2Lp1q/KnUaNGtGyZN391XbasI23bdmDWrAVYWFhw4sQxve0eP37EkSN/snTpYtq188HPrx8JCQn89ttepY2XlzfLl6/h11+3cuVKFJs3b3jreTk5lefWrRtK+sA/Ubp0WZKSkrhx47reekfHcpw9ezpd+dmzZ3BwKJemrHjxEhgZGREcvJkmTbz+8dyEEEIIIV6X6zm8SUlJhISE0LZt29we+p168OA+Z85EKp/v34/myZMYihYtprf9jh2h1K/fiE2bQtmwIYQNG0L45pvvCA0NTtfW1rYggwcPZfnyn0hMfLvXo9nbl6RWrbpMmzaR+PjnAGi1WkJCthAfH5+tvszNzenQoTNTp04gJuYxwP8/tLaf27dv0bhxM549i2Xt2pXKMfv2hXPixDHatu2Qrr8BA/wZOHBQpukfQgghhBBvK9dzePfu3UuRIkVwcUmfq/pPJSQms3pqlzc3fIt+30Sr1RIUtIh79+5iYmKKTpdC374DM3xwLSwsBD+/gDRlderUZ/r0Sdy9eydd+5o1a1OqVBm2bt1Ehw6d09W/6tUcXoARI0bh7FyBUaPGsnTpYnr37oaRkSE6nY4aNWphbGz8xvN7+PABQ4cOVt4y8dlnfqxbt4pBgz4DUgPeSpXccXf3wMjIiJkz5zN37gw2bvwVlUpFsWLFmTlzvt70DlfXSri6VnrjHIQQQggh3oZKl52EyxzQr18/6tSpQ/fu3f9RP2fP/kWxYvIKKyHyqjt3ruPiUuF9T0MIIfKE1/cpeBcLhP9mubrCGx0dzdGjR5k6dWq2j330KI6UlL9j85SUFDSalJycnhDiXyQlJYUHD56972kIIcQHr1Ch/HrL89q/sWq1CltbC/11uTmRzZs3U69ePaytrXNzWCGEEEII8R+W6wFvXntYTQghhBBC/LvlakrDzp07c3M4IYQQQgghZGthIYQQQgiRt0nAK4QQQggh8rRcfw/vu2RdwBjDV94/m1M0SYnEPE3KtE27dj4YGxtjZGRMQsILypQpS5cuPahYsTKQ+t7dOXN+xM7u740oqlf/mIEDBxEWFsIffxxk/Pi0b684fjyC+fNnp9nuNyO7dm1nzZoVJCYmYmpqhr29PQMHDsbOzo6NG39l69aNqFRqkpOTqFmzDv7+qe8Ajo9/zuLFgRw69D9MTExQqVSUK1ee/v0/p3DhIunGyayv2rU9cXBwRKX6++eoJUuWY2RkpNSBCtDRr99AateuR9eu7fH3H0KNGjWVY+Lj4/H1bc7y5WsICwvhxYsXyhg3blwnMHAuUVGXsLS0xNjYiE6dulO3bn0mTBhDRMSRNBtY9OjRmwYNGqc5h4ED+9Cu3ac0atQUgHnzZrFjRyjbtu0GUt+p7O3diKCglRQvXoLY2Fh8fZvTqtUnBAQMVfp59Wuq0SRTtGgxRoz4DlvbggD4+/cnOjqafPnykZiYQLNmLejZs2+aufTr14Pk5GTl/cYAd+/eoX37Vvj6tmXo0G+Usr59uxEaukc5RqNJ5ubNG5Qp4wCk7qbXq1c/pR38fV+++l7mSZOmU7RoMfbuDWfFiqXodJCUlIiTkzNjxkzQf4MJIYQQH7A8FfAaGptwbGrfNzfMJo+vfwIyD3gBxo+fQtmyjgD89ttehg0bzI8/zsPFxRUAT89q6YLanBASsoV161YxadKP2NuXBFKD5cePHxIT84j169ewZMkv5M+fH61Wy9WrV4DUzSKGDQugTBkHfvllHSYmJmi1Wnbv3sHdu3fTBbznzp3NsK+XAgOXYm5urneeL+sOHfqd77//hu3b99KihQ/bt4ekCXj37Qvno48qpNul7uHDh/j79+fzz79g0qTpADx69JAjR/5U2nTt2oO2bT/N9HpVqeLJiRPHlID35MnjFC1ajCtXLlO2rAOXLl3A3DwfxYuXAGD37h24uLgSHr4TP7/BGBkZKX29/JrqdDrGjPmWn39eogSpAAEBQ6lVqw4PHz6ka9d2VK1aQ7kfrly5zOPHjzAyMuL8+XM4O3+kHGdmZs7Bg/vp1KmbMo+XlixZDvwdBL8eLL/u1fvy1Ws5Y8ZkgoJWUqSIHTqdjkuXLmR63YQQQogPlaQ0vCP16jWkdeu2rFnz5tXZf+rnn5cwaNCXSrALqUFdhQqu3L9/n3z5LDAzMwPAwMAAR8dyAEREHOHu3TsEBAzFxMREqffy8qZyZbd042TWV3ZUqeLBixfxPHv2DC8vb/7443diY2OV+rCwELy9W6U7btOmX3F398DLy1sps7UtSPPmLbM1vru7BydOHANSV7gTExNo2LCJUnb8+DHc3T2U9qGhwfTo0QcHh3IcPPib3j5VKhVubh5ER9/TW1+wYEHs7UulqQ8NDcbLyxsvL+90W0obGxvRsWM3Fi9ekK1zy6rHjx9iYGCorIarVKoMdwUUQgghPnR5aoX336ZCBVd+//2A8jki4gg9e/69LXDbth3w8fH9R2PExDzm/v1oKlRw1VtfrVoNVq1aTrt2Pri5VcHd3YNmzVpgamrKxYvnKV/eGUPDrN0GmfX10sCBvZWUhiJFijBlysx0/fz22348PKoq72P28PAkPHwnbdq059atm1y+HEW9eg3SHXfx4nmqVauR6RxXrlxOSMhW5fPIkaMpV658mjYVK1bi7t07PH78iAsXzlGxYmXc3NxZteoX2rbtwMmTx6hbN3X8qKhLxMY+xcOjKo8fPyI0NJiGDdOmSAAkJydz6NDvNGzYRO+8bty4TmzsUyWQ1mg07N69g8DAIAwNDenduwv+/gHKDx4Abdq0p3Pntly6dAELC/0vDc+KV49287gAACAASURBVLeaNjAwIChoBY6OTlSo4ELbtt64u3tQqZIbzZq1SJMOIoQQQuQVEvC+U2l3bX5XKQ2ZMTMzY9Ginzl//i9OnTrBtm1b2LRpPT/99Eu6tkePHmb+/NnExz/H17cdnTt3y3JfL3/Nn1lKw8CBvYmPj+fRo0fMmROolHt7t2L58qW0adOesLAQGjduiomJqd4+3iQrKQ0mJqZ89JELJ04c48KF87i7e+Dk5MylSxfQaDRERp4kIGAYANu2bcXLyxuVSkW9eg2YOXMaDx7cp1ChwsDfP8TcvXubUqXKpAt4Z82aTmDgXG7cuIa/f4AS5P/++0Hs7Usq6QrlypXnwIF9NGni9co8TejZsy+LFs3nq69GvNX1AP0pDWq1mkmTfuTKlShOnDjOwYP7Wb16Bb/8shZLywJvPZYQQgjxbyQpDe/QuXN/KQ8UvSvW1jYUKlSYc+fOZthGpVLx0UcudOzYlQULgoiOvsuVK5dxcirPxYupQR5A1arVWbZsNXXrNuDFi/hs9ZUVgYFLWb8+mL59BzB69LckJiYC8PHHtbl/P5qoqEvs2BGqN50BwMnJmb/+yvg8s+NlHu/Jk8dxd/fAwMCA4sXt2b17BxYW+SlWrDjJycmEh+8gNDSYdu186NKlPRqNhrCwEKUfT89qLFu2ms2bw1CpVAQFLUwzTkDAUFau/JV58xazaNF8Ll+OAlLTGa5du0K7dj60a+dDVNTFdGkNAC1a+HDnzm1OnTqRI+f9urJlHWnbtgOzZi3AwsJCSesQQggh8hIJeN+Rgwf3s2XLBjp27PrOx+rRow9z587g9u1bStnJk8f5668zXL9+jStXopTyGzeuk5ycTOHChfH0rE7hwoWZO3eGEnwCJCQk6B0ns76yo1OnrtjY2LB160YADA0NadasBZMnj8PCwoKPPnLRe1ybNu05duwou3btUMpiYh6zffu2bI0PqXm8hw8fIj4+noIFCwHg5ubO8uVLlbSDgwd/w96+FJs3h7FhQwgbNoQwc+Y8veOZm+dj2LBv2bx5Aw8fPkxXX6mSG23adOCnnwJ59OghJ08eZ/36YKXfjRtDOX/+HPfupc0BNjAwoF+/gQQFLcr2OWbmwYP7nDkTqXy+fz+aJ09i0j0oKIQQQuQFktKQg0aNGq68lqx06TJMmzZbeSIf0ufwOjt/xIgR3wFw6NDvfPJJC6WuRQsfPDyqcvnypTTlnp7VGDlyTJpxfX3bYmJiwqhRX5OYmIharcbBoRyff/4FT548Yc6cH4mJeYyxsQlqtZrvvx+HtbUNANOnz2Hx4gV069YBMzMzzM3NsbcvRbNmLXhdQkJCpn1B2hze1P5nKwHlSyqVCj+/AEaP/pbWrdtgYmKKt3cr1qxZwaBBQzK8vgULFmLevMUEBs5hyZJAzMxMMTMzp2vXHkqb13N4fX3b4OvbLl1fLi4VefToYZrzdHOrwuLFC+jevReQugrbtGnzNMe5ulYiJSVF70qoo2M5GjRozKpVyxk8+Kt09d2796ZjR1+2b99GjRo1MTfPp9SZmJhQt259wsKC0z2El9rnL8THP8/w2mTm1RxegBEjRmFlZU1Q0CLu3buLiYkpOl0KffsOlAfXhBBC5EkqnU6ne3Oz9+/RozhSUv6e6r1717GzK5Wmzft8D68QImfp+x4XQgiRfYUK5afz16vSlK2e2oUHD569pxm9G2q1CltbC711eWqFNzUolcBUCCGEEEL8TXJ4hRBCCCFEniYBrxBCCCGEyNMk4BVCCCGEEHmaBLxCCCGEECJPk4BXCCGEEELkaXnqLQ2WBUwwMTbO8X4Tk5KIfZr45oZCCCGEEOJfJ08FvCbGxvT8eXCO97us12wg44C3X78eJCcno9Ekc/PmDWU7YSen8vTq1Y+OHT+hTBkHdLoUDA2NCAgYSsWKlQEIClrEixcv8PcP4PjxCIYNG4y9fSm0Wg22tgUZPnwURYsWY8KEMUREHKFAASsSExOoW7cBAwcOSjeXV/t7VVhYCH/8cZBevfozbtz3AMTGPiU+/jl2dqm7a/n4+GJmZsacOT8qZQDVq3+sdyyNRsOyZT8RHr4LExNj1Go1VapUZeDAQRgaGnLlShTz5s3m9u2bpKToKF++PIMGfUmRInYATJgwhj17drF69Sbs7P4uc3b+iLZtPyUsLISJE8cyduxEGjVqmuY8xo+fyt27d5Rr+1KBAlbMnr0AgEePHrJgwRwiI0+RP78FBgYG+Ph8QkzMY/bt2wPA7ds3sbKyIV++1E0gpkyZwbhx39OpUzcSEl6wbt1qFi9elu4aR0ff49tvR1O7tie7dh1g3LjvuXv3DgBRURdxcHBEpVJjY2ODVptC/foN+eST1A0wkpKS6N27K35+X/Dxx7UzvK+EEEIIkTPyVMD7vixZshyAu3fv0LdvN5YtW63U3b17BwsLC6Vs8+YNTJ48jlWrNujtq3TpsgQFrQBg7twZzJ07k4kTpwHQtWsP2rb9lLi4OHr16kzFipWoXbtetubq4OCozOXV4PGlsLAQPD2rpSnLyMSJY0lMTGDp0hWYm+dDo9EQGhpMUlIS8fHxBAT4ERAwjIYNGwOwbt0qvvzSn+XL12JomHrr2djYsnTpIr79drTeMezsirJkyULq1WuoHPOqV6/tqxISEvD370+LFj6MHDkGtVpNXFwc4eE76dGjDz169AHA378/nTp1o1atOun6qFOnPj/+OIXr169RqlRpAHQ6HTt2hDJq1Ng0bSdNmq78vXZtTwIDl2Jubg7AvXt3GTCgNzVq1KRo0WIsXboYV9eKEuwKIYQQuURyeHOZu7sH0dH3stTW07MaN25cT1duYWGBs3MFvXW55ebNGxw4sI/hw79Ttsg1NDSkdes2mJubs3HjOtzdqyjBLsCnn3bB3Dwf4eE7lTJf37YcPXqYq1ev6B3H2fkjSpYsxbZtW/XWZ2T37h1YWhagW7deqNWpt7mFhQW+vm2z3IexsTFNmjQjNDRYKTt+PAIDA0MqV3bPcj92dkXp0+czJk36gXPnzrJ37+5Mt1AWQgghRM6SgDeXHTiwT/