diff --git a/exercicios/para-casa/atividadeparacasa.ipynb b/exercicios/para-casa/atividadeparacasa.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gerando dados falsos\n",
+    "categorias = ['Eletrônicos', 'Roupas', 'Alimentos', 'Livros', 'Brinquedos']\n",
+    "datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')\n",
+    "num_registros = len(datas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Criando DataFrame\n",
+    "data = {\n",
+    "       'Data': np.random.choice(datas, num_registros),\n",
+    "       'Categoria': np.random.choice(categorias, num_registros),\n",
+    "       'Quantidade_Vendida': np.random.randint(1, 160, num_registros),\n",
+    "       'Preco_Unitario': np.round(np.random.uniform(10, 1100, num_registros), 2)\n",
+    "   }\n",
+    "df = pd.DataFrame(data)\n",
+    "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Salvando o DataFrame em um arquivo CSV\n",
+    "df.to_csv('dados_vendas.csv', index=False)\n",
+    "\n",
+    "print(\"Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Carregando dados CSV\n",
+    "df = pd.read_csv('dados_vendas.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Descobrir quantidade de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?**\n",
+    "\n",
+    "Existem 365 linhas e 5 colunas\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data                   object\n",
+       "Categoria              object\n",
+       "Quantidade_Vendida      int64\n",
+       "Preco_Unitario        float64\n",
+       "Valor_Total           float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Mostrar o tipo de dados\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 2: Quais são os tipos de dados (dtypes) das colunas?**\n",
+    "\n",
+    "Os dados das colunas são: data sendo um object, categoria sendo um object, quantidade vendida sendo int, preço unitario sendo float, valor total sendo float."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0  2023-06-06   Brinquedos                  35          700.59     24520.65\n",
+      "1  2023-07-12  Eletrônicos                  42          780.76     32791.92\n",
+      "2  2023-09-12   Brinquedos                  36          901.62     32458.32\n",
+      "3  2023-03-05    Alimentos                  68          221.30     15048.40\n",
+      "4  2023-07-19    Alimentos                  52          971.42     50513.84\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exibe as 5 primeiras linhas\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Estão presentes 5 categorias únicas de produto.\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Quantidade de categorias unicas\n",
+    "categorias_unicas = df['Categoria'].unique()\n",
+    "numero_categorias = len(categorias_unicas)\n",
+    "print(f\"Estão presentes {numero_categorias} categorias únicas de produto.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "1    2023-07-12  Eletrônicos                  42          780.76     32791.92\n",
+      "8    2023-08-31  Eletrônicos                  26          759.32     19742.32\n",
+      "13   2023-01-26  Eletrônicos                   3          997.77      2993.31\n",
+      "14   2023-05-24  Eletrônicos                 139           97.94     13613.66\n",
+      "15   2023-06-21  Eletrônicos                 151          908.09    137121.59\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "351  2023-12-24  Eletrônicos                 122          802.17     97864.74\n",
+      "353  2023-12-16  Eletrônicos                 124           14.94      1852.56\n",
+      "354  2023-04-15  Eletrônicos                 154          978.98    150762.92\n",
+      "357  2023-06-06  Eletrônicos                 101          447.21     45168.21\n",
+      "364  2023-10-25  Eletrônicos                  86          583.12     50148.32\n",
+      "\n",
+      "[81 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos':\n",
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "print(eletronicos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agrupado_media = df.groupby('Categoria')['Valor_Total'].mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Categoria\n",
+       "Alimentos      33911.476944\n",
+       "Brinquedos     40160.953649\n",
+       "Eletrônicos    47346.598765\n",
+       "Livros         41195.197231\n",
+       "Roupas         44284.231781\n",
+       "Name: Valor_Total, dtype: float64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_media"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?**\n",
+    "\n",
+    "A média d valor total das vendas da categoria eletrônicos é R$ 4751.63"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0    2023-06-06   Brinquedos                  35          700.59     24520.65\n",
+      "1    2023-07-12  Eletrônicos                  42          780.76     32791.92\n",
+      "2    2023-09-12   Brinquedos                  36          901.62     32458.32\n",
+      "3    2023-03-05    Alimentos                  68          221.30     15048.40\n",
+      "4    2023-07-19    Alimentos                  52          971.42     50513.84\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "359  2023-04-08       Roupas                  98          647.19     63424.62\n",
+      "360  2023-10-20   Brinquedos                  35          699.79     24492.65\n",
+      "361  2023-09-27       Livros                 115          148.39     17064.85\n",
+      "363  2023-02-06       Livros                 111          219.32     24344.52\n",
+      "364  2023-10-25  Eletrônicos                  86          583.12     50148.32\n",
+      "\n",
+      "[355 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Filtrar as vendas acima de R$ 1000:\n",
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "print(vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "355\n"
+     ]
+    }
+   ],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "quantidade_vendas_acima_1000 = len(vendas_acima_1000)\n",
+    "print(quantidade_vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 5: Quantas vendas acima de R$ 1000 ocorreram?**\n",
+    "\n",
+    "Houve 355 vendas com valor cima de R$ 1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Valor_Total\n",
+      "0    Alimentos   2441626.34\n",
+      "1   Brinquedos   2971910.57\n",
+      "2  Eletrônicos   3835074.50\n",
+      "3       Livros   2677687.82\n",
+      "4       Roupas   3232748.92\n"
+     ]
+    }
+   ],
+   "source": [
+    "##**Agrupar os dados por categoria e calcular o total de vendas por categoria:##\n",
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "print(total_por_categoria)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 6: Qual categoria teve o maior valor total de vendas?**\n",
+    "\n",
+    "A categoria que teve o maior valor de vendas foi de eletrônoico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WqVKl1KNHD644AQ+pFi1aKDU1VZMnT7ZpHz9+vCwWi80fYry8vDIMQy4uLumupkyaNEmpqakO1+Pi4qLw8HAtW7ZMsbGx1vYDBw5o7dq1Nn2ffvppubi46J133km3f8MwdO7cObv2eevq0ttvv609e/ak++6m9u3bKyYmJt3+pZvDlN+4ccOu/dxStGhRVatWTXPnzrW53Wr9+vXpnoVxcXGRxWKxeS2PHz+e7jbsjD6jq1WrJunmH8zMxMTE2DwzdfLkSS1fvlxNmzaVi4uLXFxc1LRpUy1fvtzmebDTp09r3rx5euyxx+Tr62u6n4w888wzqlKlikaOHKmYmJh0yy9duqQ33nhD0v9dzbv9/TYMQxMnTky33q3vjfrnOdu+fXulpqbqvffeS7fOjRs3rP0bNWqk3Llza+rUqTZ9/vl/Rbr5/2jbtm029ScnJ2vGjBkqVapUhs+umXHkWAHgXuXoK053Exsbq9mzZys2NlYBAQGSpFdeeUVr1qzR7Nmz9cEHH+iPP/7QiRMntHDhQn3++edKTU3Vyy+/rGeeeUbff/+9k48AQGZr3bq1nnjiCb3xxhs6fvy4QkNDtW7dOi1fvlwDBw60Djku3byt7bvvvtO4ceMUEBCg0qVL69FHH1WrVq30xRdfyM/PTxUrVlRMTIy+++47FShQ4J5qeuedd7RmzRo9/vjj6t27t27cuKFJkyapUqVK+uWXX6z9ypYtq/fff1/Dhg3T8ePHFRERIR8fHx07dkxLly7VCy+8oFdeecV0f6VLl1bdunW1fPlySUoXnIYMGaL//e9/atWqlbp06aIaNWooOTlZ+/bt06JFi3T8+HEVLFjQoWOMiopSy5Yt9dhjj6lbt246f/689RiTkpKs/Vq2bKlx48apWbNm+u9//6szZ85oypQpCgoKsnkt3n33XW3evFktW7ZUyZIldebMGX3yyScqXrx4ht+N9E+VK1dWeHi4zXDk0s334pb333/f+l1RvXv3Vu7cuTV9+nSlpKRo1KhRDh3/7fLkyaMlS5aocePGql+/vtq3b6969eopT548+vXXXzVv3jzly5dPI0eOVPny5VW2bFm98sor+uuvv+Tr66vFixenuwIkyTrYRf/+/RUeHm4dDKRBgwZ68cUXFRUVpT179qhp06bKkyePjhw5ooULF2rixIl65plnVKRIEQ0YMEBjx45VmzZt1KxZM+3du1erV69WwYIFba5ovfbaa5o/f76aN2+u/v37K3/+/Jo7d66OHTumxYsXK1cux/+m68ixAsA9c8JIftmSJGPp0qXW+RUrVhiSDC8vL5spd+7cRvv27Q3DMIyePXsakoxDhw5Z19u5c6chyTh48OCDPgQAmSyjIZovXbpkvPzyy0ZAQICRJ08eIzg42Bg9erSRlpZm0+/gwYNG/fr1DQ8PD5vhmC9cuGB07drVKFiwoOHt7W2Eh4cbBw8eNEqWLGkzZLO9w5EbhmFs2rTJqFGjhuHq6mqUKVPGmDZtmjF8+PAMh5devHix8dhjj1k/08qXL2/06dPH5nPMzJQpUwxJRu3atTNcfunSJWPYsGFGUFCQ4erqahQsWNCoW7euMWbMGOPatWuGYfzfcOSjR49Ot74kY/jw4enqrlChguHm5mZUrFjRWLJkidG5c+d0w5F/9tlnRnBwsOHm5maUL1/emD17drrXYsOGDUbbtm2NgIAAw9XV1QgICDA6duyYbgj1jEgy+vTpY3z55ZfW/VSvXj3D92nXrl1GeHi44e3tbXh6ehpPPPGEsXXrVps+t4Yj3759u+m+b3fhwgXj7bffNqpUqWJ4enoa7u7uRuXKlY1hw4YZcXFx1n6//fab0bhxY8Pb29soWLCg0bNnT2Pv3r2GJGP27NnWfjdu3DD69etnFCpUyLBYLOnOnRkzZhg1atQwPDw8DB8fH6NKlSrG0KFDjVOnTtls46233jL8/f0NDw8P48knnzQOHDhgFChQwOjVq5fN9o4ePWo888wzRt68eQ13d3ejdu3axooVK2z63Po/sHDhwnTHn9H/D3uPFQDulcUw7uEJ3IeQxWLR0qVLFRERIUn6+uuv1alTJ/3666/pHhT29vaWv7+/hg8frg8++EDXr1+3Lrty5Yo8PT21bt06NWnS5EEeAgAgi1ksFvXp0yfDW9CQ3sWLF5UvXz69//771lsIAeDfilv17qB69epKTU3VmTNnrN8r8k/16tXTjRs3dPToUestOocPH5Z0bw+3AgDwb3XlypV0A0hMmDBBkqyDKgHAv1mODk5JSUn6/fffrfPHjh3Tnj17lD9/fpUrV06dOnVSZGSkxo4dq+rVq+vs2bPasGGDqlatqpYtW6px48Z65JFH1K1bN02YMEFpaWnq06ePmjRpcl+jVgEA8G/z9ddfa86cOWrRooW8vb31448/av78+WratKnq1avn7PIA4L7l6OC0Y8cOPfHEE9b5QYMGSbr5LeVz5szR7Nmz9f7772vw4MH666+/VLBgQdWpU0etWrWSdHM422+//Vb9+vVT/fr15eXlpebNm9sMMwwAQE5QtWpV5c6dW6NGjVJiYqJ1wIj333/f2aUBQKbgGScAAAAAMMH3OAEAAACACYITAAAAAJjINs84ffjhhxo2bJgGDBhgHYUnIwsXLtRbb72l48ePKzg4WB999JFatGhh937S0tJ06tQp+fj42HwhHwAAAICcxTAMXbp0SQEBAaZfwJ0tgtP27ds1ffp0Va1a9a79tm7dqo4dOyoqKkqtWrXSvHnzFBERoV27dqly5cp27evUqVMKDAzMjLIBAAAAPAROnjyp4sWL37WP0weHSEpK0iOPPKJPPvlE77//vqpVq3bHK04dOnRQcnKyVqxYYW2rU6eOqlWrpmnTptm1v4SEBOXNm1cnT56Ur69vZhwCAAAAgH+hxMREBQYG6uLFi/Lz87trX6dfcerTp4/1O5HMhiyNiYmxDhl+S3h4uJYtW3bHdVJSUpSSkmKdv3TpkiTJ19eX4AQAAADArkd4nBqcFixYoF27dmn79u129Y+Pj1eRIkVs2ooUKaL4+Pg7rhMVFaV33nnnvuoEAAAAkLM5bVS9kydPasCAAfrqq6/k7u6eZfsZNmyYEhISrNPJkyezbF8AAAAAHk5Ou+K0c+dOnTlzRo888oi1LTU1VZs3b9bkyZOVkpIiFxcXm3X8/f11+vRpm7bTp0/L39//jvtxc3OTm5tb5hYPAAAAIEdx2hWnRo0aad++fdqzZ491qlmzpjp16qQ9e/akC02SFBYWpg0bNti0rV+/XmFhYQ+qbAAAAAA5kNOuOPn4+KQbQtzLy0sFChSwtkdGRqpYsWKKioqSJA0YMEANGjTQ2LFj1bJlSy1YsEA7duzQjBkzHnj9AAAAAHIOp11xskdsbKzi4uKs83Xr1tW8efM0Y8YMhYaGatGiRVq2bJnd3+EEAAAAAPfC6d/j9KAlJibKz89PCQkJDEcOAAAA5GCOZINsfcUJAAAAALIDghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmCA4AQAAAIAJghMAAAAAmMjt7AIAANmL5R2Ls0tAJjOGG84uAQD+9bjiBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmnBqcpk6dqqpVq8rX11e+vr4KCwvT6tWr79h/zpw5slgsNpO7u/sDrBgAAABATpTbmTsvXry4PvzwQwUHB8swDM2dO1dt27bV7t27ValSpQzX8fX11aFDh6zzFovlQZULAAAAIIdyanBq3bq1zfzIkSM1depU/fTTT3cMThaLRf7+/g+iPAAAAACQlI2ecUpNTdWCBQuUnJyssLCwO/ZLSkpSyZIlFRgYqLZt2+rXX3+963ZTUlKUmJhoMwEAAACAI5wenPbt2ydvb2+5ubmpV69eWrp0qSpWrJhh35CQEM2aNUvLly/Xl19+qbS0NNWtW1d//vnnHbcfFRUlPz8/6xQYGJhVhwIAAADgIWUxDMNwZgHXrl1TbGysEhIStGjRIs2cOVObNm26Y3i63fXr11WhQgV17NhR7733XoZ9UlJSlJKSYp1PTExUYGCgEhIS5Ovrm2nHAQAPC8s7PDv6sDGGO/VHPQBkW4mJifLz87MrGzj1GSdJcnV1VVBQkCSpRo0a2r59uyZOnKjp06ebrpsnTx5Vr15dv//++x37uLm5yc3NLdPqBQAAAJDzOP1WvX9KS0uzuUJ0N6mpqdq3b5+KFi2axVUBAAAAyMmcesVp2LBhat68uUqUKKFLly5p3rx5io6O1tq1ayVJkZGRKlasmKKioiRJ7777rurUqaOgoCBdvHhRo0eP1okTJ9SjRw9nHgYAAACAh5xTg9OZM2cUGRmpuLg4+fn5qWrVqlq7dq2aNGkiSYqNjVWuXP93UezChQvq2bOn4uPjlS9fPtWoUUNbt26163koAAAAALhXTh8c4kFz5AEwAMiJGBzi4cPgEACQMUeyQbZ7xgkAAAAAshuCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYyO3sAgAAAIC7sbxjcXYJyGTGcMPZJTiMK04AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmCE4AAAAAYILgBAAAAAAmnBqcpk6dqqpVq8rX11e+vr4KCwvT6tWr77rOwoULVb58ebm7u6tKlSpatWrVA6oWAAAAQE7l1OBUvHhxffjhh9q5c6d27NihJ598Um3bttWvv/6aYf+tW7eqY8eO6t69u3bv3q2IiAhFRERo//79D7hyAAAAADmJxTAMw9lF3C5//vwaPXq0unfvnm5Zhw4dlJycrBUrVljb6tSpo2rVqmnatGl2bT8xMVF+fn5KSEiQr69vptUNAA8LyzsWZ5eATGYMz1Y/6gGH8bn08Mkun0uOZINs84xTamqqFixYoOTkZIWFhWXYJyYmRo0bN7ZpCw8PV0xMzB23m5KSosTERJsJAAAAABzh9OC0b98+eXt7y83NTb169dLSpUtVsWLFDPvGx8erSJEiNm1FihRRfHz8HbcfFRUlPz8/6xQYGJip9QMAAAB4+Dk9OIWEhGjPnj36+eef9dJLL6lz58767bffMm37w4YNU0JCgnU6efJkpm0bAAAAQM6Q29kFuLq6KigoSJJUo0YNbd++XRMnTtT06dPT9fX399fp06dt2k6fPi1/f/87bt/NzU1ubm6ZWzQAAACAHMXpwemf0tLSlJKSkuGysLAwbdiwQQMHDrS2rV+//o7PRAE5CQ/OPpyyy8OzAADkdE4NTsOGDVPz5s1VokQJXbp0SfPmzVN0dLTWrl0rSYqMjFSxYsUUFRUlSRowYIAaNGigsWPHqmXLllqwYIF27NihGTNmOPMwAAAAADzknBqczpw5o8jISMXFxcnPz09Vq1bV2rVr1aRJE0lSbGyscuX6v8ew6tatq3nz5unNN9/U66+/ruDgYC1btkyVK1d21iEAAAAAyAGcGpw+++yzuy6Pjo5O19auXTu1a9cuiyoCAAAAgPScPqoeAAAAAGR3BCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMHFfwSklJSWz6gAAAACAbMuh4LR69Wp17txZZcqUUZ48eeTp6SlfX181aNBAI0eO1KlTp7KqTgAAAABwGruC09KlS1WuXDl169ZNuXPn1quvvqolS5Zo7dq1mjlzpho0aKDvvvtOZcqUUa9evXT27NmsrhsAAAAAHpjc9nQaNWqUxo8fr+bNmytXrvRZq3379pKkv/76S5MmTdKXX36pl19+OXMrBQAAAAAnsSs4xcTE2LWxYsWK6cMPP7yvggAAAAAgu2FUPQAAAAAw4VBwunHjhq5du2bTNnPmTHXu3FmTJk2SYRiZWhwAAAAAZAcOBadOnTpp+PDh1vnp06drwIABSk5O1rvvvqvXX3890wsEAAAAAGdzKDjt2rVLzZo1s85Pnz5dEyZM0KJFi7Rw4ULNmzcv0wsEAAAAAGeza3CIrl27SpL+/PNPffzxx5o7d64Mw9DevXu1evVqxcTE6MaNGzp16pS6desmSZo1a1bWVQ0AAAAAD5BdwWn27NmSpO+//14DBw7U448/rpUrV2rLli1asmSJJCkhIUHLly8nMAEAAAB46NgVnG5p2LChXnjhBUVGRmr27Nnq0KGDddnevXsVHByc6QUCAAAAgLM59IzTuHHjVLNmTc2bN09PPvmkzWAQy5Yt03PPPZfpBQIAAACAszl0xalAgQL64osvMlw2bty4TCkIAAAAALIbh4KTmUWLFumZZ57JzE0CAIB/Kcs7FmeXgExmDOc7O5FzOfwFuPv379fhw4dt2pcvX67Q0FB16tQpU4sDAAAAgOzA7uC0f/9+BQUFKTQ0VBUqVNDTTz+t06dPq0GDBurWrZuaN2+uo0ePZmWtAAAAAOAUdt+q9+qrryooKEiTJ0/W/PnzNX/+fB04cEDdu3fXmjVr5OHhkZV1AgAAAIDT2B2ctm/frnXr1qlatWp6/PHHNX/+fL3++ut6/vnns7I+AAAAAHA6u2/V+/vvvxUQECBJ8vPzk5eXl+rUqZNlhQEAAABAdmH3FSeLxaJLly7J3d1dhmHIYrHoypUrSkxMtOnn6+ub6UUCAAAAgDPZHZwMw1C5cuVs5qtXr24zb7FYlJqamrkVAgAAAICT2R2cNm7cmJV1AAAAAEC2ZXdwatCgQVbWAQAAAADZll2DQyQnJzu0UUf7AwAAAEB2ZldwCgoK0ocffqi4uLg79jEMQ+vXr1fz5s318ccfZ1qBAAAAAOBsdt2qFx0drddff10jRoxQaGioatasqYCAALm7u+vChQv67bffFBMTo9y5c2vYsGF68cUXs7puAAAAAHhg7ApOISEhWrx4sWJjY7Vw4UL98MMP2rp1q65cuaKCBQuqevXq+vTTT9W8eXO5uLhkdc0AAAAA8EDZPTiEJJUoUUKDBw/W4MGDs6oeAAAAAMh27HrGCQAAAAByMqcGp6ioKNWqVUs+Pj4qXLiwIiIidOjQobuuM2fOHFksFpvJ3d39AVUMAAAAICdyanDatGmT+vTpo59++knr16/X9evX1bRpU9PhzH19fRUXF2edTpw48YAqBgAAAJATOfSMU2Zbs2aNzfycOXNUuHBh7dy5U/Xr17/jehaLRf7+/lldHgAAAABIymbPOCUkJEiS8ufPf9d+SUlJKlmypAIDA9W2bVv9+uuvd+ybkpKixMREmwkAAAAAHOFwcFqzZo1+/PFH6/yUKVNUrVo1/fe//9WFCxfuuZC0tDQNHDhQ9erVU+XKle/YLyQkRLNmzdLy5cv15ZdfKi0tTXXr1tWff/6ZYf+oqCj5+flZp8DAwHuuEQAAAEDO5HBwGjJkiPWqzb59+zR48GC1aNFCx44d06BBg+65kD59+mj//v1asGDBXfuFhYUpMjJS1apVU4MGDbRkyRIVKlRI06dPz7D/sGHDlJCQYJ1Onjx5zzUCAAAAyJkcfsbp2LFjqlixoiRp8eLFatWqlT744APt2rVLLVq0uKci+vbtqxUrVmjz5s0qXry4Q+vmyZNH1atX1++//57hcjc3N7m5ud1TXQAAAAAg3cMVJ1dXV12+fFmS9N1336lp06aSbj6X5OjzQ4ZhqG/fvlq6dKm+//57lS5d2tFylJqaqn379qlo0aIOrwsAAAAA9nD4itNjjz2mQYMGqV69etq2bZu+/vprSdLhw4cdvlrUp08fzZs3T8uXL5ePj4/i4+MlSX5+fvLw8JAkRUZGqlixYoqKipIkvfvuu6pTp46CgoJ08eJFjR49WidOnFCPHj0cPRQAAAAAsIvDV5wmT56s3Llza9GiRZo6daqKFSsmSVq9erWaNWvm0LamTp2qhIQENWzYUEWLFrVOt8KYJMXGxiouLs46f+HCBfXs2VMVKlRQixYtlJiYqK1bt1pvHwQAAACAzGYxDMNwdhEPUmJiovz8/JSQkCBfX19nlwNkGss7FmeXgCxgDH/wH9GcSw8fZ5xHEufSw4hzCZnFWefSPzmSDe7rC3CvXr2qa9eu2bQRRhzHh8HDJ7t8GAAAACBzOHyrXnJysvr27avChQvLy8tL+fLls5kAAAAA4GHjcHAaOnSovv/+e02dOlVubm6aOXOm3nnnHQUEBOjzzz/PihoBAAAAwKkcvlXv22+/1eeff66GDRuqa9euevzxxxUUFKSSJUvqq6++UqdOnbKiTgAAAABwGoevOJ0/f15lypSRdPN5pvPnz0u6OUz55s2bM7c6AAAAAMgGHA5OZcqU0bFjxyRJ5cuX1zfffCPp5pWovHnzZmpxAAAAAJAdOBycunbtqr1790qSXnvtNU2ZMkXu7u56+eWXNWTIkEwvEAAAAACczeFnnF5++WXrvxs3bqyDBw9q586dCgoKUtWqVTO1OAAAAADIDu7re5wkqWTJkipZsmRm1AIAAAAA2ZJdwenjjz+2e4P9+/e/52IAAAAAIDuyKziNHz/eZv7s2bO6fPmydTCIixcvytPTU4ULFyY4AQAAAHjo2DU4xLFjx6zTyJEjVa1aNR04cEDnz5/X+fPndeDAAT3yyCN67733srpeAAAAAHjgHB5V76233tKkSZMUEhJibQsJCdH48eP15ptvZmpxAAAAAJAdOByc4uLidOPGjXTtqampOn36dKYUBQAAAADZicPBqVGjRnrxxRe1a9cua9vOnTv10ksvqXHjxplaHAAAAABkBw4Hp1mzZsnf3181a9aUm5ub3NzcVLt2bRUpUkQzZ87MihoBAAAAwKkc/h6nQoUKadWqVTp8+LAOHjwoSSpfvrzKlSuX6cUBAAAAQHZwz1+AW65cOcISAAAAgBzB4eCUmpqqOXPmaMOGDTpz5ozS0tJsln///feZVhwAAAAAZAcOB6cBAwZozpw5atmypSpXriyLxZIVdQEAAABAtuFwcFqwYIG++eYbtWjRIivqAQAAAIBsx+FR9VxdXRUUFJQVtQAAAABAtuRwcBo8eLAmTpwowzCyoh4AAAAAyHYcvlXvxx9/1MaNG7V69WpVqlRJefLksVm+ZMmSTCsOAAAAALIDh4NT3rx59dRTT2VFLQAAAACQLTkcnGbPnp0VdQAAAABAtuXwM06SdOPGDX333XeaPn26Ll26JEk6deqUkpKSMrU4AAAAAMgOHL7idOLECTVr1kyxsbFKSUlRkyZN5OPjo48++kgpKSmaNm1aVtQJAAAAAE7j8BWnAQMGqGbNmrpw4YI8PDys7U899ZQ2bNiQqcUBAAAAQHbg8BWnH374QVu3bpWrq6tNe6lSpfTXX39lWmEAAAAAkF04fMUpLS1Nqamp6dr//PNP+fj4ZEpRAAAAAJCdOBycmjZtqgkTJljnLRaLkpKSNHz4cLVo0SIzawMAAACAbMHhW/XGjh2r8PBwVaxYUVevXtV///tfHTlyRAULFtT8+fOzokYAAAAAcCq7g9PkyZP13HPPqXjx4tq7d68WLFigX375RUlJSerevbs6depkM1gEAAAAADws7A5Ob7zxhoYOHaqIiAj16NFDzz33XFbWBQAAAADZht3POMXHx2vatGmKi4tTkyZNVLp0ab333nv6888/s7I+AAAAAHA6u4OTh4eHIiMjtXHjRh05ckTPP/+8PvvsM5UqVUrNmjXTwoULdf369aysFQAAAACcwuFR9SSpTJkyevfdd3Xs2DGtXr1aBQoUUJcuXVSsWLHMrg8AAAAAnO6egtMtFotFuXPnlsVikWEYXHECAAAA8FC6p+B08uRJvfvuuypTpoyaNGmiU6dO6dNPP1VcXFxm1wcAAAAATmf3qHrXrl3TkiVLNGvWLH3//fcqWrSoOnfurG7duqlMmTJZWSMAAAAAOJXdwcnf31+XL19Wq1at9O233yo8PFy5ct3XnX4AAAAA8K9gd3B688039fzzz6tQoUJZWQ8AAAAAZDt2B6dBgwZlZR0AAAAAkG1xrx0AAAAAmCA4AQAAAIAJpwanqKgo1apVSz4+PipcuLAiIiJ06NAh0/UWLlyo8uXLy93dXVWqVNGqVaseQLUAAAAAcqp7Dk7Xrl3ToUOHdOPGjXve+aZNm9SnTx/99NNPWr9+va5fv66mTZsqOTn5juts3bpVHTt2VPfu3bV7925FREQoIiJC+/fvv+c6AAAAAOBuHA5Oly9fVvfu3eXp6alKlSopNjZWktSvXz99+OGHDm1rzZo16tKliypVqqTQ0FDNmTNHsbGx2rlz5x3XmThxopo1a6YhQ4aoQoUKeu+99/TII49o8uTJjh4KAAAAANjF4eA0bNgw7d27V9HR0XJ3d7e2N27cWF9//fV9FZOQkCBJyp8//x37xMTEqHHjxjZt4eHhiomJybB/SkqKEhMTbSYAAAAAcITDwWnZsmWaPHmyHnvsMVksFmt7pUqVdPTo0XsuJC0tTQMHDlS9evVUuXLlO/aLj49XkSJFbNqKFCmi+Pj4DPtHRUXJz8/POgUGBt5zjQAAAAByJoeD09mzZ1W4cOF07cnJyTZBylF9+vTR/v37tWDBgnveRkaGDRumhIQE63Ty5MlM3T4AAACAh5/DwalmzZpauXKldf5WWJo5c6bCwsLuqYi+fftqxYoV2rhxo4oXL37Xvv7+/jp9+rRN2+nTp+Xv759hfzc3N/n6+tpMAAAAAOCI3I6u8MEHH6h58+b67bffdOPGDU2cOFG//fabtm7dqk2bNjm0LcMw1K9fPy1dulTR0dEqXbq06TphYWHasGGDBg4caG1bv379PYc2AAAAADDj8BWnxx57THv27NGNGzdUpUoVrVu3ToULF1ZMTIxq1Kjh0Lb69OmjL7/8UvPmzZOPj4/i4+MVHx+vK1euWPtERkZq2LBh1vkBAwZozZo1Gjt2rA4ePKgRI0Zox44d6tu3r6OHAgAAAAB2cfiKkySVLVtWn3766X3vfOrUqZKkhg0b2rTPnj1bXbp0kSTFxsYqV67/y3d169bVvHnz9Oabb+r1119XcHCwli1bdtcBJQAAAADgftgVnBwZwtuRZ4gMwzDtEx0dna6tXbt2ateund37AQAAAID7YVdwyps3r90j5qWmpt5XQQAAAACQ3dgVnDZu3Gj99/Hjx/Xaa6+pS5cu1gEZYmJiNHfuXEVFRWVNlQAAAADgRHYFpwYNGlj//e6772rcuHHq2LGjta1NmzaqUqWKZsyYoc6dO2d+lQAAAADgRA6PqhcTE6OaNWuma69Zs6a2bduWKUUBAAAAQHbicHAKDAzMcES9mTNnKjAwMFOKAgAAAIDsxOHhyMePH6///Oc/Wr16tR599FFJ0rZt23TkyBEtXrw40wsEAAAAAGdz+IpTixYtdOTIEbVp00bnz5/X+fPn1bp1ax0+fFgtWrTIihoBAAAAwKnu6QtwixcvrpEjR2Z2LQAAAACQLTl8xQkAAAAAchqCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAm7RtWrXr26LBaLXRvctWvXfRUEAAAAANmNXcEpIiIii8sAAAAAgOzLruA0fPjwrK4DAAAAALItnnECAAAAABN2XXG6XWpqqsaPH69vvvlGsbGxunbtms3y8+fPZ1pxAAAAAJAdOHzF6Z133tG4cePUoUMHJSQkaNCgQXr66aeVK1cujRgxIgtKBAAAAADncjg4ffXVV/r00081ePBg5c6dWx07dtTMmTP19ttv66effsqKGgEAAADAqRwOTvHx8apSpYokydvbWwkJCZKkVq1aaeXKlZlbHQAAAABkAw4Hp+LFiysuLk6SVLZsWa1bt06StH37drm5uWVudQAAAACQDTgcnJ566ilt2LBBktSvXz+99dZbCg4OVmRkpLp165bpBQIAAACAszk8qt6HH35o/XeHDh1UsmRJbd26VcHBwWrdunWmFgcAAAAA2YHDwWnz5s2qW7eucue+uWqdOnVUp04d3bhxQ5s3b1b9+vUzvUgAAAAAcCaHb9V74oknMvyupoSEBD3xxBOZUhQAAAAAZCcOByfDMGSxWNK1nzt3Tl5eXplSFAAAAABkJ3bfqvf0009LkiwWi7p06WIzgl5qaqp++eUX1a1bN/MrBAAAAAAnszs4+fn5Sbp5xcnHx0ceHh7WZa6urqpTp4569uyZ+RUCAAAAgJPZHZxmz54tSSpVqpReeeUVbssDAAAAkGM4PKre8OHDJUlnz57VoUOHJEkhISEqVKhQ5lYGAAAAANmEw4NDXL58Wd26dVPRokVVv3591a9fXwEBAerevbsuX76cFTUCAAAAgFM5HJxefvllbdq0Sd9++60uXryoixcvavny5dq0aZMGDx6cFTUCAAAAgFM5fKve4sWLtWjRIjVs2NDa1qJFC3l4eKh9+/aaOnVqZtYHAAAAAE53T7fqFSlSJF174cKFuVUPAAAAwEPJ4eAUFham4cOH6+rVq9a2K1eu6J133lFYWFimFgcAAAAA2YHdt+q5uLgoLi5OEyZMULNmzVS8eHGFhoZKkvbu3St3d3etXbs2ywoFAAAAAGexOzgZhiFJqlKlio4cOaKvvvpKBw8elCR17NhRnTp1svlSXAAAAAB4WDg8OIQkeXp6qmfPnpldCwAAAABkSw4Fp5kzZ8rb2/uuffr3739fBQEAAABAduNQcJo2bZpcXFzuuNxisRCcAAAAADx0HApOO3bsUOHChbOqFgAAAADIluwejtxisWRlHQAAAACQbdkdnG6NqgcAAAAAOY3dwWn48OGmA0MAAAAAwMPI7mechg8fnpV1AAAAAEC2ZfcVJwAAAADIqZwanDZv3qzWrVsrICBAFotFy5Ytu2v/6OhoWSyWdFN8fPyDKRgAAABAjuRQcDIMQ7Gxsbp69Wqm7Dw5OVmhoaGaMmWKQ+sdOnRIcXFx1okh0gEAAABkJYe+x8kwDAUFBenXX39VcHDwfe+8efPmat68ucPrFS5cWHnz5r3v/QMAAACAPRy64pQrVy4FBwfr3LlzWVWPXapVq6aiRYuqSZMm2rJly137pqSkKDEx0WYCAAAAAEc4/IzThx9+qCFDhmj//v1ZUc9dFS1aVNOmTdPixYu1ePFiBQYGqmHDhtq1a9cd14mKipKfn591CgwMfIAVAwAAAHgYOHSrniRFRkbq8uXLCg0Nlaurqzw8PGyWnz9/PtOK+6eQkBCFhIRY5+vWraujR49q/Pjx+uKLLzJcZ9iwYRo0aJB1PjExkfAEAAAAwCEOB6cJEyZkQRn3rnbt2vrxxx/vuNzNzU1ubm4PsCIAAAAADxuHg1Pnzp2zoo57tmfPHhUtWtTZZQAAAAB4iDkcnCQpNTVVy5Yt04EDByRJlSpVUps2beTi4uLQdpKSkvT7779b548dO6Y9e/Yof/78KlGihIYNG6a//vpLn3/+uaSbV7tKly6tSpUq6erVq5o5c6a+//57rVu37l4OAwAAAADs4nBw+v3339WiRQv99ddf1ueNoqKiFBgYqJUrV6ps2bJ2b2vHjh164oknrPO3nkXq3Lmz5syZo7i4OMXGxlqXX7t2TYMHD9Zff/0lT09PVa1aVd99953NNgAAAAAgszkcnPr376+yZcvqp59+Uv78+SVJ586d03PPPaf+/ftr5cqVdm+rYcOGMgzjjsvnzJljMz906FANHTrU0ZIBAAAA4L44HJw2bdpkE5okqUCBAvrwww9Vr169TC0OAAAAALIDh7/Hyc3NTZcuXUrXnpSUJFdX10wpCgAAAACyE4eDU6tWrfTCCy/o559/lmEYMgxDP/30k3r16qU2bdpkRY0AAAAA4FQOB6ePP/5YZcuWVVhYmNzd3eXu7q569eopKChIEydOzIoaAQAAAMCpHH7GKW/evFq+fLmOHDmigwcPSpIqVKigoKCgTC8OAAAAALKDe/oeJ0kKDg5WcHBwZtYCAAAAANmSXcHp1vcr2WPcuHH3XAwAAAAAZEd2Bafdu3fbtTGLxXJfxQAAAABAdmRXcNq4cWNW1wEAAAAA2ZbDo+oBAAAAQE5zT4ND7NixQ998841iY2N17do1m2VLlizJlMIAAAAAILtw+IrTggULVLduXR04cEBLly7V9evX9euvv+r777+Xn59fVtQIAAAAAE7lcHD64IMPNH78eH377bdydXXVxIkTdfDgQbVv314lSpTIihoBAAAAwKkcDk5Hjx5Vy5YtJUmurq5KTk6WxWLRyy+/rBkzZmR6gQAAAADgbA4Hp3z58unSpUuSpGLFimn//v2SpIsXL+ry5cuZWx0AAAAAZAMODw5Rv359rV+/XlWqVFG7du00YMAAff/991q/fr0aNWqUFTUCAAAAgFPZHZz279+vypUra/Lkybp69aok6Y033lCePHm0detW/ec//9Gbb76ZZYUCAAAAgLPYHZyqVq2qWrVqqUePHnr22WclSbly5dJrr72WZcUBAAAAQHZg9zNOmzZtUqVKlTR48GAVLVpUnTt31g8//JCVtQEAAABAtmB3cHr88cc1a9YsxcXFadKkSTp+/LgaNGigcuXK6aOPPlJ8fHxW1gkAAAAATuPwqHpeXl7q2rWrNm3apMOHD6tdu3aaMmWKSpQooTZt2mRFjQAAAADgVA4Hp9sFBQXp9ddf15tvvikfHx+tXLkys+oCAAAAgGzD4eHIb9m8ebNmzZqlxYsXK1euXGrfvr26d++embUBAAAAQLbgUHA6deqU5syZozlz5uj3339X3bp19fHHH6t9+/by8vLKqhoBAAAAwKnsDk7NmzfXd999p4IFCyoyMlLdunVTSEhIVtYGAAAAANmC3cEpT548WrRokVq1aiUXF5esrAkAAAAAshW7g9P//ve/rKwDAAAAALKt+xpVDwAAAAByAoITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACacGp82bN6t169YKCAiQxWLRsmXLTNeJjo7WI488Ijc3NwUFBWnOnDlZXicAAACAnM2pwSk5OVmhoaGaMmWKXf2PHTumli1b6oknntCePXs0cOBA9ejRQ2vXrs3iSgEAAADkZLmdufPmzZurefPmdvefNm2aSpcurbFjx0qSKlSooB9//FHjx49XeHh4VpUJAAAAIIf7Vz3jFBMTo8aNG9u0hYeHKyYm5o7rpKSkKDEx0WYCAAAAAEf8q4JTfHy8ihQpYtNWpEgRJSYm6sqVKxmuExUVJT8/P+sUGBj4IEoFAAAA8BD5VwWnezFs2DAlJCRYp5MnTzq7JAAAAAD/Mk59xslR/v7+On36tE3b6dOn5evrKw8PjwzXcXNzk5ub24MoDwAAAMBD6l91xSksLEwbNmywaVu/fr3CwsKcVBEAAACAnMCpwSkpKUl79uzRnj17JN0cbnzPnj2KjY2VdPM2u8jISGv/Xr166Y8//tDQoUN18OBBffLJJ/rmm2/08ssvO6N8AAAAADmEU4PTjh07VL16dVWvXl2SNGjQIFWvXl1vv/22JCkuLs4aoiSpdOnSWrlypdavX6/Q0FCNHTtWM2fOZChyAAAAAFnKqc84NWzYUIZh3HH5nDlzMlxn9+7dWVgVAAAAANj6Vz3jBAAAAADOQHACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABPZIjhNmTJFpUqVkru7ux599FFt27btjn3nzJkji8ViM7m7uz/AagEAAADkNE4PTl9//bUGDRqk4cOHa9euXQoNDVV4eLjOnDlzx3V8fX0VFxdnnU6cOPEAKwYAAACQ0zg9OI0bN049e/ZU165dVbFiRU2bNk2enp6aNWvWHdexWCzy9/e3TkWKFHmAFQMAAADIaZwanK5du6adO3eqcePG1rZcuXKpcePGiomJueN6SUlJKlmypAIDA9W2bVv9+uuvd+ybkpKixMREmwkAAAAAHOHU4PT3338rNTU13RWjIkWKKD4+PsN1QkJCNGvWLC1fvlxffvml0tLSVLduXf35558Z9o+KipKfn591CgwMzPTjAAAAAPBwc/qteo4KCwtTZGSkqlWrpgYNGmjJkiUqVKiQpk+fnmH/YcOGKSEhwTqdPHnyAVcMAAAA4N8utzN3XrBgQbm4uOj06dM27adPn5a/v79d28iTJ4+qV6+u33//PcPlbm5ucnNzu+9aAQAAAORcTr3i5Orqqho1amjDhg3WtrS0NG3YsEFhYWF2bSM1NVX79u1T0aJFs6pMAAAAADmcU684SdKgQYPUuXNn1axZU7Vr19aECROUnJysrl27SpIiIyNVrFgxRUVFSZLeffdd1alTR0FBQbp48aJGjx6tEydOqEePHs48DAAAAAAPMacHpw4dOujs2bN6++23FR8fr2rVqmnNmjXWASNiY2OVK9f/XRi7cOGCevbsqfj4eOXLl081atTQ1q1bVbFiRWcdAgAAAICHnNODkyT17dtXffv2zXBZdHS0zfz48eM1fvz4B1AVAAAAANz0rxtVDwAAAAAeNIITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACACYITAAAAAJggOAEAAACAiWwRnKZMmaJSpUrJ3d1djz76qLZt23bX/gsXLlT58uXl7u6uKlWqaNWqVQ+oUgAAAAA5kdOD09dff61BgwZp+PDh2rVrl0JDQxUeHq4zZ85k2H/r1q3q2LGjunfvrt27dysiIkIRERHav3//A64cAAAAQE7h9OA0btw49ezZU127dlXFihU1bdo0eXp6atasWRn2nzhxopo1a6YhQ4aoQoUKeu+99/TII49o8uTJD7hyAAAAADlFbmfu/Nq1a9q5c6eGDRtmbcuVK5caN26smJiYDNeJiYnRoEGDbNrCw8O1bNmyDPunpKQoJSXFOp+QkCBJSkxMvM/qM9FVZxeAzOaU84vz6KHEuYTM4LSfeZxLDx3OJWSW7PK7+K06DMMw7evU4PT3338rNTVVRYoUsWkvUqSIDh48mOE68fHxGfaPj4/PsH9UVJTeeeeddO2BgYH3WDVgzu9DP2eXgIcE5xIyA+cRMgvnEjJLdjuXLl26JD+/u9fk1OD0IAwbNszmClVaWprOnz+vAgUKyGKxOLGynCUxMVGBgYE6efKkfH19nV0O/sU4l5BZOJeQWTiXkBk4j5zDMAxdunRJAQEBpn2dGpwKFiwoFxcXnT592qb99OnT8vf3z3Adf39/h/q7ubnJzc3Npi1v3rz3XjTui6+vLx8GyBScS8gsnEvILJxLyAycRw+e2ZWmW5w6OISrq6tq1KihDRs2WNvS0tK0YcMGhYWFZbhOWFiYTX9JWr9+/R37AwAAAMD9cvqteoMGDVLnzp1Vs2ZN1a5dWxMmTFBycrK6du0qSYqMjFSxYsUUFRUlSRowYIAaNGigsWPHqmXLllqwYIF27NihGTNmOPMwAAAAADzEnB6cOnTooLNnz+rtt99WfHy8qlWrpjVr1lgHgIiNjVWuXP93Yaxu3bqaN2+e3nzzTb3++usKDg7WsmXLVLlyZWcdAuzg5uam4cOHp7ttEnAU5xIyC+cSMgvnEjID51H2ZzHsGXsPAAAAAHIwp38BLgAAAABkdwQnAAAAADBBcAIA5BgHDhzQyJEjdf36dWeXAgD4lyE4QZIUHR0ti8WiixcvSpLmzJnD913hnv3bzp8uXbooIiLC2WXkeBaLRcuWLcuy7aempqpLly768ccf9f777zu8/r/tvMa9y+pzEcC/E8Eph4mJiZGLi4tatmx5134dOnTQ4cOHH1BVd9ewYUMNHDjQ2WXkSF26dJHFYrFOBQoUULNmzfTLL7/cdb3sdP4g+/jn+XRratas2T1vz5HAO3r0aIWHh2v58uXasGGD9uzZ49D+OK8fLnc7f+Li4tS8efMHWxD+FW7/HMuTJ49Kly6toUOH6urVq84uDQ+A04cjx4P12WefqV+/fvrss8906tQpBQQEZNjPw8NDHh4eD7g6ZEfNmjXT7NmzJUnx8fF688031apVK8XGxmbY//r165w/uKPbz6dbsnro3evXrytPnjx67bXXrG0//vijw9vhvM45/P3972v9a9euydXVNZOqQXZz63Ps+vXr2rlzpzp37iyLxaKPPvrI2aUhi3HFKQdJSkrS119/rZdeekktW7bUnDlz7tj3n7ekjBgxQtWqVdOsWbNUokQJeXt7q3fv3kpNTdWoUaPk7++vwoULa+TIkTbbuXjxonr06KFChQrJ19dXTz75pPbu3Ztuu1988YVKlSolPz8/Pfvss7p06ZKkm3/Z2bRpkyZOnGj9C8/x48clSZs2bVLt2rXl5uamokWL6rXXXtONGzes2160aJGqVKkiDw8PFShQQI0bN1ZycvL9v5A5jJubm/z9/eXv769q1arptdde08mTJ3X27FkdP35cFotFX3/9tRo0aCB3d3d99dVXdzx/7vQ+S1JycrIiIyPl7e2tokWLauzYsemuNmZ0+0zevHltzuWTJ0+qffv2yps3r/Lnz6+2bdtazxnp5u1agwYNUt68eVWgQAENHTpU//xWhpSUFPXv31+FCxeWu7u7HnvsMW3fvt26/MKFC+rUqZMKFSokDw8PBQcHpwsDyNjt59OtKV++fBn2vdt7OWLECM2dO1fLly+3fjZER0ff8ZxMS0vTu+++q+LFi8vNzc36nYG33FpvyZIleuKJJ+Tp6anQ0FDFxMRY+2R0q963336rWrVqyd3dXQULFtRTTz1lXXbhwgVFRkYqX7588vT0VPPmzXXkyBHr8hMnTqh169bKly+fvLy8VKlSJa1atSoTXmXcr9s/a+rWratXX33VZvnZs2eVJ08ebd68WZJUqlQpvffee4qMjJSvr69eeOEFSdLixYtVqVIlubm5qVSpUho7dqzNdj755BMFBwfL3d1dRYoU0TPPPJP1B4f7dutzLDAwUBEREWrcuLHWr18vyfznR0afI8uWLZPFYrHO3/qZOX36dAUGBsrT01Pt27dXQkKCtc/27dvVpEkTFSxYUH5+fmrQoIF27dplXW4YhkaMGKESJUrIzc1NAQEB6t+/fxa9IjkHwSkH+eabb1S+fHmFhIToueee06xZs9L9wng3R48e1erVq7VmzRrNnz9fn332mVq2bKk///xTmzZt0kcffaQ333xTP//8s3Wddu3a6cyZM1q9erV27typRx55RI0aNdL58+dttrts2TKtWLFCK1as0KZNm/Thhx9KkiZOnKiwsDD17NlTcXFxiouLU2BgoP766y+1aNFCtWrV0t69ezV16lR99tln1ucW4uLi1LFjR3Xr1k0HDhxQdHS0nn76aYeOF+klJSXpyy+/VFBQkAoUKGBtf+211zRgwAAdOHBA4eHhGa57t/dZkoYMGaJNmzZp+fLlWrdunaKjo21+CNjj+vXrCg8Pl4+Pj3744Qdt2bJF3t7eatasma5duyZJGjt2rObMmaNZs2bpxx9/1Pnz57V06VKb7QwdOlSLFy/W3LlztWvXLgUFBSk8PNx63r711lv67bfftHr1ah04cEBTp05VwYIFHaoVd2f2Xr7yyitq3769mjVrZv1sqFu3rnX9f56TEydO1NixYzVmzBj98ssvCg8PV5s2bWyCjCS98cYbeuWVV7Rnzx6VK1dOHTt2tPmDzO1Wrlypp556Si1atNDu3bu1YcMG1a5d27q8S5cu2rFjh/73v/8pJiZGhmGoRYsW1oEp+vTpo5SUFG3evFn79u3TRx99JG9v7yx4NXE/OnXqpAULFtj8/Pj6668VEBCgxx9/3No2ZswYhYaGavfu3Xrrrbe0c+dOtW/fXs8++6z27dunESNG6K233rL+oWfHjh3q37+/3n33XR06dEhr1qxR/fr1H/Th4T7t379fW7dutV5hNPv5Ya/ff/9d33zzjb799lutWbNGu3fvVu/eva3LL126pM6dO+vHH3/UTz/9pODgYLVo0cL6B8nFixdr/Pjxmj59uo4cOaJly5apSpUqmXfgOZWBHKNu3brGhAkTDMMwjOvXrxsFCxY0Nm7caBiGYWzcuNGQZFy4cMEwDMOYPXu24efnZ113+PDhhqenp5GYmGhtCw8PN0qVKmWkpqZa20JCQoyoqCjDMAzjhx9+MHx9fY2rV6/a1FG2bFlj+vTpd9zukCFDjEcffdQ636BBA2PAgAE223j99deNkJAQIy0tzdo2ZcoUw9vb20hNTTV27txpSDKOHz/u4KuE23Xu3NlwcXExvLy8DC8vL0OSUbRoUWPnzp2GYRjGsWPHDEnW8+oWe86f29/nS5cuGa6ursY333xjXX7u3DnDw8PD5r2XZCxdutRmX35+fsbs2bMNwzCML774It15kZKSYnh4eBhr1641DMMwihYtaowaNcq6/Pr160bx4sWNtm3bGoZhGElJSUaePHmMr776ytrn2rVrRkBAgHW91q1bG127drXnJcRt/nk+3ZpGjhxpGIbt+2vPe9m5c2fr+3bLnc7JgIAA635uqVWrltG7d2+b9WbOnGld/uuvvxqSjAMHDhiGkf68DgsLMzp16pThsR4+fNiQZGzZssXa9vfffxseHh7W87xKlSrGiBEj7vqaIetkdP7ccvu5eObMGSN37tzG5s2brcvDwsKMV1991TpfsmRJIyIiwmYb//3vf40mTZrYtA0ZMsSoWLGiYRiGsXjxYsPX19fmcxHZ3+2fY25uboYkI1euXMaiRYvs+vnxz88RwzCMpUuXGrf/Sj58+HDDxcXF+PPPP61tq1evNnLlymXExcVlWFdqaqrh4+NjfPvtt4ZhGMbYsWONcuXKGdeuXcusQ4dhGFxxyiEOHTqkbdu2qWPHjpKk3Llzq0OHDvrss8/s3kapUqXk4+NjnS9SpIgqVqyoXLly2bSdOXNGkrR3714lJSWpQIEC8vb2tk7Hjh3T0aNH77jdokWLWrdxJwcOHFBYWJjNpe169eopKSlJf/75p0JDQ9WoUSNVqVJF7dq106effqoLFy7Yfaz4P0888YT27NmjPXv2aNu2bQoPD1fz5s114sQJa5+aNWuabudu7/PRo0d17do1Pfroo9bl+fPnV0hIiEO17t27V7///rt8fHys51v+/Pl19epVHT16VAkJCYqLi7PZT+7cuW3qP3r0qK5fv6569epZ2/LkyaPatWvrwIEDkqSXXnpJCxYsULVq1TR06FBt3brVoTpzstvPp1tTr1690vUzey/N3P6eJiYm6tSpUzbvqXTzM+PWe3pL1apVrf8uWrSoJN3x82jPnj1q1KhRhssOHDig3Llz25xrBQoUUEhIiHWf/fv31/vvv6969epp+PDhpoOuwDkKFSqkpk2b6quvvpIkHTt2TDExMerUqZNNv39+Dh44cCDDc+7IkSNKTU1VkyZNVLJkSZUpU0bPP/+8vvrqK12+fDlrDwaZ4tbn2M8//6zOnTura9eu+s9//mPXzw97lShRQsWKFbPOh4WFKS0tTYcOHZIknT59Wj179lRwcLD8/Pzk6+urpKQk6/PH7dq105UrV1SmTBn17NlTS5cuvePVc9iPwSFyiM8++0w3btywGQzCMAy5ublp8uTJdm0jT548NvO3RpT5Z1taWpqkm7d1FS1aVNHR0em2dfv9vXfbxr1ycXHR+vXrtXXrVq1bt06TJk3SG2+8oZ9//lmlS5e+r23nNF5eXgoKCrLOz5w5U35+fvr000/Vo0cPax8zmfE+WyyWdLdb3v59PElJSapRo4b1F5zbFSpUyKF93c2t4Lhq1SqtX79ejRo1Up8+fTRmzJhM28fD6p/n053c73tpzzmZkdvP01t/mLnTeXq/A0X06NFD4eHhWrlypdatW6eoqCiNHTtW/fr1u6/tIvN16tRJ/fv316RJkzRv3jxVqVIl3W1Pjp5zPj4+2rVrl6Kjo7Vu3Tq9/fbbGjFihLZv386w99nc7Z9js2bNUmhoqD777DPVqlXLdN1cuXLd9eeYvTp37qxz585p4sSJKlmypNzc3BQWFma9LT0wMFCHDh3Sd999p/Xr16t3794aPXq0Nm3alO7nMezHFacc4MaNG/r88881duxYm7/y7t27VwEBAZo/f36W7PeRRx5RfHy8cufOraCgIJvJkedBXF1dlZqaatNWoUIF6zMDt2zZskU+Pj4qXry4pJu/9NSrV0/vvPOOdu/eLVdX13TPssBxFotFuXLl0pUrVzJtm2XLllWePHlsno+7cOFCuqGfCxUqpLi4OOv8kSNHbP5C+8gjj+jIkSMqXLhwunPOz89Pfn5+Klq0qM1+bty4oZ07d9rU4urqqi1btljbrl+/ru3bt6tixYo2tXTu3FlffvmlJkyYoBkzZmTOiwFJ5u+llPFnQ0Z8fX0VEBBg855KNz8zbn9PHVW1alVt2LAhw2UVKlTQjRs3bM61c+fO6dChQzb7DAwMVK9evbRkyRINHjxYn3766T3Xg6zTtm1bXb16VWvWrNG8efPSXW3KSIUKFTI858qVKycXFxdJN694N27cWKNGjdIvv/yi48eP6/vvv8+SY0DWyJUrl15//XW9+eabdv38KFSokC5dumQzWFVGX40QGxurU6dOWed/+ukn5cqVy3onxpYtW9S/f3+1aNHCOgDJ33//bbMNDw8PtW7dWh9//LGio6MVExOjffv2Zebh5zhcccoBVqxYoQsXLqh79+7WXzhu+c9//qPPPvtMo0ePzvT9Nm7cWGFhYYqIiNCoUaNUrlw5nTp1yvpAtT23d0k3b/H6+eefdfz4cevtOr1799aECRPUr18/9e3bV4cOHdLw4cM1aNAg5cqVSz///LM2bNigpk2bqnDhwvr555919uxZVahQIdOP82GXkpKi+Ph4STfDzOTJk5WUlKTWrVtn2j68vb3VvXt3DRkyRAUKFFDhwoX1xhtv2NwGKklPPvmkJk+erLCwMKWmpurVV1+1+ctZp06dNHr0aLVt29Y6gtqJEye0ZMkSDR06VMWLF9eAAQP04YcfKjg4WOXLl9e4ceOsX/ws3fxL4ksvvaQhQ4Yof/78KlGihEaNGqXLly+re/fukqS3335bNWrUUKVKlZSSkqIVK1Zwbtnp9vPplty5c6f7Y4o972WpUqW0du1aHTp0SAUKFEj3+Xa7IUOGaPjw4SpbtqyqVaum2bNna8+ePRle0bLX8OHD1ahRI5UtW1bPPvusbty4oVWrVunVV19VcHCw2rZtq549e2r69Ony8fHRa6+9pmLFiqlt27aSpIEDB6p58+YqV66cLly4oI0bN3IePWAJCQnpfmm9feCbW7y8vBQREaG33npLBw4csN72fjeDBw9WrVq19N5776lDhw6KiYnR5MmT9cknn0i6+bP5jz/+UP369ZUvXz6tWrVKaWlpDt+iDOdr166dhgwZoqlTp5r+/Hj00Ufl6emp119/Xf3799fPP/+c4SjH7u7u6ty5s8aMGaPExET1799f7du3tw6VHxwcrC+++EI1a9ZUYmKihgwZYnMVfM6cOUpNTbXu78svv5SHh4dKliz5QF6Th5ZzH7HCg9CqVSujRYsWGS77+eefDUnGxIkTTQeHCA0NtVk3owdr/zmQQ2JiotGvXz8jICDAyJMnjxEYGGh06tTJiI2NveN2x48fb5QsWdI6f+jQIaNOnTqGh4eHIck4duyYYRiGER0dbdSqVctwdXU1/P39jVdffdW4fv26YRiG8dtvvxnh4eFGoUKFDDc3N6NcuXLGpEmT7Hq98H86d+5sSLJOPj4+Rq1atYxFixYZhvF/D9Tv3r3bZj17zp9/vs+XLl0ynnvuOcPT09MoUqSIMWrUqHTn019//WU0bdrU8PLyMoKDg41Vq1bZDA5hGIYRFxdnREZGGgULFjTc3NyMMmXKGD179jQSEhIMw7g5GMSAAQMMX19fI2/evMagQYOMyMhIm3P5ypUrRr9+/azbqFevnrFt2zbr8vfee8+oUKGC4eHhYeTPn99o27at8ccff9zTa5yT/PN8ujWFhIQYhpF+8A+z9/LMmTNGkyZNDG9vb0OSsXHjxjuek6mpqcaIESOMYsWKGXny5DFCQ0ON1atXW5dntN6FCxes2zWMjB/qXrx4sVGtWjXD1dXVKFiwoPH0009bl50/f954/vnnDT8/P8PDw8MIDw83Dh8+bF3et29fo2zZsoabm5tRqFAh4/nnnzf+/vvv+3iF4Yg7nY/du3fPcCCaVatWGZKM+vXrp9tWyZIljfHjx6drX7RokVGxYkUjT548RokSJYzRo0dbl/3www9GgwYNjHz58hkeHh5G1apVja+//jqzDxOZ7E6DikRFRRmFChUykpKS7vrzwzBuDgYRFBRkeHh4GK1atTJmzJiRbnCI0NBQ45NPPjECAgIMd3d345lnnjHOnz9v7bNr1y6jZs2ahru7uxEcHGwsXLjQ5jxcunSp8eijjxq+vr6Gl5eXUadOHeO7777LktckJ7EYBuMzA8ieGjZsqGrVqmnChAnOLgUAgAdixIgRWrZsWYa38MG5eMYJAAAAAEwQnAAAAADABLfqAQAAAIAJrjgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAAACYIDgBAAAAgAmCEwAAmSw6OloWi0UXL150dikAgExCcAIAOFV8fLz69eunMmXKyM3NTYGBgWrdurU2bNhg1/pz5sxR3rx5s7ZIB9WtW1dxcXHy8/NzdikAgEyS29kFAAByruPHj6tevXrKmzevRo8erSpVquj69etau3at+vTpo4MHDzq7RIddv35drq6u8vf3d3YpAIBMxBUnAIDT9O7dWxaLRdu2bdN//vMflStXTpUqVdKgQYP0008/SZLGjRunKlWqyMvLS4GBgerdu7eSkpIk3bwlrmvXrkpISJDFYpHFYtGIESMkSSkpKXrllVdUrFgxeXl56dFHH1V0dLTN/j/99FMFBgbK09NTTz31lMaNG5fu6tXUqVNVtmxZubq6KiQkRF988YXNcovFoqlTp6pNmzby8vLSyJEj092qd+7cOXXs2FHFihWTp6enqlSpovnz52f66wkAyDoEJwCAU5w/f15r1qxRnz595OXllW75rQCTK1cuffzxx/r11181d+5cff/99xo6dKikm7fETZgwQb6+voqLi1NcXJxeeeUVSVLfvn0VExOjBQsW6JdfflG7du3UrFkzHTlyRJK0ZcsW9erVSwMGDNCePXvUpEkTjRw50qaGpUuXasCAARo8eLD279+vF198UV27dtXGjRtt+o0YMUJPPfWU9u3bp27duqU7lqtXr6pGjRpauXKl9u/frxdeeEHPP/+8tm3bdt+vIwDgwbAYhmE4uwgAQM6zbds2Pfroo1qyZImeeuopu9dbtGiRevXqpb///lvSzWecBg4caDMQQ2xsrMqUKaPY2FgFBARY2xs3bqzatWvrgw8+0LPPPqukpCStWLHCuvy5557TihUrrNuqV6+eKlWqpBkzZlj7tG/fXsnJyVq5cqWkm1ecBg4cqPHjx1v7REdH64knntCFCxfu+PxVq1atVL58eY0ZM8buYwcAOA9XnAAATmHv3+2+++47NWrUSMWKFZOPj4+ef/55nTt3TpcvX77jOvv27VNqaqrKlSsnb29v67Rp0yYdPXpUknTo0CHVrl3bZr1/zh84cED16tWzaatXr54OHDhg01azZs27HkNqaqree+89ValSRfnz55e3t7fWrl2r2NhY0+MHAGQPDA4BAHCK4OBgWSyWuw4Acfz4cbVq1UovvfSSRo4cqfz58+vHH39U9+7dde3aNXl6ema4XlJSklxcXLRz5065uLjYLPP29s7U45CU4a2Gtxs9erQmTpyoCRMmWJ/XGjhwoK5du5bptQAAsgZXnAAATpE/f36Fh4drypQpSk5OTrf84sWL2rlzp9LS0jR27FjVqVNH5cqV06lTp2z6ubq6KjU11aatevXqSk1N1ZkzZxQUFGQz3RrtLiQkRNu3b7dZ75/zFSpU0JYtW2zatmzZoooVKzp0rFu2bFHbtm313HPPKTQ0VGXKlNHhw4cd2gYAwLkITgAAp5kyZYpSU1NVu3ZtLV68WEeOHNGBAwf08ccfKywsTEFBQbp+/bomTZqkP/74Q1988YWmTZtms41SpUopKSlJGzZs0N9//63Lly+rXLly6tSpkyIjI7VkyRIdO3ZM27ZtU1RUlPXZpH79+mnVqlUaN26cjhw5ounTp2v16tWyWCzWbQ8ZMkRz5szR1KlTdeTIEY0bN05LliyxDkBhr+DgYK1fv15bt27VgQMH9OKLL+r06dP3/wICAB4YghMAwGnKlCmjXbt26YknntDgwYNVuXJlNWnSRBs2bNDUqVMVGhqqcePG6aOPPlLlypX11VdfKSoqymYbdevWVa9evdShQwcVKlRIo0aNkiTNnj1bkZGRGjx4sEJCQhQREaHt27erRIkSkm4+qzRt2jSNGzdOoaGhWrNmjV5++WW5u7tbtx0REaGJEydqzJgxqlSpkqZPn67Zs2erYcOGDh3nm2++qUceeUTh4eFq2LCh/P39FRERcV+vHQDgwWJUPQAA/r+ePXvq4MGD+uGHH5xdCgAgm2FwCABAjjVmzBg1adJEXl5eWr16tebOnatPPvnE2WUBALIhrjgBAHKs9u3bKzo6WpcuXVKZMmXUr18/9erVy9llAQCyIYITAAAAAJhgcAgAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAATBCcAAAAAMEFwAgAAAAAT/w9N/QZcP8HePwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##Criar um gráfico de barras mostrando o total de vendas por categoria:\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='green')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?**\n",
+    "\n",
+    "Com base no gráfico o que mais se destaca em termos totais de vendas é a categoria de eletrônicos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "##**Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos':\n",
+    "\n",
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?**\n",
+    "\n",
+    "Podemos obervar que houve um pico de vendas de eletrônicos a segunda quinzena de Outubro.\n",
+    "\n"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/dados_vendas.csv b/exercicios/para-casa/dados_vendas.csv
new file mode 100644
index 0000000..dde99d0
--- /dev/null
+++ b/exercicios/para-casa/dados_vendas.csv
@@ -0,0 +1,366 @@
+Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total
+2023-06-06,Brinquedos,35,700.59,24520.65
+2023-07-12,Eletrônicos,42,780.76,32791.92
+2023-09-12,Brinquedos,36,901.62,32458.32
+2023-03-05,Alimentos,68,221.3,15048.400000000001
+2023-07-19,Alimentos,52,971.42,50513.84
+2023-08-02,Roupas,110,493.28,54260.799999999996
+2023-03-05,Brinquedos,83,890.92,73946.36
+2023-02-08,Roupas,8,778.31,6226.48
+2023-08-31,Eletrônicos,26,759.32,19742.32
+2023-12-20,Alimentos,133,1032.09,137267.97
+2023-03-08,Alimentos,46,1089.95,50137.700000000004
+2023-05-16,Livros,72,681.37,49058.64
+2023-08-08,Livros,32,822.59,26322.88
+2023-01-26,Eletrônicos,3,997.77,2993.31
+2023-05-24,Eletrônicos,139,97.94,13613.66
+2023-06-21,Eletrônicos,151,908.09,137121.59
+2023-09-19,Roupas,31,624.35,19354.850000000002
+2023-01-01,Livros,24,504.87,12116.880000000001
+2023-10-23,Eletrônicos,104,133.0,13832.0
+2023-10-22,Brinquedos,47,610.74,28704.78
+2023-05-17,Alimentos,119,712.99,84845.81
+2023-10-27,Alimentos,96,293.64,28189.44
+2023-07-13,Alimentos,117,650.37,76093.29
+2023-03-11,Brinquedos,71,231.72,16452.12
+2023-01-07,Roupas,106,57.66,6111.96
+2023-08-10,Alimentos,1,826.11,826.11
+2023-10-30,Brinquedos,119,1069.06,127218.14
+2023-01-25,Brinquedos,30,93.06,2791.8
+2023-05-12,Brinquedos,112,731.9,81972.8
+2023-01-23,Livros,113,176.07,19895.91
+2023-09-05,Alimentos,97,609.18,59090.45999999999
+2023-03-20,Eletrônicos,106,707.52,74997.12
+2023-02-04,Livros,29,893.02,25897.579999999998
+2023-12-24,Alimentos,81,675.17,54688.77
+2023-02-19,Roupas,140,59.12,8276.8
+2023-02-19,Eletrônicos,102,482.1,49174.200000000004
+2023-11-09,Alimentos,46,49.14,2260.44
+2023-12-11,Roupas,151,796.52,120274.52
+2023-05-04,Brinquedos,72,299.63,21573.36
+2023-09-15,Roupas,51,878.63,44810.13
+2023-06-13,Alimentos,117,347.36,40641.12
+2023-05-15,Brinquedos,110,496.33,54596.299999999996
+2023-05-19,Brinquedos,9,687.28,6185.5199999999995
+2023-09-25,Livros,82,825.55,67695.09999999999
+2023-06-24,Alimentos,8,941.58,7532.64
+2023-09-05,Alimentos,84,611.94,51402.96000000001
+2023-05-18,Eletrônicos,109,929.01,101262.09
+2023-04-19,Roupas,58,955.51,55419.58
+2023-01-14,Brinquedos,13,775.85,10086.050000000001
+2023-10-18,Eletrônicos,7,291.92,2043.44
+2023-07-03,Roupas,30,494.33,14829.9
+2023-07-16,Brinquedos,20,958.92,19178.399999999998
+2023-07-19,Alimentos,59,506.96,29910.64
+2023-02-27,Roupas,113,574.44,64911.72000000001
+2023-05-26,Roupas,140,704.92,98688.79999999999
+2023-06-04,Eletrônicos,84,672.86,56520.24
+2023-05-16,Roupas,24,820.84,19700.16
+2023-07-22,Alimentos,28,652.92,18281.76
+2023-03-30,Roupas,98,1071.63,105019.74
+2023-10-05,Eletrônicos,109,288.09,31401.809999999998
+2023-07-11,Brinquedos,130,847.69,110199.70000000001
+2023-02-13,Eletrônicos,126,706.08,88966.08
+2023-06-02,Brinquedos,38,958.4,36419.2
+2023-04-09,Alimentos,13,383.61,4986.93
+2023-04-10,Brinquedos,78,601.96,46952.880000000005
+2023-03-21,Livros,112,922.6,103331.2
+2023-06-17,Eletrônicos,42,301.53,12664.259999999998
+2023-04-26,Alimentos,45,774.42,34848.9
+2023-04-06,Eletrônicos,121,21.85,2643.8500000000004
+2023-09-27,Alimentos,16,121.55,1944.8
+2023-01-05,Livros,115,759.21,87309.15000000001
+2023-10-30,Brinquedos,3,620.36,1861.08
+2023-08-31,Brinquedos,143,1093.5,156370.5
+2023-12-14,Roupas,17,358.65,6097.049999999999
+2023-07-28,Alimentos,24,379.91,9117.84
+2023-06-26,Eletrônicos,31,677.24,20994.44
+2023-07-23,Alimentos,40,309.35,12374.0
+2023-11-14,Roupas,157,1067.7,167628.9
+2023-09-04,Roupas,14,455.98,6383.72
+2023-03-13,Alimentos,56,53.49,2995.44
+2023-05-23,Livros,41,821.23,33670.43
+2023-11-15,Brinquedos,106,655.75,69509.5
+2023-05-11,Roupas,79,544.57,43021.030000000006
+2023-08-27,Brinquedos,105,367.8,38619.0
+2023-06-24,Eletrônicos,84,196.0,16464.0
+2023-06-21,Alimentos,66,1014.43,66952.37999999999
+2023-07-08,Roupas,80,580.63,46450.4
+2023-12-23,Eletrônicos,139,1067.61,148397.78999999998
+2023-01-24,Livros,102,15.61,1592.22
+2023-01-03,Livros,56,902.66,50548.96
+2023-07-18,Roupas,158,729.69,115291.02
+2023-03-23,Livros,65,774.42,50337.299999999996
+2023-12-20,Alimentos,99,333.47,33013.530000000006
+2023-08-30,Alimentos,57,495.3,28232.100000000002
+2023-01-24,Livros,105,922.45,96857.25
+2023-09-16,Roupas,66,408.19,26940.54
+2023-11-20,Brinquedos,46,1078.08,49591.67999999999
+2023-10-27,Eletrônicos,132,740.15,97699.8
+2023-12-26,Eletrônicos,153,61.77,9450.810000000001
+2023-09-19,Eletrônicos,107,639.1,68383.7
+2023-02-07,Brinquedos,10,589.11,5891.1
+2023-06-28,Eletrônicos,135,736.47,99423.45
+2023-02-28,Brinquedos,86,1062.23,91351.78
+2023-12-03,Alimentos,111,435.3,48318.3
+2023-03-23,Alimentos,73,655.02,47816.46
+2023-04-07,Alimentos,20,928.89,18577.8
+2023-05-06,Livros,114,128.89,14693.46
+2023-05-03,Livros,21,211.81,4448.01
+2023-11-27,Eletrônicos,148,1092.04,161621.91999999998
+2023-06-10,Livros,62,747.77,46361.74
+2023-12-26,Livros,152,90.0,13680.0
+2023-10-05,Eletrônicos,4,664.12,2656.48
+2023-12-23,Alimentos,23,815.63,18759.49
+2023-12-18,Eletrônicos,99,165.65,16399.350000000002
+2023-12-03,Roupas,47,15.87,745.89
+2023-04-02,Alimentos,9,356.44,3207.96
+2023-09-22,Livros,88,724.95,63795.600000000006
+2023-07-10,Brinquedos,109,336.93,36725.37
+2023-12-14,Brinquedos,49,416.22,20394.780000000002
+2023-06-18,Livros,102,144.73,14762.46
+2023-09-26,Eletrônicos,139,833.83,115902.37000000001
+2023-12-08,Roupas,32,151.79,4857.28
+2023-12-13,Alimentos,79,200.94,15874.26
+2023-07-29,Livros,145,300.37,43553.65
+2023-11-19,Brinquedos,81,641.94,51997.14000000001
+2023-03-04,Brinquedos,89,189.54,16869.059999999998
+2023-09-28,Brinquedos,3,619.14,1857.42
+2023-11-02,Roupas,19,758.04,14402.759999999998
+2023-02-09,Brinquedos,74,597.1,44185.4
+2023-10-15,Livros,8,512.2,4097.6
+2023-02-11,Alimentos,139,359.83,50016.369999999995
+2023-08-26,Roupas,63,914.26,57598.38
+2023-11-22,Alimentos,86,18.22,1566.9199999999998
+2023-08-01,Livros,159,889.44,141420.96000000002
+2023-11-17,Eletrônicos,158,50.54,7985.32
+2023-06-26,Roupas,7,132.72,929.04
+2023-04-12,Eletrônicos,42,65.62,2756.04
+2023-05-17,Eletrônicos,23,637.25,14656.75
+2023-06-18,Eletrônicos,70,1021.65,71515.5
+2023-04-10,Alimentos,35,960.42,33614.7
+2023-10-02,Alimentos,102,833.13,84979.26
+2023-12-21,Eletrônicos,122,969.06,118225.31999999999
+2023-12-13,Eletrônicos,11,634.94,6984.34
+2023-10-22,Eletrônicos,83,169.88,14100.039999999999
+2023-08-30,Eletrônicos,101,1081.25,109206.25
+2023-11-05,Eletrônicos,28,912.93,25562.039999999997
+2023-01-16,Livros,119,969.25,115340.75
+2023-02-11,Roupas,83,841.57,69850.31
+2023-01-13,Alimentos,34,251.08,8536.720000000001
+2023-08-26,Livros,13,332.21,4318.73
+2023-07-13,Alimentos,20,693.63,13872.6
+2023-06-08,Livros,73,117.92,8608.16
+2023-12-09,Eletrônicos,25,988.26,24706.5
+2023-07-03,Brinquedos,153,585.07,89515.71
+2023-03-05,Brinquedos,10,992.33,9923.300000000001
+2023-05-09,Brinquedos,118,182.28,21509.04
+2023-04-27,Eletrônicos,44,30.58,1345.52
+2023-07-19,Alimentos,126,40.78,5138.28
+2023-08-18,Livros,51,505.84,25797.84
+2023-04-30,Roupas,37,812.57,30065.09
+2023-06-13,Brinquedos,88,204.85,18026.8
+2023-09-25,Livros,100,117.21,11721.0
+2023-07-30,Eletrônicos,46,251.73,11579.58
+2023-03-28,Eletrônicos,70,427.61,29932.7
+2023-11-02,Eletrônicos,68,176.6,12008.8
+2023-07-16,Brinquedos,113,1012.35,114395.55
+2023-09-16,Roupas,60,45.63,2737.8
+2023-06-22,Roupas,137,723.42,99108.54
+2023-08-26,Eletrônicos,70,659.41,46158.7
+2023-07-31,Brinquedos,16,809.66,12954.56
+2023-08-22,Eletrônicos,133,322.46,42887.18
+2023-05-15,Brinquedos,145,723.46,104901.70000000001
+2023-05-13,Alimentos,5,614.51,3072.55
+2023-09-23,Brinquedos,18,85.01,1530.18
+2023-09-03,Roupas,61,766.61,46763.21
+2023-10-21,Roupas,91,856.94,77981.54000000001
+2023-04-04,Brinquedos,115,500.9,57603.5
+2023-12-25,Alimentos,33,1064.93,35142.69