n1fGZSUlLYv38vTk7l09U9fPiA06dPUa5c+rqcEBFxhJ49Oyt/QkK2pGtz8eIFSpQoiaWlpd4+Ll++hItLxXTlLi4ViYq6pHw2MzOja9eeLF68IMP5fPaZH7/8spTExIR0dXFxcWnmOm3aRAAuXDhPhQqubzzXN/H2bsXOnWFotVoAwsKC8fb2yXY/Pj6+GBkZM2SIP19/PZJ8+fRvfSiEEEKInCcpDbngZVD25EkMSUlJSgqEPteuXaFnz87odDocHR3TrASuXLmckJCtGBgY0Llzd6pWrf5O5pvVlIbM6HS6LLdt3boN69at4uzZM3rrHRwccXOrwoYN67C2tklTl1FKQ05xcnLGxsaGw4cPUbmyGwcPHmDgwC/eqq9Onboyf/4sPD2r5fAshRBCCJEZCXhzwcugTKvVMnfuDMaMGcnixctQqVTp2r6aw/u6lzm8/wZOTuW5desGsbGxeld5HR2dOHv2dLryv/46ozy89ZKhoSF9+nzG4sXzKVy4iN7x+vYdwIABvenatWeW5le+vHOaVIR/wtu7FWFhwTx8+IDKld0oWLDQW/WjVqtRqw1yZE5CCCGEyDpJachFBgYGDBz4BY8ePeTgwd/e93T+EXv7ktSqVZdp0yYSH/8cAK1WS0jIFuLj42nbtgPHjx9j795w5Zh161YRF/eMJk280vXXpIkXT57EcPLkcb3jFStWnPr1G7J+/Zosza9x42Y8eRLD6tW/KKvNz5/HsXXrpuyeKk2bNufIkcNs3LgOb+9W2T5eCCGEEO9XnlrhTUxK+v9XiOV8vznFxMSEfv0G8vPPi6lTJ3tvWMiqrVs3sWfPLuVzjx59MM7G+4lf5vC+5Oz8ESNGfJeu3ahRY1m6dDG9e3fDyMgQnU5HjRq1MDY2xtzcnJkz5zN//iwWLpyLTgflyjkxY8Y8vW9bUKvV9O/vx/DhGT/M1bNnX7Zv35am7GW6yKuWLVuNmZkZ8+YtZsGCOXTo0BozMzMMDQ1p06Z9lq/DS5aWBahRoybHjx/N9lsxhBBCCPH+qXTZSbZ8jx49iiMl5e+p3rt3HTu7Uu9xRkKId0m+x4UQImcUKpSfzl+vSlO2emoXHjx49p5m9G6o1SpsbfU/FC4pDUIIIYQQIk+TgFcIIYQQQuRpEvAKIYQQQog8TQJeIYQQQgiRp0nAK4QQQggh8jQJeIUQQgghRJ6Wp97Da5XfGCNTkxzvNzkhkSfPMn8Xb7t2PkydOpOyZR3TlEdH32Pu3JlcuHAetVpF8eIl8PcPSNPuyJE/WbbsJx48eIClpSVqtZpPPmlHixY+ANSu7cmuXQcYN+577t69A0BU1EUcHBxRqdTY2NgwY8Y8atf2VMpeWrJkOUZGRsTHP2fRovn8+ecfmJmZo1arqV+/Id279yYsLIQ//jiYZjvh338/yJo1K5g3b3G2r1dmYwGcOnWChQvnERMTg1arxd29Cv7+Q5Qd2/z9+3P27Gm2bt2BpWUBAI4fj+CLLwbQsWNX/P0DOH48gmHDBmNvXwqtVoOtbUGGDx9F0aLF8PfvT6dO3ahVq44yp1GjvqZmzTo8eHCfffv2AHD79k2srGzIly8fAFOmzGDcuO+Jjo5WygC++mo4FStWxt+/v1KXmJhAs2Yt6Nmzb6bXol+/HiQnJ6fb/vjcubMsWjSfO3duY2lZAHNzc3r37o+bWxV0Oh3r168lOHgzAEZGhjg7V+DzzwcTF/eMjh0/oUwZB6WvAgWsmD17ATqdjqCgRezfvxcDAwO0Wg0tW7amY8eumdbBm+/ToKBFbN68gYIFC5GUlIiTkzPDh4/CzMwMSHv/T5gwhoiIIxQoYKXMsUeP3jRo0JhLly4ye/Z04uLi0GiSsbDIz8SJ07Cxsc3SvSWEEEK8jTwV8BqZmhDWvVeO99vil5/hDQGvPhqNhi+/9MfHx5fx46cAsG9fOAEBfqxcuR5LS0uOHPmTSZN+YNy4Kbi6VgTg7t077NwZlq6/SZOmK3+vXduTwMClmJubp2mjr0yn0zF06GAcHcuxcuV6jIyMSExMJCRkc7bP6U3eNNatWzcZOXIY48ZNwd3dg5SUFObNm8l3341g9uwFSj9lyjgQHr5L2SgiLCwEJyfnNGO9ug3z3LkzmDt3JhMnTst0fj169KFHjz4AegNjgICAoenKXq97+PAhXbu2o2rVGri4uOpte+XKZR4/foSRkRHnz5/D2fkjAC5fjmLYsAC+++4Hqlf/GIDbt29x6dIFAJYsCeTkyePMmROIjY0tOp2OAwf2ERv7FLVarWxV/bp9+/Zw7NhRgoJWYGJiQlJSErdv33pjXVbuUwAvL2/8/QNITk5m8OCBbNy4LsOtnjPaBnvs2FEMHDhIub43b97A1NRMbx9CCCFETpGUhndo9+4d5MtnoayiATRo0Bg3typs2vQrAD//vIQePfoowS5A0aLF3rhymB0REUe4d+8uX3zxFUZGRkDqjm/t2nXMsTGyOtYvvyzF27s17u4eQOoOa59/PpibN69z6tQJpZ/mzVuyY0coAPHx8URGnqRGjZoZjuvpWY0bN67n+PlkpGDBgtjblyI6+l6GbUJDg/Hy8sbLy5vQ0GClfNWq5bRs2VoJdgGKFy9B/fqNiI+PZ+3aVYwYMUpZ9VSpVNSr15DixUtkOqcHD6KxsrJSdtUzNjamTJmyb6zLyn36KiMjI1xdKxEdHZ3pfDKaY6FChZTP9vYl0/2AJoQQQuS0PLXC+29z+XIULi4V05W7uLhy+vQpAC5ePM+XX36dY2MOHNhbSWkoUqQIU6bM5OLF85Qv76x3S9+XXt9O+Pnz5xQpUiTb479prMuXo5QV1pcMDQ1xcipPVNRFKld2B6BYseIYGxtz7dpVzp49TZ069TEwMNDbZ0pKCvv378XJqXy256vPrFnTWbIkUPk8c+Y8rK1t0rS5ceM6sbFPlcD9dRqNht27dxAYGIShoSG9e3fB3z8AExMTLl48T/36jfQed+3aFYyNjShZsnSG83t9O2UXF1eGDfuWRo2asWXLRjp2/ITKld3x8KhKo0ZNMTQ0zLQuK/fpq+Ljn3Py5HE++8wvwzmuXLmckJCtyueRI0dTrlx5unfvjZ9fP1xdK+HqWonGjZtRqlTG5yqEEELkBAl436G32bV51KivuXnzJjExjwkO3pnt4/WlNGSFp2c1vTm8OS0716R585Zs376Ns2dPM2TI1+zfvydN/bVrV+jZszM6nQ5HR0cGDRoCpK6I6pNR+esyS2mYNWs6gYFzuXHjGv7+AVhbW+tt9/vvB7G3L6msypYrV54DB/bRpIlXluaQmYxSGgoWLMiKFb9y9uxpIiNP8ssvS9m5czszZszNtC6rX5MdO0I5evQwt27doFq1j6lSxTPDthmlNHTp0oNmzVpw7NhRIiKO0KdPV6ZPn4ObW5WsXwAhhBAimySl4R1ydCzH2bOn05WfPXsGB4dyADg5lefcub+UuvHjpxIYGMTjx49ybB5OTs5cvHgBjUbzj/t6+vQJPXt2pmfPznz//TfZHkvfNdFoNFy8eEG5Ji81aNCY8PCdJCQk4OCQ9mFASM3hXbZsNcuXr+G778Ypq7BWVtbExj5N0/bJkydYWekPTrMjIGAoK1f+yrx5i1m0aD6XL0fpbRcaGsy1a1do186Hdu18iIq6qKQ1ODk5c+7cWb3HlS5dlqSkpLdOzzA0NKRyZXe6devF3LmLOHLkkHItMqrLyn0KqTm8y5ev4ddft3LlShSbN294qzkWLFiIZs1aMHLkGLy8vNP9ICOEEELkNAl436HGjZvx7Fksa9euVMr27QvnxIljtG3bAYAePfqybNlP/PXXGaVNQsKLHJ2Hp2c1ChcuzLx5s0hOTgYgKSmJjRvXZbuvAgWsWLZsNcuWreaHHyZle6yuXXuybdsWTpw4BqSmIyxYMJsSJezTrfKZm5vz+edf4Oc3OFtzrFq1Ojt2hJKYmAjApUsXuX79GhUquGT7fDNSqZIbbdp04KefAtPVPXr0kJMnj7N+fTAbNoSwYUMIGzeGcv78Oe7du0enTt0ICdnM0aOHlWPu3LnN/v17MDc3p0OHzkydOoGYmMcA///Q2n7lIbOMnD9/TnmLB8CFC+fJn98SC4v8mdZl5T59la1tQQYPHsry5T+RmJiQ9YsGHDy4H61WC0BiYiLXrl2laNFi2epDCCGEyC5JachBAQF+afJMly9fy8yZ85k7dwYbN/6KSqWiWLHizJw5X3ndVo0aNRk27Fvmzp3Bw4ePsLGxwcjIiGHDvn2rObyawwswffpsChYsxPTpc1i4cD5durTD1NQUgCZNmv+Ds9VPpVJlOlbJkqUYP34qixbN58mTJ2i1GtzcqihvB3hdo0ZNsz2Hli1bEx19j379uqNWG2BiYsLYsRPTvCYrM6/n8Pbt+xm1a9dL165799507OjLxYvn07xBYvv2bdSoURNz879fbWZiYkLduvUJCwumd+/+TJ48kyVLFjBt2kRMTU2xsrKmT58BAHz2mR/r1q1i0KDPgNSAt1Ild9zdPYiLe5Yuhxdg2bLVPH36hB9/nEx8/HOMjIwxNTVl0qTpqNXqTOvUavUb79PX1axZm1KlyrB16yY6dOicrv71HF5f3zb4+rZj3749LFgwB2NjE7RaDZ6e1fWmPgghhBA5SaV7m0TT9+DRozhSUv6e6r1717GzK5Wmzft8D68QImfp+x4XQgiRfYUK5afz16vSlK2e2oUHD569pxm9G2q1CltbC711eWqF98mzpLd6X64QQgghhMi7JIdXCCGEEELkaRLwCiGEEEKIPE0CXiGEEEIIkadJwCuEEEIIIfI0CXiFEEIIIUSelqfe0lDA0gxjk5w/paREDU9jc3YzCCGEEEIIkTvyVMBrbGLIxJFvt91pZr6d0C5L7dq182Hq1JmULetIbGwsvr7NadXqEwIChiptwsJCmDPnR+zsUneXUqtV+PkF4OFRVenD2NgYIyNjNJpkOnbsio+Pr3K8VqulbduWODt/xOTJMzKci79/f6Kjo8mXLx8aTTKlSpXmm29GY2FhQVhYCH/8cZDx46cq7X///SBr1qxg3rzF6HQ6goIWsX//XgwMDNBqNbRs2ZqOHbty9+4d+vbtRmjoHmW+ZmZmLF++FrVane46TJgwhoiII2k2fejRozcNGjTm0qWLzJ49nbi4ODSaZCws8jNx4jRsbGwzrRNCCCGEyI5cC3gTExOZOHEihw4dwsTEBDc3N8aNG5dbw+e63bt34OLiSnj4Tvz8BmNkZKTUeXpWU4LNQ4f+x4wZU1i16u9Affz4KZQt68iVK1H07t2Vjz+uRcGChQA4fPgQBQsWIjLyFI8fP8o0AAwIGEqtWnXQ6XSMHv0tW7ZsoGvXnm+c+759ezh27ChBQSswMTEhKSkp021tX7x4wc6dYTRv3lJvfdeuPfTupjV27CgGDhxErVp1ALh58wampmZvrBNCCCGEyI5cy+GdNm0aJiYm7Ny5k5CQEAYPHpxbQ78XoaHB9OjRBweHchw8+FuG7eLi4sif31JvXdmyjuTPb8mDB/df6Xcrvr5tqFu3Pjt2hGZpLlqtloSEhAzHed2DB9FYWVlhbGwMgLGxMWXKlM2wfe/e/Vm6dAnJyclZ6v/VcQoVKqR8trcvibm5+RvrhBBCCCGyI1dWeJ8/f86WLVv47bffUKlUABQsWDA3hn4voqIuERv7FA+Pqjx+/IjQ0GAaNmys1EdEHKFnz868eBHPkycxTJ06W28/kZEnKVDACkdHJwCePHnCsWMRjBw5hpIlSzN16gQ6d+6e4TxmzZrOkiWBREffo2TJUhmuwL6uUaNmbNmykY4dP6FyZXc8PKrSqFFTDA313y7Ozh9RvrwzmzdvoEOHTunqV65cTkjIVuXzyJGjKVeuPN2798bPrx+urpVwda1E48bNKFWqNECmdUIIIYQQ2ZErK7w3b97EysqKefPm0aZNG7p160ZERERuDP1ebNu2FS8vb1QqFfXqNeCvv86kWaX19KzGsmWrWbduC5Mm/ciYMd+SkJCg1I8aNZxOndrg79+ffv0GKOkQO3eGUqtWHczN81GpkhsajZYzZyIznEdAwFCWLVvNtm27KVvWgcDAuQDKDx2ve/WHkRUrfuXbb0djb1+SX35ZytdfD8n0nPv3H8iqVcuJj49PV9e1aw+WLVut/ClXrjwAXbr0YM2aTXh5eXPv3l369OnKyZPH31gnhBBCCJEdubLCq9VquXnzJhUqVGD48OGcOnWKAQMGsHv3biwsLLLUh61t2nb376sxNMy9t6pldSyVCsLDd2BkZKykHGi1Gnbs2EavXn1Rq1WoVCqlv2rVqqHRaLhx4yoVKrgAMGnSNBwcHNmzZzcTJ/6Am5s7tra2hIWFEBMTQ7t2PgA8fx5HWFgIbm5ueuahwsAgdRxDQzUNGjRi7tyZGBqqsbW1ITY2Ns05PXv2FBsbG6XM0NAYDw8PPDw8aNXKF2/vJjx//gwDAzWgSnOsgYGasmXLUrNmbdavX62UGRqqUalUqNWqDK+fnV0RvL1b4u3dElNTU377bS+enp5vrBN5n1qtplCh/O97GkIIkWf9l/6NzZWAt2jRohgaGtKyZeqv1CtXroy1tTVXr16lYsWKWerj0aM4UlJ0yueUlBQ0mpR3Ml99sjrWb7/tx96+FIGBQUrZmTORjB8/mm7depOSokOn0yn9Xb4cRXz8cwoVslPKtNrUc6tXrxG7d+9i2bKlNG3qxbNnz9i6dYeyEvvgwX26devAF198hampaZp56HQ6tNq/x4mIOEKJEiXRaFJwcqrAlSuXuXDhIg4OjiQmJrBtWzBeXi3QaFI4f/4cBQoUoGjR1DdJ/PXXX+TPb4mZWT5iY58BujTX4+V8e/XqR58+3dBqtUqZTqcjJUWn9/odPLifmjXrYGBgQGJiIlevXqFOnXpoNCmZ1on/hpSUFB48ePa+pyGEEB+8jALbvPZvrFqtSrdA+lKuBLw2NjZUr16d33//ndq1a3P16lUePXpEqVKlcnScpERNll8hlt1+s0Kr1XL6dCRNmzZPU+7qWomUlBROnDgG/J3Dq9PpAB3ffjsGa2trvX0OGOBPnz5def48jsaNm6VJRyhUqDBOTs7s2xeuNz/3ZQ6vRqOhSBE7hg37BgArKyvGjp3I1KkTSExMJCVFS716DWnRohUAT58+4ccfJxMf/xwjI2NMTU2ZNGm68tqxjBQuXIRmzVqwdu3KNOWv5/D6+rbB17cd+/btYcGCORgbm6DVavD0rK68zSGzOiGEEEKI7FDpUqOud+7mzZt8++23PHnyBENDQwICAqhXr16Wj399hffevevY2eVswPxPPHz4kC5d2hIcvBMTE9M3HyCEyNS/7XtcCCE+VIUK5afz16vSlK2e2kVWeN8Fe3t7VqxYkVvD5ar169eyefN6/PwCJNgVQgghhPiXyVM7rb0v7dt3pH37ju97GkIIIYQQQo/ce82BEEIIIYQQ74EEvEIIIYQQIk+TgFcIIYQQQuRpEvAKIYQQQog8LU89tFbA0hhjE5Mc7zcpMZGnsUmZtmnXzgdjY2OMjIxJSdHSo0cfGjduxvHjEcyfP5ugIP1vqIiNjcXXtzmtWn1CQMBQpTwsLISJE8cyduxEGjVqqpT98cdBevXqz7hx3///8U+Jj3+OnV3qJhE+Pr6YmZkxZ86P2NkVQ6NJpmjRYowY8R22tgWB1Bf6r1y5jLCwbRgYpO6G1qFDZ1q1+gSAu3fv0L59K3x92zJ06DdKWd++3QgN3aOc79SpMylb1hGAXbu2s2bNChITEzE1NcPe3p6BAwdjZ2fHxo2/snXrRlQqNcnJSdSsWQd//4B01yIoaBEvXrzA3z+A48cj+OKLtosVZwAAIABJREFUAQwY4E/Xrj0B0l3L+PjnLFo0nz///AMzM3PUajX16zeke/feb/iKCiGEEOK/JE8FvMYmJsz45rMc7/fLSYuAzANegPHjp1C2rCMXL55nwIA+eHpWf+Mxu3fvwMXFlfDwnfj5DcbIyEips7MrypIlC6lXryGGhn9/qRwcHFm2LHUL35dB8PjxU5X6sLCQ/2vvzgNsrB4/jn/unZk7DDOWMZasZc/OpPoSkeyiKLJkjSTxRQjZ1xQRZUmWIqQIo5Skr6+visYSkn3fxtiXZrvn94efmzEzd8a4c4fH+/XX3HOe5ZzzzL3PZ54593kUGlpZI0e+K2OMhg4doNmzZ7rC67x5n2rz5t80Y8YcBQUF6fTpU+rd+035+/urTp36kqSMGQO0fv06vfxyG+XNm89tH1asWKZFi+ZrzJj3lT9/AUk3wum5c2d1/nykvvzyC82cOU+BgYGKi4vTwYMHkh0XSQoOzqHFi79Q48ZNFRgY/ykxxhj16dNDRYoU1eeffyk/Pz9FRUVpxYqlKdo2AAB4cDClIQ0UK1ZCAQEBOnnyeLLLhoUtV9u2HVW4cFGtX/9zvLoSJUqqQIGCWrnymyTWTp7NZlP58pV0+vQpSVJUVJQ++2y23nprgIKCgiRJuXLlVrduPTRr1nTXeg6Hn1q0aKMZMz5Kdh+zZ89U9+69XGFXkipWDNWjj5bWmTNnlClTZmXMmFGS5OPjoyJFiqao7TlyhKhGjWc0f/7cBHWbN/+mU6dO6s03e7v+SPD391ezZtweDgAAxEfgTQPh4ZsVHR2tfPkKuF1u3769unTpoipVekwNGjRSWNjyBMt06dJN8+Z9qqiov1PVlpiYGG3cuEE1az4rSTp+/Kj8/PxUsGCheMuVKlVGJ04c17Vr11xlL7zwonbs2K69e/9Kcvvnz5/TmTOn9eijpROtr1z5Cfn4+KhZs0YaOnSgvvnma/39d8r70rZtR61c+Y3Onj0br3zPnt0qXrxEvCvfAAAAiSHwetCgQf3Url1LzZo1XaNGjUvwb/jbrVz5jerWbSCbzabq1Wto164diog4E2+ZwoWLqHz5ilqyZNEdtWXz5t/Url1LNWxYSxcunHcF3uQfJP3PAv7+/mrXrpOmT596R/u+VcaMGTV9+myNGfOeSpQoqZUrl6lLl/aKiYlJ0frZswfrueee15w5n6S6DQAA4MFG4PWgkSPHac6cBZo6daYee+wJt8vGxMRozZrvFBa2XM2aNVKrVi8qNjZWq1atSLBsp06vadGiBbpy5UqK2xIaWllz5izQ0qWrZLPZNGvWNElSvnz5FRMTo8OHD8VbfufOP5QnT14FBGSKV16/fiOdOHFc27ZtSXQ/2bJlV0hITv35584k22Kz2VSyZCm1aNFaH300S6dPn9SBA/tT3JeWLV/R+vU/6cSJY66yYsVKaM+evxQbG5vi7QAAgAcTgTedrF//s/LnL6ilS1dpyZIVWrJkhSZOnKJvv12ZYNmHHsqrp5+uqS+//OKO9xMQkElvvTVAS5cu0dmzZ+Xv769Wrdpq/PjRunTpkiTp9OlT+uijSerQ4dUE6/v4+OjVV7vGm997u7ZtO+rDDyfo+PF/AunWreHatWuHDh8+pAMH9rnKjxw5rJiYGOXMmTPFfcicObOaN2+tuXM/dZWFhlZWzpw5NWXKB66rxdHR0frqqzu7Eg4AAKyPCZBesH//Xj3/fH3X69DQyjp37pxq164Xb7nSpcvK6XRqy5bfE2yjXbtOiYbhlChSpKhq1Kil+fPnqkeP3mrXrpPsdrs6d24rHx8fSdKLL7ZQvXoNE13/xrrzdO3a1UTrmzRpKn9/fw0a1FdRUVGy2+0qXLioXn/9TV24cEGTJ7+v8+fPyeHwl91u1+DBI5QtW/Y76kPTpi9pyZKFrtc2m03vvTdZ06ZNVatWzZQhQwZJ0rPP1ktqEwAA4AFlMyb5WZ33gsjIK3I6/2nqqVOHlTt3wXjLpOd9eAF4VmLvcQDAnQsJCVTLvvPjlS14t5UiIi6nU4vSht1uU3Bw5kTrLHWF90YoJZgCAADgH8zhBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKVZ6i4N2bJklK/D812KjY7V+YvXPb5dAAAApD1LBV5fh6+2fbTO49st9/rTyS4TGxurOXM+0Zo138vf3yG73a6KFR9T167d5evrqwMH9mnKlEk6fvyonE6j4sWLq3v3XsqVK7ckadSoofrxx++1YMHXyp37n7ISJUqqadPmWrVqhSZPfl+5cz/k2ufw4aNVoEAhVa0aqu+//48CAgJkjNFXXy3SN998rbi4OPn7+ytbtmC1b99JZcqUS9Bup9OpWbOm68cff5DD4adcuXJr/PhJnhk4AACAe4ClAm96Gj16mKKi/tann36mgIBMio2NVVjYckVHR+vatWvq2bObevZ8SzVr1pIkLVo0X716vaG5cxfK1/fGYciePViffjpdAwYMSXQfoaGVNXLku27bMXPmx9q6NVwTJkxRSMiNx/f+8cc2HT58KNHAu3jxAh05cliff75Yvr6+Oncu8m6GAQAA4J7DHF4POHr0iP7zn5/Ur987CgjIJEny9fVV48YvKCAgQF99tUgVKlR0hV1Jat68lQICMmnNmtWusiZNmmrTpl918OCBVLXj2rVrWrjwc/XvP8gVdiWpTJlyatiwcaLrLFw4X6+99ka80A0AAGAlBF4P2LPnL+XLV0BBQUGJ1u/fv1elSpVJUF6qVBnt27fX9Tpjxoxq3bqdZsz4KNHtbN78m9q1a6l27Vrq7bf7JKg/dOiAHA6HChQolKJ2X7lyRZcuXdTatWv06qtt1aVLe61fvy5F6wIAANwvmNLgBcaYFC/buPELWrRovnbu3JGgLiVTGm51+fJlde/eRTEx0SpY8GGNHj0+Xr3TGafo6GgZ49TMmXN17NhRvf56Jz3ySBHlzZsvxfsBAAC4l3GF1wOKFSuuY8eO6NKlS4nWFylSTDt3/pGgfNeuHSpcuEi8Ml9fX3Xs2EUzZky943YUKvSIoqOjdfToEUlSYGCg5sxZoNdf76FLly4mWD4oKIsyZgxQ7dr1JUn58uVXsWLFtWfP7jveNwAAwL2KwOsB+fMXUJUq1TR+/Ghdu3ZVkhQXF6cVK5bp2rVratr0JYWH/661a9e41lm0aL6uXLmsZ5+tm2B7zz5bVxcunNfWreF31I6AgAA1b95K48aN1NmzEa7yv/9O+pZqtWrV0a+//k+SdP78Oe3bt1ePPFIkyeUBAADuN5aa0hAbHZuiW4ilZrvJGTRomD79dIY6dGgjPz9fGWP0xBNV5HA4FBAQoIkTp2rq1A80bdqHMkYqWrSYJkyY4vqy2K3sdrs6d+6mfv3+fcdt7dz5dX355Rfq2bObnM44ZcmSVYGBgerQoXOiy3fp0k2jRw/TkiULZbPZ1KVLNxUsWOiO9wsAAHCvspk7mWCajiIjr8jp/Kepp04dVu7cBdOxRQDSEu9xAPCMkJBAtew7P17ZgndbKSLicjq1KG3Y7TYFB2dOvM7LbQEAAAC8isALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAAS7PUfXizZMkgh8PP49uNjo7RxYt/u12mWbNGcjgccjj8FR0dpXLlKqh37/7y9fXVqlUrNHny+8qd+yHX8o8//qS6du2uVatW6H//W5/gkcHh4Zs1deokzZr1maQbT2UbOLCv+vR5W1WqPKWqVUP1/ff/UUBAgN54o7MOHTqoxYu/UUBAgCTpjTc66+WX26hKlafuuL+//rpRc+bM1Pnz55UpU2YFBwerS5c3XE+Fu3Tpkpo0qafnnntePXv2ca13ez/tdpu6deupSpUec43Ru+9OTPBgizfe6KzTp08rU6ZMrrLevfupTJlyCg/frI8//lAxMTGKiYlWcHAOffDBR7Lb+VsNAACkjKUCr8Php/fff9/j2+3du7ck94FXkkaOHKdHHimiuLg4dev2qn7+ea2eeaa2JCk0tHKCUJtSv/++SSNGDNaQISNVoUKlRJfJkCGDFi78PMkHTKTUb7/9orFjR2jMmPdUosSjkqS9e/9SZORZV+D94YfvVKpUaa1Zs1rduvWQn98/f2Tc2s+NG/+rCRPGaf78Jcnut2fPPgnCeWxsrAYO7KsPP5yuIkWKSpL27Nktm812V30EAAAPFi6TpYHo6GhFR0cpMDDorrf13//+RyNHDtHYse8nGXYlqXXrdlq6dIkuXLhwV/ubPXum2rbt6Aq7klS0aHFVrvyE63VY2HK1bdtRhQsX1fr1Pye5rStXrtzVGFy7dk3Xr19T9uzZXWXFipUg8AIAgDtiqSu86W3QoH5yOPx1/PgxVa78eLyQuHnzb2rXrqXrddOmL6lRoyZut3fs2BENH/6Opk37VI88UtjtsiEhOVW3bgPNmzdLb77ZO9V92LNnt3r16ptk/b59e3Xp0kVVqvSYzp2LVFjYctWsWctVf7Of169f04UL5/Xuu5NStN8PPnhPM2d+7Ho9ceIUZcuWXc8997xatHhB5ctXVJky5VS7dl3lypU71f0DAAAPHgKvB92c0hAVFaVBg/pq8eIFeumlGyE3NVMagoNzKFOmzPrmm6/0738nHUJvat26rVq3fknNm7dKVftTYuXKb1S3bgPZbDZVr15DEyeOV0TEGYWE5JQUv5/h4Zs1dOgAffHF18qQIYPb7SY2pUGSevXqp+bNWyk8fLN++WWDPv98tj755DPlz1/A850DAACWxJSGNODv769//espbdr0611tJ2PGAE2cOFW7du3QxInJh+UsWbKqadOXNGvW9CSXmTt3ltq1a6l27VoqPHxzgvpixUpo166dia4bExOjNWu+U1jYcjVr1kitWr2o2NhYrVq1ItHlK1YMVWxsrA4e3J9s293JmzefGjVqolGjxqt06XLasOE/d7U9AADwYCHwpgGn06mtW3/3yFXIzJkza+LEj1Iceps3b6XffvtFJ04cT7S+bduOmjNngebMWaCKFUMTrZ87d5b++mu3q2zfvr367bdftH79z8qfv6CWLl2lJUtWaMmSFZo4cYq+/XZlovvav3+frl27Gu/uFHfi2rVr+u23X2SMkSRdvnxZJ08eV548eVO1PQAA8GBiSoMH3ZzDGxsbo4cfLqx27V511d0+h7dEiZLq3/8dSdLGjRv0/PP1XXX16zdy3cpLuhF6J0yYqn//u5smTnzX7fSGjBkzqk2bdpo4cXyq+vDEE//SW2+9rQkTxunixYvy9fXVQw89pNdee0OLFi1Q7dr14i1funRZOZ1Obdnye7x+3gipRgMGDFW2bNlcy/fs2U0+Pj6u13PnLpSUcA5vp05dVLFiqL7+erEmTnxXDoe/4uLiVLt2PVWvXiNVfQMAAA8mm7l5+eweFxl5RU7nP009deqwcucuGG+Z9LwPLwDPSuw9DgC4cyEhgWrZd368sgXvtlJExOV0alHasNttCg7OnGidpa7w3gilBFMAAAD8gzm8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0rz2pbWaNWvK4XDI399fktSnTx899VTCJ2sBAAAAnuTVuzRMnjxZxYoVS7PtZ8vqL18/h8e3GxsTrfMXojy+XQAAAKQ9S92WzNfPof+sHOrx7VZrOFSS+8DbrFkjORwO+fk59Pff1/Xww4+oVau2KlOmnCRp1aoVmjz5/XhPHXv88SfVtWt3rVq1Qv/733qNHBn/SWrh4Zs1deokzZr1mdt9jxo1VJs3/6YsWbK6ytq27aAaNWpp7949mjTpPV25ckWxsTHKnDlQo0ePV/bswZKkH374TgsXztfVq1cUEJBJAQEBevnlNqpSJf7V902bftUHH4zX/PlLJElXrlxRgwbP6M03e6tp05ckSQsWfKb9+/fonXdGSJI++miyFi9eoKVLVylbtuyJjlVsbIxatGitRo2auPr81ls9lD9/QcXFxSpLlqx6660BKliwkGv9jRs36K23emjUqPHxHkIxatRQ/fjj91qw4Gvlzp3bVVaiREllyJBRX3554yEXp0+fUoYMGVzj9dZbA7Rs2RKVKFFSTZs2d3usLl26pPffH6sDB/bJZrPJbvdR9+7/jvegEAAAcG/xauDt06ePjDGqVKmSevXqpaCgIG/uPs2NHDlOjzxSRJL0889r9dZbPfT++1NUqlRpSVJoaOUEodZTWrduq6ZNmycoHzZskLp27e4KsEePHlGGDBklSStWLNOiRfM1atR4V6A8cGCfNm/+LcF2ypYtp5MnT+jcuUhlzx6s7du3qnjxktqy5XdX4N269XdVq/a0JCkuLk6rV69S2bLl9d13q/Tyy63jbe/mWB04sE8dOrTWk09WUY4cIZKkQoUecYX8jz6arMmTJ+j99ye71g0LW65KlR5TWNjyBE9dy549WJ9+Ol0DBgyJV96gwXNq0OA5Sf+E4FvHa9myJfGWT+pYzZz5sXLmzKWhQ0fJZrPp4sULun6dez8DAHAv81rgnT9/vvLkyaPo6GiNGjVKw4cP13vvvZfi9W9/csaZM3b5+nrvO3cp2ZePzz9teuaZWtq9e5cWLfpco0e/K7vdJpvNluh2kqrz8bHLZkt+3zeuNCa+7YiIM8qdO5er7uGHC7nqPv10hgYOHKzChR9xlRUrVizRaSe+vgEqWfJRbdsWrmefraNt28L10kstNGvWDPn62hUXF6ft27eqd+++8vW1a+PG9cqXL586d+6qd98drTZtXkl0rIoVK6bAwCCdO3dWuXPnStDn0NDHtHHjf12vL168oPDwTfrii6/08svNdPHiOQUH53CNwwsvvKgvv1yoo0cP6eGHH0l0bJIrc3eszp49o4oVK8nP78bjkYODsydYBp5ht9sVEhKY3s0AAMt6kD5jvRZ48+TJI0lyOBxq2bKlunbtekfr3/5oYafTqdhYp0fb6E5K9hUXF79NJUqU0vr1Pys21imn02jTpl/VunULV33Tpi+pUaMmcjqNjDEJ9hEX55Qxye/bGKN58+bom2+WucoGDhyiokWL65VX2uu11zqqdOmyKl26rGrVqqOCBQvp/Plziog4o+LFS6V4HCtUCNXvv29WjRrPKjz8d734Ykvly/et9uzZq+joKGXKlFm5cj2k2Finli//RvXqNVLp0uUUHR2jbdu2u6503zpW27dvVZYsWfXww0UUG+uM12en06mff/5JzzxT29XGsLAwPflkVWXJkk3Vq9fQihXL1bp1O9c4+Pv7q3XrdvrooykaM+Y9GWPkdMYf2+TK3B2rpk2ba9Cgfvr+++9UunQ5Va1ajekMacTpdFrusZcAkB6SCrZW+4xN90cLX7t2TXFxcQoMDJQxRqtWrVLJkiW9set0ZuK9So8pDa1atVWdOvX1+++btHnzb+rYsbXee29yvDmxN732WgddvXpF/v4Z9Mkn8xLUV6hQSRMmjNO1a1d1/fp15ciRQ+XLV9SWLb8rKipKFSpUkiSdP39OW7b8rnfeGSZJqlevgcLCvokXeAcN6idjjI4fP6YRI8bKz8/PVXfo0AG1a9dSZ8+eUUBAJs2cOddVt2rVCnXv/u//325DjR07whV4b2rc+AUtWjRfO3fuSPkA3iapYxUaWllff71S4eGbtX37Vr3zTn+9/HIbtWnTLuFGAADAPcErgTcyMlLdu3dXXFycnE6nChcurCFDhiS/4n3uzz936eGHC6d3M5QjR4jq1KmvOnXqy9/fX+vW/aiePd9SSEhO7d69U4899oQkadq0T3XgwD717fvvRLdTunRZnTx5UuvWrXV9Ga9cuYpauPBzRUdHqXr1mpKk775bpbi4WL3yyo0rpHFxcbp+/bp69Ogtf/8Mkv6Zw7t27RqNHj1MZcqUc32R7uYc3piYGA0ZMkDvvTdWI0aM1e7df+rgwf0aM2a4q01nz0Zo+/atKlu2vKvM19dXHTt20YwZU5UzZy4Pj6YUEJBJVatWV9Wq1VW8eEnNm/cpgRcAgHuYVybB5s+fX8uWLdOKFSsUFhamyZMnK2fOnN7YdbpZv36dli1bohYtWie/cBq3Iy4uTpIUFRWlQ4cOKk+eG3cfaNeukyZPnqAjRw67lnf3BSx/f389+mgpzZs323U1t0SJkvrrrz+1fftWVawYKklatWq5Ro9+T0uWrNCSJSu0dOkqPfpoKf30048JtlmzZi099tgT+uyzOQnq/Pz81KdPf/3660bt2bNbYWHL1apVW9d2lyxZoY4duygsbHmCdZ99tq4uXDivrVvDUz5YKbBp0y+6evWKpBvTIPbs+cs1ngAA4N5kqduSxcZE//8txDy/3ZQYNKif67ZkhQo9rPHjJ8X7N/7mzb+pXbuWrtclSpRU//7vSLpxq63nn6/vqqtfv5EqVXpM+/fvjVceGlpZAwcOTbDvzz+fqxUrvnG9btLkBTVp0kw//fSjPvposhwOf8XFxSo09HHX1IfGjV9QhgwZNHToQF27dlXZsmWTv38G15SBxFSoUEmzZ89UhQoVJd24mpo3bz4dO3ZUefI8pJ07d+jSpUsJ5rU++2xdhYUtV926DRJs87XX3lDHjq3VqlXbBHXZswfr5Zdba9q0qfrzz536+ONZCbbbtm0L9ez5Vrxyu92uzp27qV+/pPviTlLHat++ffrww4ky5sZ0lXz5Cujf/+6bqn0AAADvsJmbZ+573O1fWjt16rBy5y6Yji0CkJZ4jwOAZ4SEBKpl3/nxyha82+qB+tKa9+7rBQAAAKQDAi8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAszVL34Q3KmlH+fp7vUlRMrC5duJ7scs2aNZLD4ZDD4e8qGzPmPXXv3kUOh0N+fg45nXFq27ajatWqI0k6cGCfpkyZpOPHj8rpNCpevLi6d++lXLlyS5L27t2jSZPe05UrVxQbG6PMmQM1evR411PJAAAA4J6lAq+/n696Lf3Z49ud8Hz1FC9785G5SZXv2bNbr73WUaGhj8tut6tnz27q2fMt1axZS5K0aNF89er1hubOXShfX18NGzZIXbt2V5UqT0mSjh49ogwZMnqmYwAAAA8ApjR4WbFiJRQQEKCTJ4/rq68WqUKFiq6wK0nNm7dSQEAmrVmzWpIUEXFaISEhrvr8+QsoICDA6+0GAAC4X1nqCu+9YNCgfq4pDT4+Ppo167N49eHhmxUdHa18+Qpo//65Klu2fIJtlCpVRvv27ZUkvfJKB3Xr9qpKly6r0qXLqlatOipYsFCa9wMAAMAqCLweltSUhptBOFOmTBo1apwCAwOVkqc6t2rVVnXq1Nfvv2/S5s2/qWPH1nrvvckqX75iWjQfAADAcgi8XpJYEC5SpJh27vwjwbK7du3Q8883c73OkSNEderUV5069eXv7691634k8AIAAKQQc3jTUdOmLyk8/HetXbvGVbZo0XxduXJZzz5bV5K0fv06xcXFSZKioqJ06NBB5cnzULq0FwAA4H7EFV4Pu3UOryT17z8oyWWDgrJo4sSpmjr1A02b9qGMkYoWLaYJE6bI1/fGofnppx/10UeT5XD4Ky4uVqGhj6tp0+Zp3g8AAACrsJmUTCS9B0RGXpHT+U9TT506rNy5C8ZbJr3vwwvAcxJ7jwMA7lxISKBa9p0fr2zBu60UEXE5nVqUNux2m4KDMydaZ6krvIRSAAAA3I45vAAAALA0Ai8AAAAs7b4OvPfJ9GMAd4j3NgDAk+7bwOvr69DVq5c4MQIWY4zR1auX5OvrSO+mAAAs4r790lq2bCE6fz5CV65cSO+mAPAwX1+HsmULSe9mAAAs4r4NvD4+vsqRI096NwMAAAD3uPt2SgMAAACQEgReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWBqBFwAAAJZG4AUAAIClEXgBAABgaQReAAAAWJrXA++UKVNUvHhx7dmzx9u7BgAAwAPIq4F3586d2rp1q/LmzevN3QIAAOAB5rXAGx0dreHDh2vo0KHe2iUAAABw54H35MmT2rp16x3vaNKkSXruueeUL1++O14XAAAASC3flC544sQJ9erVS7t375bNZtOWLVv03Xffaf369Ro1apTbdbds2aIdO3aoT58+qW5ocHDmVK8LAACA+EJCAtO7CV6T4sA7ePBgPf3001qwYIEef/xxSVKVKlU0bty4ZNfdtGmT9u/fr2eeeUaSdOrUKXXs2FFjxoxR1apVU7T/yMgrcjpNSpsLAAAAJR1sIyIue7klactutyV5gTTFgfePP/7QjBkzZLfbZbPZJEmBgYG6fDn5wercubM6d+7sel2zZk1NmzZNxYoVS+nuAQAAgFRJ8Rze4OBgHT58OF7Zvn37lCdPHo83CgAAAPCUFF/h7dChg1577TV17txZsbGxWrlypaZPn65XX331jne6du3aO14HAAAASI0UB95mzZopa9asWrRokfLkyaNly5apR48eqlWrVlq2DwAAALgrKQ68klSrVi0CLgAAAO4rKZ7DO3LkSIWHh8crCw8PT/aWZAAAAEB6SnHgXblypUqXLh2vrHTp0lq5cqXHGwUAAAB4SooDr81mkzHx74MbFxcnp9Pp8UYBAAAAnpLiwBsaGqoPPvjAFXCdTqc+/PBDhYaGplnjAAAAgLuV4i+tDRw4UF26dFHVqlX10EMP6eTJkwoJCdG0adPSsn0AAADAXUlx4M2dO7eWLl2qbdu26dSpU8qTJ4/Kli0ruz3FF4kBAAAAr7uj25LZ7XZVqFAhrdoCAAAAeJzbwFuvXj19++23kqTq1atHwC61AAAfv0lEQVTLZrMluty6des83jAAAADAE9wG3hEjRrh+Hj9+fJo3BgAAAPA0t4H35h0Y4uLi9NVXX2nEiBFyOBxeaRgAAADgCSn6xpmPj482bNiQ5JQGAAAA4F6V4lsstG3bVh9++KFiYmLSsj0AAACAR6X4Lg2ff/65zp49q9mzZyt79uyuJ6/ZbDa+tAYAAIB7VooDL19aAwAAwP0oxYG3fPny+vjjjxUWFqYzZ84oZ86cql+/vrp27ZqW7QMAAADuSooD79ChQ3Xw4EENHDhQefPm1fHjxzV9+nSdPn1aY8aMScs2AgAAAKmW4sD7448/6ocfflBQUJAkqUiRIipXrpxq166dZo0DAAAA7laK79KQI0cOXb9+PV5ZVFSUQkJCPN4oAAAAwFNSfIW3cePG6tSpk9q0aaNcuXLp1KlTmj9/vho3bqyNGze6lnvyySfTpKEAAABAatiMMSYlC9asWTP5jdls+vHHH++6UYmJjLwipzNFTQUAAMD/CwkJVMu+8+OVLXi3lSIiLqdTi9KG3W5TcHDmROtSfIV37dq1HmsQAAAA4C0pnsMLAAAA3I8IvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAszddbO3r99dd17Ngx2e12BQQE6J133lHJkiW9tXsAAAA8oLwWeMeNG6fAwEBJ0po1azRgwAAtXbrUW7sHAADAA8prUxpuhl1JunLlimw2m7d2DQAAgAeY167wStLAgQO1YcMGGWP0ySefeHPXAAAAeEDZjDHG2ztdtmyZwsLCNHPmTG/vGgAA4IHTsu/8eK8XvNsqnVqSPrx6hfemJk2aaPDgwTp//ryyZcuWonUiI6/I6fR6NgcAALivhYQEJloeEXHZyy1JW3a7TcHBmROv80YDrl69qpMnT7per127VlmyZFHWrFm9sXsAAAA8wLxyhff69evq0aOHrl+/LrvdrixZsmjatGl8cQ0AAABpziuBN0eOHFq8eLE3dgUAAADEw5PWAAAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AQAAYGkEXgAAAFiarzd2cv78efXt21dHjhyRw+FQwYIFNXz4cGXPnt0buwcAAMADzCtXeG02mzp16qTVq1drxYoVyp8/v9577z1v7BoAAAAPOK8E3qxZs+rxxx93vS5fvrxOnDjhjV0DAADgAeeVKQ23cjqd+uKLL1SzZs07Wi84OHMatQgAAODBExISmN5N8BqvB94RI0YoICBArVu3vqP1IiOvyOk0adQqAAAAa0oq2EZEXPZyS9KW3W5L8gKpVwPvuHHjdPjwYU2bNk12OzeIAAAAQNrzWuCdMGGCduzYoRkzZsjhcHhrtwAAAHjAeSXw7t27V9OnT1ehQoXUokULSVK+fPk0depUb+weAAAADzCvBN6iRYvqr7/+8sauAAAAgHiYSAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0nzTuwEPqqCsGeXvF3/4o2JidenC9XRqEQAAgDUReNOJv5+vei39OV7ZhOerp1NrAAAArIspDQAAALA0Ai8AAAAszSuBd9y4capZs6aKFy+uPXv2eGOXAAAAgCQvBd5nnnlG8+fPV968eb2xOwAAAMDFK19aCw0N9cZuAAAAgATum7s0BAdnTu8meEVISGB6NwEAHnhx0THycfglWwbczx6kzHHfBN7IyCtyOk16N8Njkvoli4i47OWWAABuFxISqFWvtI9XVn/ebD6jcV96UDKH3W5L8gIpd2kAAACApRF4AQAAYGleCbwjR45UtWrVdOrUKbVv314NGjTwxm4BAAAA78zhHTRokAYNGuSNXQEAAADxMKUBAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYGoEXAAAAlkbgBQAAgKUReAEAAGBpBF4AAABYmlcePAEAAO5dQVn85e9wxCuLio7WpYtR6dQiwLMIvAAAPOD8HQ61m90jXtmc9pMkEXhhDUxpAAAAgKUReAEAAGBpTGkAgDuUJcghh79/gvLoqChdvBSdDi0CALhD4AWAO+Tw99eEt7skKO81ZrokAi8A3GuY0gAAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACzNN70bAAC492UJyiiHf/xTRnRUrC5eup5OLQKAlCPwAgCS5fD31eiBS+KVDRjVLJ1aAwB3hikNAAAAsDQCLwAAACyNwAsAAABLI/ACAADA0gi8AAAAsDQCLwAAACyN25IBaSBLlgxyOPwSlEdHx+jixb/ToUUAADy4CLxAGnA4/PT+++8nKO/du7ckAi8AAN7ElAYAAABYGoEXAAAAlkbgBQAAgKUxhxdekS2rv3z9HAnKY2Oidf5CVDq0CAAAPCgIvPAKXz+H/rNyaILyag2HSiLwAgC8J1sWh3wd/vHKYqOjdP5idDq1CGmNwAsAAB4ovg5//f5up3hllfp+IonAa1XM4QUAAIClEXgBAABgaQReAAAAWBqBFwAAAJbmtcB78OBBNW/eXHXq1FHz5s116NAhb+0aAAAADzCvBd4hQ4aoZcuWWr16tVq2bKnBgwd7a9cAAAB4gHnltmSRkZHatWuXZs+eLUlq2LChRowYoXPnzil79uwp2obdbkvLJqaLbAH+Ccqs2M+b/DNmTbTcqn0OCgpKtNyq/X3QBGUNTrTcysc3S9aABGVW7m/GHAmPsZX7myNzwvOxlfvrCHrAjm+2TAnKrNZfd/2xGWNMWjdgx44d6tevn8LCwlxl9evX1/jx41WqVKm03j0AAAAeYHxpDQAAAJbmlcCbJ08enT59WnFxcZKkuLg4nTlzRnny5PHG7gEAAPAA80rgDQ4OVsmSJbVy5UpJ0sqVK1WyZMkUz98FAAAAUssrc3glaf/+/erfv78uXbqkoKAgjRs3To888og3dg0AAIAHmNcCLwAAAJAe+NIaAAAALI3ACwAAAEsj8AIAAMDSCLwAAACwNK88WthbXn/9dR07dkx2u10BAQF65513VLJkSY0bN06rV6/W8ePHtWLFChUrVkySdP78efXt21dHjhyRw+FQwYIFNXz4cGXPnl3h4eEaNmyYa9uRkZEKCQnR0qVLJUlbt27V4MGDFRUVpbx582r8+PEKDg5Otm7JkiWaO3eu7Ha7fHx8NGDAAIWGhqbbGNxqypQp+vDDD+PVf/XVV5ozZ46cTqfy58+vsWPHKmvWG48IvnDhgoYPH66dO3fK19dX9erV0xtvvJFsP91tMy3HoGbNmnI4HPL3v/FI5z59+uipp56SlLpj5nQ69fLLL+v69euSpJCQEA0bNkz58uVzWydJxYsXV7FixWS33/ib891331Xx4sU9NgbuxuGnn37SpEmTZIyRMUZvvPGGateu7fb9kFybp02bpuXLl8vX11eZMmXS8OHDVbRoUUnS9OnTtWLFCsXGxqpcuXIaMWKEHA6HJOnYsWMaNmyYjhw5Ih8fH7Vv314vvvjifTkG7vrpbnwkyRij9u3b688//9Svv/7qsf7f6vb3d1K/1wcPHtTgwYMVEREhX19flSlTRkOGDFGGDBmS7WdSfYmLi9PYsWO1ceNGxcTEqGbNmurbt69sthuPAV28eLFmzpwpY4yqVaumQYMGucbYExI73o899liqjnV0dLS6du2qHTt2SFK84+Wu7tixY6pdu3a84z5nzhxly5Yt2fFJz3FIzedfcudPd+cOT382JnXuS+qzQVKS54rk+uXuvJcW55G7HYOoqCiNHj1aGzdulL+/v8qXL68RI0ZIkg4ePKj+/fvrwoULypo1q8aNG6dChQolu966des0adIkxcbGKkuWLBozZozy58+f7HpJfSa7a/8dMxZy6dIl188//PCDadKkiTHGmE2bNpkTJ06YGjVqmL/++su1zPnz580vv/ziej127Fjz9ttvJ7rtrl27mk8++cQYY0xcXJypVauW2bRpkzHGmKlTp5r+/fsnW3fu3DlToUIFExERYYwxZs2aNaZevXoe6ftNdzoGN+3YscN07NgxXv2+fftM1apVTWRkpKsv77zzjmudLl26mNmzZ7tenzlzJtl+JrdNT0hqDJLq+90cs1v3NWfOHNOtW7cU1RUrVsxcuXLlrvqZnMTGwel0mtDQUNc4/Pnnn6Z8+fImLi4u2fdDUm3etWuXefrpp83Vq1eNMcbMnTvXdOrUyRhjzPr1603Dhg3N1atXjdPpNAMHDjTTp083xhjjdDpN48aNzQ8//OB6ffbs2ftyDNz109343DRv3jzz9ttvm8qVK3uu87e4/f3t7vf66NGjZufOncaYG++NHj16mClTpiTbT3d9WbhwoenQoYOJjo42MTExpmPHjmblypXGGGOOHDlinnrqKRMZGWni4uJMhw4dzNKlSz3W96SOd2qPdUxMjNmwYYPZtWtXguPlru7o0aNJHl934+MpqRmHu/n8u9Wt509jkj53GOP5z8akzn1JnSeMSfpccbtb++VurNLqPJJSSY3BiBEjzKhRo4zT6TTGGFf7jDGmTZs2ZtmyZcYYY5YtW2batGmT7HoXLlwwlStXNgcOHHCt16FDh2TXc/eZ7K79d8pSUxoCAwNdP1+5csX113FoaGiiT3XLmjWrHn/8cdfr8uXL68SJEwmWi4yM1IYNG9S4cWNJ0o4dO+Tv7+/6y61Fixb67rvvkq0z//+Xy9WrVyVJly9fVu7cue+637e60zGQblyVGD58uIYOHRqvfM+ePfEeEFK9enWtWLFCknTo0CHt2bNHbdu2dS0fEhIiyX0/3W3TU5Iag6TczTG7fV+3XpVyV+cNSY2D3W7X5cuXJd3oT86cOWW321P8fridzWZTTEyM/v77b9c2b47R7t27FRoaqoCAANlsNlWrVs11vP/3v/8pU6ZMqlWrlms7N6+qe4q3xsBdP92Nj3TjvRQWFqbOnTvffYcTkdj7293vdb58+fToo49KujFOZcuWdY2Bu36668vu3bv15JNPys/PT76+vqpSpYprvdWrV6tWrVrKnj277Ha7XnzxRa1atcqjY5DY8U7tsfb19dW//vWveL9bKalzx934eNKdjsPdfP7ddPv50925Iy0kde670/PE7W7vl7uxSu/zSGJjcPXqVS1btkw9evRw9T1Hjhyuvu3atUsNGzaUJDVs2FC7du3SuXPn3K53+PBh5ciRQw8//LCkG+f3//73v8muJyX9mZxU+1PDUlMaJGngwIHasGGDjDH65JNPUrye0+nUF198oZo1ayaoW7ZsmapUqeI6OCdPntRDDz3kqs+ePbucTqcuXLjgti579uwaPny4nn/+eQUFBcnpdOqzzz67i94m7k7HYNKkSXruuecS/JukRIkS+uOPP3T06FHly5dPK1eu1LVr13ThwgXt27dPuXLl0sCBA/Xnn38qR44c6tu3r4oWLeq2n+626clpDUmNQZ8+fWSMUaVKldSrVy8FBQXd9TF79dVXtWvXLmXLlk2zZs1KcV2bNm0UFxenatWqqXv37vH+LZxW42Cz2fTBBx/o9ddfV0BAgK5evaoZM2YkWC+p90NibS5RooTat2+vmjVrKjAwUEFBQfr8888lSaVKldKXX36pc+fOKSgoSN9++62OHz8uSdq3b5+yZs2qN998U0eOHFGBAgX09ttve/yR494YA3f9dDc+TqdTgwYN0pAhQ+TrmzYfx4m9v1P6WfT333/rq6++Uq9evSS5P57u+lKqVCl9/fXXatmypSRpzZo1unTpkqSEn6cPPfSQTp486bH+p+R438mxvhtXr17VCy+8IEmqX7++OnbsKJvN5nZ8PCU143C3n39SwvOnu3PHTd74bJTcnysTO1e465e7sUrL80hqHT16VFmzZtWUKVP066+/KlOmTOrRo4dCQ0N18uRJ5cqVSz4+PpIkHx8f5cyZUydPnpSPj0+S6z388MM6e/astm/frrJly7r+aEtuvZR+Jt+1VF8bvsctXbo0wb8N3V0OHzp0qOnatavrEvqt6tevb9asWeN6/d1335lXX3013jJly5Y158+fd1t3+fJl07x5c7N//35jjDFhYWGmYcOGrsv7npaSMQgPDzevvPKKqw23169cudI0bdrUNGvWzHz88cemWLFi5vLly2b16tWmZMmSrmkAq1evNs8884wxxiTbz6S2mdZjcOLECWOMMVFRUWbw4MGmd+/exhj3xzOlxywuLs589NFHZsiQIQnakFjdzbZcvnzZvPbaa2bChAke6W9Sbo5DTEyMadu2rdm8ebMxxpjNmzeb6tWrJ/gXYmLvh6TafOzYMfPyyy+b06dPG2OMmTlzpuncubNrvTlz5pgmTZqY5s2bm4kTJ5rHHnvMGGPMp59+aipWrGj27dvnen3rv808LS3HwF0/3Y3PjBkzzAcffGCMcf8v79RK6v2dkt/rmJgY89prr5nhw4fH22ZS/XTXl7i4ODNhwgTz3HPPmZYtW5qxY8e6/oU8fPhwM3PmTNey27ZtMw0bNvTYGKTkeN/psU6sj8nVRUVFuabsnD171rz44otm8eLFxhj34+MpqRkHT3z+3X7+dHfuMCbtPhvdnf9vP1cmda641e39cjdWaXUeuVO3jsGOHTtMsWLFzPLly40xxmzdutU88cQT5vLly+aPP/4w9evXj7duvXr1zI4dO9yuZ4wxGzZsMC1atDDPP/+8mThxogkNDTV//vmn2/VS+pl8t1MaLBt4jTGmTJky5ty5c67XSQ3W2LFjTfv27U1UVFSCui1btpgqVaqYmJgYV9m2bdtMgwYNXK8jIyNN+fLlk6379ttvEwTQsmXLuuazpoXkxmD69OmmSpUqpkaNGqZGjRqmZMmSpmrVqmb9+vUJtrVt2zbz1FNPGWOM2b59u6lZs2a8+pt9uZN+3rrNtHL7GBhjzO7du02NGjVcbfDEMTtz5oxrvTup+/HHH03r1q1T3qFUKlOmjNm6dWuCeeN169Y127Ztc71293646dY2f/LJJ/E+hCMiIky5cuUSXS8sLMy0aNHCGHNjbG8NuGfPnk1yjDwlrcbgdrf20934dO7c2VSvXt3UqFHDPPXUU6ZEiRKmRo0aHvsDMKn3d1hYmNvf69jYWNOjRw/Tr18/t3+Q39rPO+nLjBkzXCFi5syZZtiwYa66b7/9NsEfoHdj+/btbo93ao/1nQbe282bN8/1XYHb3To+npKacbjbz7/Ezp/uzh238+RnY3JhKbHzhDHxzxU3JdYvd2PljfNIStw6BpGRkebRRx+N9/6uV6+e2b59uzl79qypVKmSiY2NNcbc+DyoVKmSiYyMdLve7SIiIkzp0qXN1atX3a6X3O9mYu1PDcvM4b169Wq8f4OtXbtWWbJkSfbf5BMmTNCOHTs0derURP9t8tVXX+m5556L9y+60qVL6++//9bmzZslSQsXLlTdunWTrcuXL5927dqlyMhISdIvv/yizJkzK1u2bHfR83+kZgw6d+6s//73v1q7dq3Wrl2r3Llza9asWapataokKSIiQtKNb1dOnjxZHTp0cPUzICBAe/fulSRt2rRJWbJkUbZs2ZLtZ1LbTMsx8Pf3d80PMsZo1apVrm/kpvaYnTt3TufOnXPt67vvvnN9m9hd3cWLF13zOWNjY7V69WpXW9J6HPLkyaNTp07pwIEDkqT9+/crMjJSBQoUkJT0+8Fdm/Ply6fff/9d165dkyT9/PPP8f49efN4X7x4UTNnzlTHjh0lSdWqVdOpU6d05swZSdL69es9eqcKb46Bu366G5/p06dr3bp1Wrt2rRYsWKCgoCCtXbtWmTNn9sgYJPX+LlCgQJK/106nU/3795ePj49GjRqVYG5jUv1015eoqCjX++/EiRP64osv1L59e0lSnTp1tGbNGp07d05Op1Nffvml6tWr55H+S1Lu3LmTPN6pPdapERkZqZiYGEnS9evXtXbtWpUoUUKS3I6Pp6RmHFL7+XdTUufPpM4d3vhslNyfK69du5bkucJdv9yNVVqcR+5W9uzZ9fjjj2vDhg2SbtyVITIyUgULFlRwcLBKliyplStXSpJWrlzp+u6Nu/Wkfz4fnE6nJkyYoBYtWiggIMDteu5+Nz3JZowxHt1iOjl79qxef/11Xb9+XXa7XVmyZFG/fv1UqlQpjRw5Ut9//73Onj2rbNmyKWvWrAoLC9PevXvVsGFDFSpUyHXLnXz58mnq1KmSbsxfq1KlihYvXqzChQvH2194eLiGDBkS7zZWN+fyuKubPXu2Fi9eLD8/PzkcDvXv399jtyVLzRjcrmbNmpo2bZrrth+dOnXSiRMnFBMTo/r166tHjx6uieR//PGHhg0bpujoaGXMmFEDBw5U2bJlk+2nu22m1RgEBQWpe/fuiouLk9PpVOHChTVo0CDlzJlTUuqO2V9//aW3337bdRLLmzevBg4cqPz587ut27JliwYPHiybzabY2FhVqFBBAwYMUKZMmTwyBu7GoVSpUlq+fLlmzpzpCjJvvvmmatWq5fb94K7NxhiNHz9ea9eulcPhUFBQkIYOHaoiRYpIkho1aiSn06nY2Fi1atVKr7zyiqud//nPf/T+++/LGKOsWbNq+PDhrlvf3E9j4K6fyY3PTceOHVPTpk3T7LZkUvz3d1K/1+vWrVOXLl3i3RqqYsWKGjJkiNt+uuvL2bNn1aZNG9f2unXrpvr167uWX7hwoWsOZZUqVTR48GDX/EFPSOx4FyxYMNXHumnTpjp9+rTrtlRPPfWURo0a5bbu+++/1+TJk2W32xUbG6unn35avXv3lo+PT7Ljk17jIKXu809yf/5M6tyRFp+NiZ375s6dm+Rnw9GjR92eK9z1y915z9Pnkbsdg7CwMB09elQDBgzQhQsX5Ovrq549e6p69eqSboTO/v3769KlSwoKCtK4ceP0yCOPSJLb9QYOHKjw8HDFxMSoSpUqGjBggOv2bu7WS+oz2V3775RlAi8AAACQGMtMaQAAAAASQ+AFAACApRF4AQAAYGkEXgAAAFgagRcAAACWRuAFAACApRF4AeA+8uuvv6patWrp3QwAuK8QeAEAAGBpPHgCAO5Bu3bt0sCBA3Xo0CFVr15dNptNISEhWrhwoesJVdKNx43Wrl1b69atcz2+e+fOnerYsaPWr18vPz+/9OwGANwTuMILAPeY6OhodevWTY0bN9Zvv/2munXr6vvvv1fGjBk1c+ZM5cyZU1u2bNGWLVuUK1cuVa5cWd9++61r/W+++UYNGjQg7ALA/yPwAsA9Ztu2bYqJiVHbtm3l5+enunXrqkyZMkku//zzz2v58uWSpLi4OIWFhalx48beai4A3PMIvABwjzlz5oxy5colm83mKnvooYeSXP6ZZ57R/v37dfToUW3YsEGZM2dW2bJlvdFUALgv+KZ3AwAA8YWEhOj06dMyxrhC74kTJ5Q/f/54Ifgmf39/1atXT8uXL9eBAwe4ugsAt+EKLwDcY8qXLy9fX1/NmzdPMTEx+v777/XHH39IkoKDg3XhwgVdvnw53jqNGzfW0qVLtXbtWgIvANyGwAsA9xiHw6EPP/xQS5cuVeXKlbVq1So9++yzkqTChQurQYMGqlWrlkJDQ3X69GlJUqVKlWS321WqVCnlzZs3PZsPAPccpjQAwD2oTJkyWrZsmet1//79XT+PGTMm0XVy586tRo0apXnbAOB+wxVeALCA7du3a9euXapXr156NwUA7jlc4QWA+1y/fv20Zs0aDRw4UJkzZ07v5gDAPYcnrQEAAMDSmNIAAAAASyPwAgAAwNIIvAAAALA0Ai8AAAAsjcALAAAASyPwAgAAwNL+D3yNFm4dNCTfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = sns.barplot(x = 'qty',y = 'price',hue=\"manufacturer\",  data=d58).set(title='Top Manufacturers with Price, Quantity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vb4YLjU0TVKG"
   },
   "source": [
    "### 2. Which are the most profitable products?\n",
    "Based on Total Price "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TAhKceFFmTtB",
    "outputId": "d386ba51-8295-4527-ddda-0e1ad933d5d9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profit = sns.barplot(x = 'product',y = 'Total_Price',data = most_profitable_products).set(title='most_profitables_products')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "qKZccUamlypT"
   },
   "outputs": [],