+2023-03-25,Eletrônicos,119,1087.45,129406.55
+2023-05-11,Livros,45,797.13,35870.85
+2023-01-28,Livros,159,385.42,61281.780000000006
+2023-03-30,Alimentos,124,388.52,48176.479999999996
+2023-03-29,Roupas,81,1069.55,86633.55
+2023-04-25,Livros,142,463.9,65873.8
+2023-03-15,Brinquedos,110,554.84,61032.4
+2023-05-30,Roupas,47,1073.14,50437.58
+2023-08-29,Eletrônicos,31,61.87,1917.97
+2023-02-27,Livros,76,748.72,56902.72
+2023-08-07,Roupas,87,893.29,77716.23
+2023-12-27,Livros,75,635.12,47634.0
+2023-06-28,Alimentos,20,837.12,16742.4
+2023-06-16,Brinquedos,49,271.17,13287.33
+2023-05-10,Livros,16,140.76,2252.16
+2023-08-28,Brinquedos,136,406.97,55347.920000000006
+2023-07-31,Brinquedos,119,855.59,101815.21
+2023-10-28,Alimentos,51,405.06,20658.06
+2023-12-01,Eletrônicos,20,1096.31,21926.199999999997
+2023-02-08,Roupas,49,406.16,19901.84
+2023-05-23,Eletrônicos,40,118.93,4757.200000000001
+2023-10-27,Eletrônicos,149,717.55,106914.95
+2023-08-09,Roupas,77,696.83,53655.91
+2023-11-01,Alimentos,73,600.3,43821.899999999994
+2023-02-15,Brinquedos,145,260.76,37810.2
+2023-06-08,Brinquedos,76,53.67,4078.92
+2023-06-05,Livros,49,838.17,41070.329999999994
+2023-09-06,Roupas,48,45.94,2205.12
+2023-07-16,Eletrônicos,115,187.62,21576.3
+2023-04-22,Eletrônicos,5,48.09,240.45000000000002
+2023-07-18,Alimentos,10,708.64,7086.4
+2023-06-26,Roupas,79,815.52,64426.08
+2023-01-31,Eletrônicos,150,654.72,98208.0
+2023-03-02,Livros,124,595.18,73802.31999999999
+2023-12-29,Alimentos,21,431.53,9062.13
+2023-01-11,Roupas,17,405.83,6899.11
+2023-02-06,Roupas,62,992.04,61506.479999999996
+2023-08-13,Brinquedos,79,466.7,36869.299999999996
+2023-05-20,Alimentos,48,488.31,23438.88
+2023-02-12,Roupas,150,373.49,56023.5
+2023-02-09,Alimentos,89,420.73,37444.97
+2023-02-07,Eletrônicos,13,690.11,8971.43
+2023-05-20,Brinquedos,14,276.5,3871.0
+2023-03-27,Livros,29,599.14,17375.06
+2023-02-28,Roupas,54,510.71,27578.34
+2023-09-13,Roupas,123,1028.63,126521.49000000002
+2023-07-29,Livros,67,929.77,62294.59
+2023-06-01,Eletrônicos,143,703.19,100556.17000000001
+2023-01-02,Roupas,57,735.26,41909.82
+2023-09-07,Brinquedos,73,198.3,14475.900000000001
+2023-03-13,Roupas,156,582.14,90813.84
+2023-04-11,Eletrônicos,130,217.93,28330.9
+2023-11-27,Brinquedos,131,793.62,103964.22
+2023-11-15,Livros,48,15.69,753.12
+2023-10-03,Alimentos,135,335.39,45277.65
+2023-05-10,Alimentos,30,403.06,12091.8
+2023-10-13,Eletrônicos,19,174.07,3307.33
+2023-12-14,Livros,21,625.44,13134.240000000002
+2023-01-19,Eletrônicos,133,362.91,48267.030000000006
+2023-07-27,Livros,74,368.72,27285.280000000002
+2023-04-24,Alimentos,25,849.81,21245.25
+2023-03-02,Eletrônicos,13,368.84,4794.92
+2023-05-11,Roupas,57,337.87,19258.59
+2023-07-13,Brinquedos,12,959.68,11516.16
+2023-04-05,Livros,62,907.03,56235.86
+2023-06-19,Eletrônicos,140,538.45,75383.0
+2023-02-08,Brinquedos,48,105.29,5053.92
+2023-01-19,Brinquedos,137,112.24,15376.88
+2023-04-05,Livros,131,621.67,81438.76999999999
+2023-02-16,Livros,35,1049.87,36745.45
+2023-04-22,Roupas,41,499.95,20497.95
+2023-04-08,Alimentos,64,153.35,9814.4
+2023-09-02,Livros,34,214.39,7289.259999999999
+2023-08-11,Roupas,39,615.03,23986.17
+2023-04-19,Roupas,59,743.95,43893.05
+2023-05-27,Alimentos,102,196.64,20057.28
+2023-04-22,Roupas,126,839.72,105804.72
+2023-06-28,Roupas,144,495.81,71396.64
+2023-05-22,Livros,137,23.22,3181.14
+2023-11-16,Livros,2,709.47,1418.94
+2023-05-05,Livros,119,205.33,24434.27
+2023-07-09,Eletrônicos,16,810.92,12974.72
+2023-05-08,Brinquedos,104,601.64,62570.56
+2023-01-18,Livros,50,705.99,35299.5
+2023-01-10,Roupas,123,136.21,16753.83
+2023-05-07,Brinquedos,42,116.86,4908.12
+2023-07-09,Eletrônicos,43,743.98,31991.14
+2023-10-31,Brinquedos,148,1019.09,150825.32
+2023-03-06,Brinquedos,38,203.79,7744.0199999999995
+2023-07-22,Eletrônicos,134,546.57,73240.38
+2023-08-04,Eletrônicos,96,291.68,28001.28
+2023-08-16,Eletrônicos,104,662.7,68920.8
+2023-12-31,Alimentos,61,87.04,5309.4400000000005
+2023-09-08,Livros,56,23.89,1337.8400000000001
+2023-10-12,Brinquedos,39,89.73,3499.4700000000003
+2023-06-07,Livros,91,68.44,6228.04
+2023-07-16,Brinquedos,153,714.81,109365.93
+2023-03-02,Alimentos,27,51.84,1399.68
+2023-04-22,Roupas,6,732.44,4394.64
+2023-03-03,Brinquedos,7,943.62,6605.34
+2023-11-23,Brinquedos,34,436.58,14843.72
+2023-10-04,Livros,1,861.85,861.85
+2023-08-26,Brinquedos,103,639.09,65826.27
+2023-05-16,Livros,55,651.95,35857.25
+2023-04-03,Livros,137,471.93,64654.41
+2023-12-27,Roupas,21,1050.27,22055.67
+2023-06-29,Roupas,62,118.25,7331.5
+2023-11-06,Roupas,50,841.65,42082.5
+2023-08-06,Alimentos,123,544.38,66958.74
+2023-12-30,Roupas,35,181.72,6360.2
+2023-04-02,Alimentos,97,368.18,35713.46
+2023-05-01,Brinquedos,137,45.42,6222.54
+2023-01-28,Roupas,158,241.21,38111.18
+2023-08-30,Alimentos,143,1099.08,157168.44
+2023-06-03,Alimentos,58,521.92,30271.359999999997
+2023-10-07,Roupas,16,73.71,1179.36
+2023-11-24,Alimentos,16,594.66,9514.56
+2023-05-18,Alimentos,117,26.27,3073.59
+2023-11-03,Alimentos,121,752.84,91093.64
+2023-02-02,Roupas,40,43.22,1728.8
+2023-08-31,Brinquedos,31,891.71,27643.010000000002
+2023-09-01,Eletrônicos,36,197.48,7109.28
+2023-10-29,Roupas,40,11.29,451.59999999999997
+2023-10-04,Roupas,115,130.45,15001.749999999998
+2023-08-23,Eletrônicos,124,618.41,76682.84
+2023-08-06,Brinquedos,51,739.51,37715.01
+2023-08-02,Eletrônicos,152,267.56,40669.12
+2023-08-17,Eletrônicos,127,77.58,9852.66
+2023-10-06,Alimentos,149,646.56,96337.43999999999
+2023-05-03,Livros,21,212.5,4462.5
+2023-03-05,Eletrônicos,42,375.26,15760.92
+2023-05-26,Alimentos,73,469.75,34291.75
+2023-04-18,Roupas,47,362.33,17029.51
+2023-07-28,Eletrônicos,123,645.4,79384.2
+2023-09-04,Eletrônicos,108,994.49,107404.92
+2023-01-24,Brinquedos,28,243.52,6818.56
+2023-03-26,Alimentos,116,966.6,112125.6
+2023-05-05,Roupas,136,688.54,93641.44
+2023-08-29,Roupas,159,1010.66,160694.94
+2023-08-03,Roupas,45,118.89,5350.05
+2023-01-03,Livros,111,906.72,100645.92
+2023-02-12,Alimentos,19,1058.36,20108.839999999997
+2023-04-01,Eletrônicos,38,1036.31,39379.78
+2023-05-05,Eletrônicos,29,61.9,1795.1
+2023-12-05,Alimentos,123,397.0,48831.0
+2023-11-05,Eletrônicos,158,181.05,28605.9
+2023-05-21,Eletrônicos,64,454.38,29080.32
+2023-09-15,Eletrônicos,118,1092.11,128868.97999999998
+2023-04-08,Brinquedos,1,145.03,145.03
+2023-08-10,Brinquedos,122,58.52,7139.4400000000005
+2023-08-10,Brinquedos,118,1063.05,125439.9
+2023-04-19,Livros,64,966.14,61832.96
+2023-05-01,Eletrônicos,89,460.01,40940.89
+2023-01-05,Brinquedos,94,504.06,47381.64
+2023-02-27,Roupas,129,103.74,13382.46
+2023-08-08,Eletrônicos,106,729.09,77283.54000000001
+2023-07-10,Roupas,37,701.42,25952.539999999997
+2023-04-14,Alimentos,73,33.63,2454.9900000000002
+2023-09-15,Livros,2,702.56,1405.12
+2023-08-29,Brinquedos,68,889.68,60498.24
+2023-09-13,Livros,9,1020.91,9188.19
+2023-02-03,Livros,34,201.52,6851.68
+2023-05-16,Alimentos,141,163.54,23059.14
+2023-05-06,Roupas,102,977.94,99749.88
+2023-12-17,Brinquedos,1,669.03,669.03
+2023-08-20,Alimentos,21,310.27,6515.67
+2023-09-25,Roupas,73,585.84,42766.32
+2023-12-08,Livros,131,701.37,91879.47
+2023-02-28,Brinquedos,48,21.45,1029.6
+2023-02-15,Livros,142,728.76,103483.92
+2023-07-01,Livros,122,1032.96,126021.12000000001
+2023-01-28,Roupas,47,933.14,43857.58
+2023-08-27,Roupas,30,854.82,25644.600000000002
+2023-12-24,Eletrônicos,122,802.17,97864.73999999999
+2023-03-15,Brinquedos,120,97.89,11746.8
+2023-12-16,Eletrônicos,124,14.94,1852.56
+2023-04-15,Eletrônicos,154,978.98,150762.92
+2023-03-16,Alimentos,144,643.55,92671.2
+2023-09-24,Brinquedos,31,498.08,15440.48
+2023-06-06,Eletrônicos,101,447.21,45168.21
+2023-12-27,Livros,123,1079.36,132761.28
+2023-04-08,Roupas,98,647.19,63424.62
+2023-10-20,Brinquedos,35,699.79,24492.649999999998
+2023-09-27,Livros,115,148.39,17064.85
+2023-12-08,Alimentos,1,118.57,118.57
+2023-02-06,Livros,111,219.32,24344.52
+2023-10-25,Eletrônicos,86,583.12,50148.32
diff --git a/exercicios/para-sala/README.md b/exercicios/para-sala/README.md
index bd7182f..dc36d41 100644
--- a/exercicios/para-sala/README.md
+++ b/exercicios/para-sala/README.md
@@ -148,3 +148,10 @@ Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo cert
 - [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico)
 - [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`)
 - [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`)
+
+
+
+# renomear
+
+df = df.rename(columns={'Valor_1': 'estoque da loja1', 'Valor_2': 'Estoque da loja2', 'Valor_3': 'vendas'})