   "source": [
    "most_profitable_products['product']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EA_FUIW9TaMP"
   },
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "_qjxiHLPe0xj"
   },
   "outputs": [],
   "source": [
    "df['Total_Price'] =df['qty']*df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Dy-TjTAvfQ-x"
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bZDzojJafYg-"
   },
   "outputs": [],
   "source": [
    "df0 = df.groupby(['product','Total_Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "weMiywauf0Al"
   },
   "outputs": [],
   "source": [
    "df1 = df0.sum().sort_values(by='Total_Price',ascending=[False])[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4xilKVzFja6Y"
   },
   "outputs": [],
   "source": [
    "[df1.drop(['qty', 'price'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "aQ-qx1aHj-eX"
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   "outputs": [],
   "source": [
    "most_profitable_products = df1.reset_index()"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "###  3. Which is the product where you have seen a decrease in the price?"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
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   "source": [
    "pric_01 = df.drop(['manufacturer', 'category','qty'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
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    " df.to_csv('price_trend.csv', index=False)"
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 713788 entries, 0 to 713787\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   product  713788 non-null  object        \n",
      " 1   price    713788 non-null  float64       \n",
      " 2   date     713788 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), object(1)\n",
      "memory usage: 16.3+ MB\n"
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   "source": [
    "pric_01.info()"
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   "cell_type": "code",
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       "      <th>XDM31255M-WU</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XD-COMBO</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
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       "    <tr>\n",
       "      <th>XDS-P</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
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       "      <th>RW3-WU</th>\n",
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       "      <td>40</td>\n",
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       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "                price  date\n",
       "product                    \n",
       "FEE SKU            81    81\n",
       "XDM31255M-WU       40    40\n",
       "XD-COMBO           40    40\n",
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       "RW3-WU             40    40\n",
       "...               ...   ...\n",
       "F8J054BTBLK         1     1\n",
       "LBS005A             1     1\n",
       "F7U099BT04-BLK      1     1\n",
       "S041394             1     1\n",
       "JX961A              1     1\n",
       "\n",
       "[30672 rows x 2 columns]"
      ]
     },
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       "                 price  date\n",
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       "                                                            product\n",
       "date       price                                                   \n",
       "2020-08-17 0.00                      SCX-5835FN - TAABTRY-MC55EAB00\n",
       "           0.20   0192101921019210192101921019210192101921019210...\n",
       "           0.21                                               01921\n",
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       "           0.46                                          0381003820"
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111056 rows × 1 columns</p>\n",
       "</div>\n",
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "        }\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                             price\n",
       "product          date             \n",
       "0-00496          2020-08-17      1\n",
       "                 2020-08-24      1\n",
       "0-00498          2020-08-17      1\n",
       "003-000884-01-OE 2020-08-17      2\n",
       "                 2020-08-24      2\n",
       "...                            ...\n",
       "ZT62063-T01A100Z 2020-08-24      1\n",
       "ZT62063-T110100Z 2020-08-17      1\n",
       "                 2020-08-24      1\n",
       "ZUCUBJCK         2020-08-17      1\n",
       "                 2020-08-24      1\n",
       "\n",
       "[111056 rows x 1 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df102"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MoJuCEa-TdwR"
   },
   "source": [
    "### 4. Which is the product where you have seen an increase in the price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ihpDGup2Tf5v"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lPb_FF5RTgr9"
   },
   "source": [
    "\n",
    "### 5. Which is the top category by price and quantity?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "41wCUacw51Ir",
    "outputId": "ab638531-50ac-4854-ac8d-3f1923141e8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1221\n",
       "1    2220\n",
       "2    9970\n",
       "3    2240\n",
       "4    3350\n",
       "5    9995\n",
       "6     303\n",
       "7    2201\n",
       "8    1251\n",
       "9     101\n",
       "Name: category, dtype: category\n",
       "Categories (135, int64): [1, 4, 5, 11, ..., 9970, 9990, 9995, 9999]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d91"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "9P4n64F0Tgai"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HJEXhmOe2_WE"
   },
   "outputs": [],
   "source": [
    "df86 = df.groupby('category').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0kFzVW_jTmGj"
   },
   "outputs": [],
   "source": [
    "df86.drop(['Total_Price'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pTDtIoxk3ReZ"
   },
   "outputs": [],
   "source": [
    "d87 = df86.sort_values(by=['qty', 'price'],ascending= False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rtmklS1g3YCi"
   },
   "outputs": [],
   "source": [
    "d89 = d87.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4TbL9yCq3iwO"
   },
   "outputs": [],
   "source": [
    "d90 =d89[:10]"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
    "colab": {
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    "id": "xHDqQ4Op5poC",
    "outputId": "a585349c-4273-42d9-c785-89d8db90a75f"
   },
   "outputs": [
    {
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       "      <th>9</th>\n",
       "      <td>101</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      ],
      "text/plain": [
       "  category       qty        price\n",
       "0     1221  17378795    346527.07\n",
       "1     2220  12851216   1741955.11\n",
       "2     9970   8618185   1389155.66\n",
       "3     2240   8264370   1520556.20\n",
       "4     3350   7068656    580035.13\n",
       "5     9995   5015339    970241.61\n",
       "6      303   4902118   4544824.27\n",
       "7     2201   4715114    558368.49\n",
       "8     1251   4347126     13541.88\n",
       "9      101   3877634  11079362.36"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RXpahxoa5s6F"
   },
   "outputs": [],
   "source": [
    "d91=d90['category']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6Qd87LBK3pn2"
   },
   "outputs": [],
   "source": [
    "df92= d91.astype({\"category\":'category'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qg6tM9UMTmZY"
   },
   "source": [
    "### 6. Which products are aging?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N6KxbFmqqTVq",
    "outputId": "1d023b5b-20d1-4a2d-b495-ff9ce2a9e349"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'aging_products')]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = 'product',y = 'qty',data = aging_products).set(title='aging_products')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JYQcX0vvo_q8",
    "outputId": "5d431917-61d5-42cc-f813-783d7d9fd9cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-c4888bad-c4a3-45f8-90a6-048841f1937a\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>category</th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>product</th>\n",
       "      <th>qty</th>\n",
       "      <th>price</th>\n",
       "      <th>date</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9920</td>\n",
       "      <td>CASE LOGIC-PERSONAL &amp; PORTABLE</td>\n",
       "      <td>3200943</td>\n",
       "      <td>7</td>\n",
       "      <td>99.00</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>693.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>531</td>\n",
       "      <td>OMNITRON SYSTEMS</td>\n",
       "      <td>7206-0</td>\n",
       "      <td>2</td>\n",
       "      <td>59.09</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>118.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9070</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>SAC5070-800CR</td>\n",
       "      <td>3</td>\n",
       "      <td>500.06</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>1500.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9080</td>\n",
       "      <td>ZEBRA ENTERPRISE MCD-A1</td>\n",
       "      <td>KT-CLMPT-RS507-01R</td>\n",
       "      <td>115</td>\n",
       "      <td>86.34</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>9929.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9080</td>\n",
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       "      <td>KT-PAD-RS507-10R</td>\n",
       "      <td>42</td>\n",
       "      <td>51.80</td>\n",
       "      <td>2020-08-17</td>\n",
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       "  "
      ],
      "text/plain": [
       "  category                    manufacturer  ...       date  Total_Price\n",
       "0     9920  CASE LOGIC-PERSONAL & PORTABLE  ... 2020-08-17       693.00\n",
       "1      531                OMNITRON SYSTEMS  ... 2020-08-17       118.18\n",
       "2     9070         ZEBRA ENTERPRISE MCD-A1  ... 2020-08-17      1500.18\n",
       "3     9080         ZEBRA ENTERPRISE MCD-A1  ... 2020-08-17      9929.10\n",
       "4     9080         ZEBRA ENTERPRISE MCD-A1  ... 2020-08-17      2175.60\n",
       "\n",
       "[5 rows x 7 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
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  },
  {
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    "df4 = df.groupby(['product','qty'])"
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   "source": [
    "df5 = df4.sum().sort_values(by='qty',ascending=[False])[:10]"
   ]
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       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(808-3177, 60063), (808-3123, 60018), (808-3108, 60014), (808-3105, 60005), (808-3105, 60004), (808-3105, 60002), (808-3177, 60000), (808-3129, 59999), (808-3129, 59996), (808-3126, 59996)]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "s_zci_vvqMaS"
   },
   "outputs": [],
   "source": [
    "aging_products = df5.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "m4OwxYMGqtEM"
   },
   "outputs": [],
   "source": [
    "sns.barplot(x = 'product',y = 'qty',data = aging_products).set(title='aging_products')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C2KDG1rNTpg-"
   },
   "source": [
    "### 7. How to Decrease Product aging?"
   ]
  }
 ],
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