+df.head(10)
\ No newline at end of file
diff --git a/exercicios/para-sala/ativ_extra.ipynb b/exercicios/para-sala/ativ_extra.ipynb
new file mode 100644
index 0000000..44178b0
--- /dev/null
+++ b/exercicios/para-sala/ativ_extra.ipynb
@@ -0,0 +1,643 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dados fictícios para exemplo\n",
+    "dados = {\n",
+    "    'ID_Consulta': [1, 2, 3, 4, 5],\n",
+    "    'Nome_Paciente': ['João Silva', 'Maria Oliveira', 'Carlos Souza', 'Ana Paula', 'Marcos Lima'],\n",
+    "    'Data_Consulta': ['2024-08-01', '2024-08-02', '2024-08-03', '2024-08-04', '2024-08-05'],\n",
+    "    'Sintomas': ['Febre, Tosse', 'Dor de Cabeça', 'Náusea, Vômito', 'Dor de Garganta', 'Cansaço, Falta de Ar'],\n",
+    "    'Diagnostico': ['Gripe', 'Enxaqueca', 'Gastrite', 'Amigdalite', 'Bronquite'],\n",
+    "    'Prescricao': ['Antitérmico, Repouso', 'Analgésico', 'Antiácido, Dieta', 'Antibiótico', 'Broncodilatador'],\n",
+    "    'Duracao_Consulta_Minutos': [30, 45, 25, 40, 35], # Duração em minutos\n",
+    "    'Gravidade_Sintomas': [4, 6, 5, 7, 8] # Gravidade dos sintomas em uma escala de 1 a 10\n",
+    "}\n",
+    "df = pd.DataFrame(dados)"
+   ]
+  },
+  {
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                 0\n",
+       "Nome_Paciente               0\n",
+       "Data_Consulta               0\n",
+       "Sintomas                    0\n",
+       "Diagnostico                 0\n",
+       "Prescricao                  0\n",
+       "Duracao_Consulta_Minutos    0\n",
+       "Gravidade_Sintomas          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                  int64\n",
+       "Nome_Paciente               object\n",
+       "Data_Consulta               object\n",
+       "Sintomas                    object\n",
+       "Diagnostico                 object\n",
+       "Prescricao                  object\n",
+       "Duracao_Consulta_Minutos     int64\n",
+       "Gravidade_Sintomas           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Data_Consulta'] = pd.to_datetime(df['Data_Consulta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Duracao_Consulta_Minutos'] = pd.to_numeric(df['Duracao_Consulta_Minutos'], errors='coerce')\n",
+    "df['Gravidade_Sintomas'] = pd.to_numeric(df['Gravidade_Sintomas'], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                 0\n",
+       "Nome_Paciente               0\n",
+       "Data_Consulta               0\n",
+       "Sintomas                    0\n",
+       "Diagnostico                 0\n",
+       "Prescricao                  0\n",
+       "Duracao_Consulta_Minutos    0\n",
+       "Gravidade_Sintomas          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], color='blue')\n",
+    "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n",
+    "plt.xlabel('Duração da Consulta (minutos)')\n",
+    "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Análise\n",
+    "\n",
+    "\n",
+    "A clínica da ReprogramaHealth retornou uma análise do tempo de consulta dos seus pacientes em relação à gravidade dos seus sintomas.\n",
+    "A escala de gravidade varia de 1 a 10, sendo a partir de 4 os casos mais graves e o tempo teve uma variação de 25 a 45 minutos. \n",
+    "Conforme o gráfico, nota-se que não há correlação entre a gravidade dos sintomas e o tempo de consulta.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Storytelling\n",
+    "\n",
+    "Normalmente o que se espera é que quanto mais grave um sintoma, maior seja o tempo da consulta de cada paciente, porém, através deste gráfico não é possível estabelecer essa relação, já que temos casos menos graves, ou de gravidade média, com uma duração maior ao de um caso grave. Por exemplo, baseado nesses dados notamos que uma condição grave não significa necessariamente que é complexa. O paciente de gravidade 8  teve uma consulta de  35min enquanto um de gravidade 6 teve uma consulta de 45min.\n",
+    "Os sintomas e o diagnóstico tem muita variação, não permitindo criar uma relação entre esses fatores para estabelecer uma análise temporal que justifique o porquê da variação do tempo.\n",
+    "Para uma melhor análise, precisaríamos de uma amostra mais ampliada, que pudesse trazer dados quantitativos que permitissem um olhar mais apurado ao que se refere às consultas na ReprogramaHealth.\n",
+    "Conforme o gráfico, nota-se que não há correlação entre a gravidade dos sintomas e o tempo de consulta, visto que a direção de ambas não estão convergindo, assim, há indícios de que se faz necessários analisar outras variáveis, como variáveis de estratificação ou estabelecer outros testes para entender melhor esse relacionamento. \n",
+    "\n",
+    "Possíveis variáveis:\n",
+    "Idade do paciente\n",
+    "Se é a primeira consulta na clínica\n",
+    "Qual o médico está realizando o atendimento\n",
+    "Tempo dos sintomas\n",
+    "Necessidade de análises clínicas\n",
+    "\n",
+    "Embora o tempo da consulta e a gravidade dos sintomas não tenham uma relação, de acordo com o gráfico, podemos observar que o tempo mínimo de atendimento de pacientes é de 25 minutos, concluindo que é um tempo considerável que está sendo usado na atenção e cuidado de quem frequenta a clínica. É importante salientar que a atenção despendida por pessoa, nos faz chegar a melhores resultados nos diagnósticos, gerando uma maior recuperação e satisfação dos nossos pacientes.\n",
+    "\n",
+    "Observação extra: \n",
+    "Conforme os dados observados, nota-se que faria um maior sentido considerarmos o ‘tempo da consulta’ como a variável resposta (estando no eixo Y), e a ‘gravidade dos sintomas’ estar enquanto variável explicativa (estando no eixo X). \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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HDiA4OBjVq1dHrVq14OjoqLqGvLi/1NRUAI//R/4kS0tLeHp6qp7X5M6dO3j48CEaN25c4rmn+ytrPJrY2toCQJkLwNTUVLi5uZX4/fj4+Kief9KTxVQxe3t7tWv/lyxZgjNnzsDd3R1t2rTBvHnzKm3J2x9//BHt2rWDlZUVHBwc4OjoiM8++6zUnD2pUaNGmDdvHg4fPgxfX1988MEHz3xNce6eLAqK/fDDD0hMTMTHH3+s8bV169bVOOH8jz/+QN++fWFnZwdbW1s4Ojpi2LBhANR//wMHDsTDhw9VhU5ubi62b9+O/v37l3pvm9TUVHh5eZXYR9O5mJaWhpEjR8LBwQE1atSAo6MjOnXqpBZL8XlSlvP74sWLyM7OhpOTk1qx5ujoiNzcXNy+fbvUuMt6nLKe2wMHDkT79u0xduxYODs7Y9CgQUhISChTcSKXy/H+++/j7NmzuHHjBr7++mu0a9cOCQkJmDx58jNfX5a/J210/TyqV69eid+3nZ0d3N3dS7QBUIvh4cOHmDNnjmouTp06deDo6IisrCy183H+/PnIysrCCy+8gObNm2PGjBk4derUM98LERkPrrJFz6Vr164hOzsbXl5eqjaZTFZiUikArffG0LQ614YNGzBy5Ej06dMHM2bMgJOTE8zNzREVFaWabFpWly9fRpcuXdCkSRMsW7YM7u7usLS0xPbt2xEdHa3Tv5rqQ3ni8fLyQrVq1XD69OkKiU3bSkFP/j4HDBiADh064Pvvv8euXbvw0UcfYfHixfjuu+/QvXt3AND6ZVmhUEhejejXX39F79690bFjR6xcuRKurq6wsLDA2rVrsXHjxjL3U7xE7o0bN8q0/HNxzjWtXFb8xb1aNc0f8ZrO7aysLHTq1Am2traYP38+GjVqBCsrKxw7dgzvvfee2u+/Xbt2aNCgARISEjBkyBD873//w8OHDzFw4MAyv9/SKBQKhISE4O7du3jvvffQpEkTVK9eHdevX8fIkSMl/W0olUo4OTnhq6++0vj806OhFc3a2hr79u3DL7/8gp9++gk7d+7EN998g1deeQW7du0q8/no6uqKQYMGoV+/fvD19UVCQgLi4+O1/u6Bsv096Yu2Y5Ulhv/85z9Yu3YtwsPDERgYCDs7O8hkMgwaNEjtHOjYsSMuX76MH374Abt27cLnn3+O6OhoxMbGVvn7OhGZChYk9Fxav349AKhdlmRvb6/xX8xLG2F42ubNm+Hp6YnvvvtO7cvt05dmNWrUCD///DPu3r2rdZTkf//7H/Lz87Ft2za1f7F8+tIRDw8PAMD58+fh6empai8oKMCVK1cQHBysNV5HR0dYW1trvJTh/PnzkuLRxMbGBq+88gr27NmD9PT0Ev/6+TQPDw/s3r0b9+/fV/uX5OLLgorfs65cXV0xceJETJw4Ebdv30br1q2xYMECVUFib2+v8ZKW1NRUtdxqoq2Y2bJlC6ysrPDzzz+r3e9h7dq1ZY47NjYWiYmJWLBgAaKiovDWW2/hhx9+KPU11atXR+fOnbF3715cv34ddevWLfPxNElOTsbff/+N7777Dh07dlS1X7lyReP+AwYMwIoVK5CTk4NvvvkGDRo0QLt27Uo9hoeHB86cOQMhhFo+nz4XT58+jQsXLmDdunVqK5A9vWpS8XlSlvO7UaNG2L17N9q3b6/zUuC6HEeXc9vMzAxdunRBly5dsGzZMixcuBDvv/8+fvnll1L/rjWxsLCAn58fLl68iMzMzHIvIa7tfC/P55GuNm/ejBEjRmDp0qWqtkePHmn8G3ZwcMCoUaMwatQo5ObmomPHjpg3bx4LEqIqgpds0XNnz549iIyMRMOGDTF06FBVe6NGjXDu3DncuXNH1Xby5EkcOHCgzH0X/6vek/+K99tvvyElJUVtv379+kEIgYiIiBJ9FL9WU1/Z2dklvsgGBwfD0tISn3zyidq+X3zxBbKzsxEWFlZqvKGhodi6dSvS0tJU7WfPnsXPP//8zPemKR5t5s6dCyEE3njjDY2XER09ehTr1q0DAPTo0QMKhQIxMTFq+0RHR0Mmk6kKiLJSKBQlLo9ycnKCm5ub2pKyjRo1wqFDh1BQUKBq+/HHH5Genv7MY1SvXh0ASnwZMjc3h0wmUxtpu3r1KrZu3Vqm2K9cuYIZM2agX79+mD17Nj7++GNs27ZNbRlWbebMmQOFQoFhw4ZpzLku/+Kt6fdfUFCAlStXatx/4MCByM/Px7p167Bz504MGDDgmcfo0aMHbty4obYkcl5eHuLi4p4ZixACK1asUNvP1dUVLVu2xLp169R+/4mJifjzzz/V9h0wYAAUCgUiIyNLxFVUVFTq3AtdjlPWc/vu3bsljtOyZUsA0LgMcrGLFy+q/S0Xy8rKQkpKCuzt7fUy2qPtfC/P55GuzM3NS5zDn376aYlR7b///lvtcY0aNeDl5VVqHonIuHCEhKq0HTt24Ny5cygqKsKtW7ewZ88eJCYmwsPDA9u2bYOVlZVq39GjR2PZsmUIDQ3FmDFjcPv2bcTGxsLX11c1QfhZevbsie+++w59+/ZFWFgYrly5gtjYWDRt2lTtC+HLL7+MN954A5988gkuXryIbt26QalU4tdff8XLL7+MyZMno2vXrrC0tESvXr3w1ltvITc3F6tXr4aTkxNu3ryp6svR0RGzZs1CREQEunXrht69e+P8+fNYuXIlXnzxRdU1/tpERERg586d6NChAyZOnIiioiJ8+umn8PX1VbvOuqzxaBMUFIT//ve/mDhxIpo0aaJ2p/bk5GRs27YNH374IQCgV69eePnll/H+++/j6tWraNGiBXbt2oUffvgB4eHhahPYy+L+/fuoV68eXn/9dbRo0QI1atTA7t27cfjwYbV/XR07diw2b96Mbt26YcCAAbh8+TI2bNhQpuP5+/sDAKZMmYLQ0FCYm5tj0KBBCAsLw7Jly9CtWzcMGTIEt2/fxn//+194eXk98zp2IQRGjx4Na2tr1f1e3nrrLWzZsgVTp05FcHAw3NzctL6+Q4cOiImJwX/+8x80btxYdaf2goICXLhwAV999RUsLS3L9K/lQUFBsLe3x4gRIzBlyhTIZDKsX79ea1HTunVreHl54f3330d+fn6ZLtcaN24cYmJiMHz4cBw9ehSurq5Yv359iZsyNmnSBI0aNcI777yD69evw9bWFlu2bNE4xyEqKgphYWF46aWXMHr0aNy9e1d1fj/5N9mpUye89dZbiIqKwokTJ9C1a1dYWFjg4sWL+Pbbb7FixQq1yfZSj1PWc3v+/PnYt28fwsLC4OHhgdu3b2PlypWoV69eqQsDnDx5EkOGDEH37t3RoUMHODg44Pr161i3bh1u3LiB5cuX6+VmiC1btoS5uTkWL16M7OxsyOVy1T2KyvN5pIuePXti/fr1sLOzQ9OmTZGSkoLdu3er3WsFeDxJv3PnzvD394eDgwOOHDmCzZs3l2k+DREZiUpc0YtIb4qXsyzeLC0thYuLiwgJCRErVqxQW8v+SRs2bBCenp7C0tJStGzZUvz8889al/396KOPSrxeqVSKhQsXCg8PDyGXy0WrVq3Ejz/+WKIPIR4vEfzRRx+JJk2aqOLs3r27OHr0qGqfbdu2CT8/P2FlZSUaNGggFi9eLNasWVNiqU4hHi+r2aRJE2FhYSGcnZ3FhAkTxL1798qUr7179wp/f39haWkpPD09RWxsrOpeAk/SJR5tjh49KoYMGSLc3NyEhYWFsLe3F126dBHr1q1TWyb0/v37Ytq0aar9GjduLD766CO1ZZGFeLzsr6blfJ9cwjc/P1/MmDFDtGjRQtSsWVNUr15dtGjRQqxcubLE65YuXSrq1q0r5HK5aN++vThy5EiZlv0tKioS//nPf4Sjo6OQyWRqufviiy9E48aNhVwuF02aNBFr167VmN+nrVixQgAQW7ZsUWtPS0sTtra2okePHqW+vtjx48fF8OHDRf369YWlpaWoXr268PPzE2+//bbacslCPF6K1dfXV2M/Bw4cEO3atRPW1tbCzc1NvPvuu6rlWJ9e/lUIId5//30BQHh5eWnsT9NS26mpqaJ3797CxsZG1KlTR0ydOlW19O6Tx/jzzz9FcHCwqFGjhqhTp44YN26caqnjJ38vQgixZcsW4ePjI+RyuWjatKn47rvvNP5NCiFEXFyc8Pf3F9bW1qJmzZqiefPm4t133xU3btzQ+B6kHKcs53ZSUpJ49dVXhZubm7C0tBRubm5i8ODB4sKFC6XGcOvWLbFo0SLRqVMn4erqKqpVqybs7e3FK6+8IjZv3qy2r7ZlfzUtkavpd7V69Wrh6empWiL8yd9PWT6PtJ1r2mJ4+m/93r17YtSoUaJOnTqiRo0aIjQ0VJw7d67E8t0ffvihaNOmjahVq5awtrYWTZo0EQsWLHjmvWWIyHjIhKiAWWxEpGb//v147733dLo8jIiIiMgUsCAhqiTFlxI8a/I0ERERkSnhHBKiCnTnzh2sWbMGwOMJ4pomHhMRERGZMhYkRBVIoVDgk08+wb179zBs2DD4+fkZOiQiIiIio8Jlf4kqkIuLC65fv468vDzVkrdERERE+qBQKPDBBx+gYcOGsLa2RqNGjRAZGfnMZd+Tk5PRunVryOVyeHl5IT4+vnIC1oIjJEREREREVdDixYvx2WefYd26dfD19cWRI0cwatQo2NnZYcqUKRpfc+XKFYSFhWH8+PH46quvkJSUhLFjx8LV1VXthtKViZPaiYiIiIiqoJ49e8LZ2RlffPGFqq1fv36wtrbGhg0bNL7mvffew08//YQzZ86o2gYNGoSsrCzs3LmzwmPWxORGSJRKJW7cuIGaNWtCJpMZOhwiIiIieooQAvfv34ebmxvMzIxvhsGjR49QUFBQIX0LIUp8R5XL5ZDL5SX2DQoKQlxcHC5cuIAXXngBJ0+exP79+7Fs2TKt/aekpCA4OFitLTQ0FOHh4XqJXwqTK0hu3LgBd3d3Q4dBRERERM+Qnp6OevXqGToMNY8ePUJDjxrIuK2okP5r1KhRYlXOuXPnYt68eSX2nTlzJnJyctCkSROYm5tDoVBgwYIFGDp0qNb+MzIy4OzsrNbm7OyMnJwcPHz4ENbW1np5H7owuYKkZs2aAB6f4La2thV+vMLCQuzatQtdu3aFhYVFhR+vKmFuNGNetGNuNGNetGNuNGNetGNuNKvsvOTk5MDd3V31vc2YFBQUIOO2AqlHG8C2pn5Hb3LuK+Hhf7XE91RNoyMAkJCQgK+++gobN26Er68vTpw4gfDwcLi5uWHEiBF6ja0imVxBUjwEZmtrW2kFiY2NDWxtbfnB9hTmRjPmRTvmRjPmRTvmRjPmRTvmRjND5cWYL6+vUVOGGjX1G58Sun1PnTFjBmbOnIlBgwYBAJo3b47U1FRERUVpLUhcXFxw69YttbZbt27B1tbWIKMjgAkWJERERERE5aUQSij0vDSUQih12j8vL6/EHBtzc3Moldr7CQwMxPbt29XaEhMTERgYqNOx9cn4ZgkREREREdEz9erVCwsWLMBPP/2Eq1ev4vvvv8eyZcvQt29f1T6zZs3C8OHDVY/Hjx+Pv/76C++++y7OnTuHlStXIiEhAdOmTTPEWwDAERIiIiIiIp0pIaCEfodIdO3v008/xQcffICJEyfi9u3bcHNzw1tvvYU5c+ao9rl58ybS0tJUjxs2bIiffvoJ06ZNw4oVK1CvXj18/vnnBrsHCcCChIiIiIioSqpZsyaWL1+O5cuXa91H013YO3fujOPHj1dcYDpiQUJEREREpCMllNBtxkfZ+jRFnENCREREREQGwxESIiIiIiIdKYSAQuh3Dom++6sqOEJCREREREQGwxESIiIiIiIdGcMqW88LFiRERERERDpSQkDBgkQveMkWEREREREZDEdIiIiIiIh0xEu29IcjJEREVZxQ3nv8X1Fg4EiIiIh0x4KEiKiKEvkHoPx7MMSdLo8f33kFypwFEMpsA0dGRPT8K172V9+bKTJoQaJQKPDBBx+gYcOGsLa2RqNGjRAZGQnxjF9GcnIyWrduDblcDi8vL8THx1dOwERERkI8/B/EvdFA4fEnGvOAvA0Qfw9gUUJERFWGQQuSxYsX47PPPkNMTAzOnj2LxYsXY8mSJfj000+1vubKlSsICwvDyy+/jBMnTiA8PBxjx47Fzz//XImRExEZjlDeh8h+H4AAoHzqWQWgSIPI/a8BIiMiMh3KCtpMkUEntR88eBCvvvoqwsLCAAANGjTA119/jd9//13ra2JjY9GwYUMsXboUAODj44P9+/cjOjoaoaGhlRI3EZFBPfofgPxSdlAADxMgar4DmcyysqIiIiKSxKAFSVBQEOLi4nDhwgW88MILOHnyJPbv349ly5ZpfU1KSgqCg4PV2kJDQxEeHq5x//z8fOTn//s/7pycHABAYWEhCgsLy/8mnqH4GJVxrKqGudGMedGOuXlM+egqUGQDoAgAUKSQq/33MQVk+RmQmbtWenzGhOeMZsyLdsyNZpWdl6qQf0UF3IdE3/1VFQYtSGbOnImcnBw0adIE5ubmUCgUWLBgAYYOHar1NRkZGXB2dlZrc3Z2Rk5ODh4+fAhra2u156KiohAREVGin127dsHGxkY/b6QMEhMTK+1YVQ1zoxnzoh1z4/vPpm73iQ+eajn+z0Y8ZzRjXrRjbjSrrLzk5eVVynHKQyEeb/ru0xQZtCBJSEjAV199hY0bN8LX11c1J8TNzQ0jRozQyzFmzZqF6dOnqx7n5OTA3d0dXbt2ha2trV6OUZrCwkIkJiYiJCQEFhYWFX68qoS50Yx50Y65eUwUnoO4O0T1uEghx+4THyC4ZSSqmecDMAMsWsDM4QvDBWkkeM5oxrxox9xoVtl5Kb6ihUyDQQuSGTNmYObMmRg0aBAAoHnz5khNTUVUVJTWgsTFxQW3bt1Sa7t16xZsbW1LjI4AgFwuh1wuL9FuYWFRqR80lX28qoS50Yx50c7kc2PRHMpHbYGC/XhyCmQ183xYVMsHIIOs1luQmXKOnmLy54wWzIt2zI1mlZWXqpD7ipiEbqqT2g26ylZeXh7MzNRDMDc3h1Kp/dcRGBiIpKQktbbExEQEBgZWSIxERMZIVms5YNnhn0fmxa0ArCCz+xgyeXvDBEZERKQjg46Q9OrVCwsWLED9+vXh6+uL48ePY9myZRg9erRqn1mzZuH69ev48ssvAQDjx49HTEwM3n33XYwePRp79uxBQkICfvrpJ0O9DSKiSiczqwGZw2qIwj+B3MfLnstsZ0FWoxdkZjUMHB0R0fNPCRkUkOm9T1Nk0ILk008/xQcffICJEyfi9u3bcHNzw1tvvYU5c+ao9rl58ybS0tJUjxs2bIiffvoJ06ZNw4oVK1CvXj18/vnnXPKXiEySzKIpzGo0BrAdMuvXITMz/ssciIiInmTQgqRmzZpYvnw5li9frnUfTXdh79y5M44f58oxRERERGQYSvF403efpsigc0iIiIiIiMi0GXSEhIiIiIioKlJUwBwSffdXVbAgISIiIiLSEQsS/eElW0REREREZDAcISEiIiIi0pFSyKAUel72V8/9VRUcISEiIiIiIoPhCAkRERERkY44h0R/OEJCREREREQGwxESIiIiIiIdKWAGhZ7/bV+h196qDo6QEBERERGRwXCEhIiIiIhIR6ICVtkSJrrKFgsSIiIiIiIdcVK7/vCSLSIiIiIiMhiOkBARERER6UghzKAQep7ULvTaXZXBERIiIiIiIjIYjpAQEREREelICRmUev63fSVMc4iEIyRERERERGQwHCEhIiIiItIRV9nSH46QEBERERGRwXCEhIiIiIhIRxWzypZpziFhQUJEREREpKPHk9r1e4mVvvurKnjJFhERERERGQxHSIiIiIiIdKSEGRRc9lcvOEJCREREREQGwxESIiIiIiIdcVK7/nCEhIiIiIiIDIYFCRERERGRjpQwq5BNFw0aNIBMJiuxTZo0SeP+8fHxJfa1srLSRzrKhZdsERERERFVQYcPH4ZCoVA9PnPmDEJCQtC/f3+tr7G1tcX58+dVj2Uywy81zIKEiIiIiEhHCiGDQuj3y3xxfzk5OWrtcrkccrm8xP6Ojo5qjxctWoRGjRqhU6dOWo8hk8ng4uKih2j1h5dsERERERHpSPHPsr/63gDA3d0ddnZ2qi0qKuqZ8RQUFGDDhg0YPXp0qaMeubm58PDwgLu7O1599VX88ccfesuJVBwhISIiIiIyIunp6bC1tVU91jQ68rStW7ciKysLI0eO1LqPt7c31qxZAz8/P2RnZ+Pjjz9GUFAQ/vjjD9SrV08foUvCgoSIiIiISEdKYQalnpf9Vf6z7K+tra1aQVIWX3zxBbp37w43Nzet+wQGBiIwMFD1OCgoCD4+Pli1ahUiIyOlBa0HLEiIiIiIiKqw1NRU7N69G999951Or7OwsECrVq1w6dKlCoqsbDiHhIiIiIhIRxU5h0RXa9euhZOTE8LCwnR7DwoFTp8+DVdXV0nH1RcWJEREREREVZRSqcTatWsxYsQIVKumfvHT8OHDMWvWLNXj+fPnY9euXfjrr79w7NgxDBs2DKmpqRg7dmxlh62Gl2wREREREelICeh92V+lhNfs3r0baWlpGD16dInn0tLSYGb27/jDvXv3MG7cOGRkZMDe3h7+/v44ePAgmjZtWo6oy48FCRERERFRFdW1a1eIfybDPy05OVntcXR0NKKjoyshKt2wICEiIiIi0pESZlDqefaDvvurKliQEBERERHpSCHMoNDzsr/67q+qMM13TURERERERoEjJEREREREOlJCBiX0Paldv/1VFQYdIWnQoAFkMlmJbdKkSRr3j4+PL7GvlZVVJUdNRERERET6YtARksOHD0OhUKgenzlzBiEhIejfv7/W19ja2uL8+fOqxzKZaVaSRERERGQ4nEOiPwYtSBwdHdUeL1q0CI0aNUKnTp20vkYmk8HFxaWiQyMiIiIiokpgNHNICgoKsGHDBkyfPr3UUY/c3Fx4eHhAqVSidevWWLhwIXx9fbXun5+fj/z8fNXjnJwcAEBhYSEKCwv19wa0KD5GZRyrqmFuNGNetGNuNGNetGNuNGNetGNuNKvsvFSF/CtgBoWeZz/ou7+qQia03UmlkiUkJGDIkCFIS0uDm5ubxn1SUlJw8eJF+Pn5ITs7Gx9//DH27duHP/74A/Xq1dP4mnnz5iEiIqJE+8aNG2FjY6PX90BERERE5ZeXl4chQ4YgOzsbtra2hg5HTU5ODuzs7PDxkZdgXUO//7b/MLcI7wTsN8r3XZGMpiAJDQ2FpaUl/ve//5X5NYWFhfDx8cHgwYMRGRmpcR9NIyTu7u7IzMyslF90YWEhEhMTERISAgsLiwo/XlXC3GjGvGjH3GjGvGjH3GjGvGjH3GhW2XnJyclBnTp1jPKLeXFBsuRwhwopSN598VejfN8VySgu2UpNTcXu3bvx3Xff6fQ6CwsLtGrVCpcuXdK6j1wuh1wu1/jayvygqezjVSXMjWbMi3bMjWbMi3bMjWbMi3bMjWaVlRfm3rQYxYVqa9euhZOTE8LCwnR6nUKhwOnTp+Hq6lpBkRERERERlaT8Zw6JPjelcXw1r3QGHyFRKpVYu3YtRowYgWrV1MMZPnw46tati6ioKADA/Pnz0a5dO3h5eSErKwsfffQRUlNTMXbsWEOETkREREQmSinMoNTzMr367q+qMHhBsnv3bqSlpWH06NElnktLS4OZ2b+/mHv37mHcuHHIyMiAvb09/P39cfDgQTRt2rQyQyYiIiIiIj0xeEHStWtXaJtXn5ycrPY4Ojoa0dHRlRAVEREREZF2CsiggH5v0K3v/qoK0xwXIiIiIiIio2DwERIiIiIioqqGc0j0xzTfNRERERERGQWOkBARERER6UgB/c/5UOi1t6qDIyRERERERGQwHCEhIiIiItIR55DoDwsSIiIiIiIdKYQZFHouIPTdX1Vhmu+aiIiIiIiMAkdIiIiIiIh0JCCDUs+T2gVvjEhERERERFS5OEJCRERERKQjziHRH9N810REREREZBQ4QkJEREREpCOlkEEp9DvnQ9/9VRUcISEiIiIiIoPhCAkRERERkY4UMINCz/+2r+/+qgoWJEREREREOuIlW/pjmmUYEREREREZBY6QEBERERHpSAkzKPX8b/v67q+qMM13TURERERERoEjJEREREREOlIIGRR6nvOh7/6qCo6QEBERERGRwXCEhIiIiIhIR1xlS384QkJERERERAbDERIiIiIiIh0JYQal0O+/7Qs991dVsCAhIiIiItKRAjIooOdJ7Xrur6owzTKMiIiIiIiMAkdIiIiIiIh0pBT6n4SuFHrtrsrgCAkRERERERkMR0iIiIiIiHSkrIBJ7frur6owzXdNRERERERGgSMkREREREQ6UkIGpZ5XxdJ3f1UFR0iIiIiIiKqgBg0aQCaTldgmTZqk9TXffvstmjRpAisrKzRv3hzbt2+vxIg1Y0FCRERERKQjhZBVyKaLw4cP4+bNm6otMTERANC/f3+N+x88eBCDBw/GmDFjcPz4cfTp0wd9+vTBmTNnyp2P8mBBQkRERESko+JJ7fredOHo6AgXFxfV9uOPP6JRo0bo1KmTxv1XrFiBbt26YcaMGfDx8UFkZCRat26NmJgYfaREMhYkRERERERGJCcnR23Lz89/5msKCgqwYcMGjB49GjKZ5pGWlJQUBAcHq7WFhoYiJSVFL3FLxYKEiIiIiEhHSsigFHre/pnU7u7uDjs7O9UWFRX1zHi2bt2KrKwsjBw5Uus+GRkZcHZ2VmtzdnZGRkZGuXJRXlxli4iIiIjIiKSnp8PW1lb1WC6XP/M1X3zxBbp37w43N7eKDK1CsCAhIiIiItKRqIBlf8U//dna2qoVJM+SmpqK3bt347vvvit1PxcXF9y6dUut7datW3BxcdE9WD3iJVtERERERFXY2rVr4eTkhLCwsFL3CwwMRFJSklpbYmIiAgMDKzK8Z9J5hOTs2bPYtGkTfv31V6SmpiIvLw+Ojo5o1aoVQkND0a9fvzINKxERERERVVXF8z703afOr1EqsXbtWowYMQLVqql/tR8+fDjq1q2rmoMydepUdOrUCUuXLkVYWBg2bdqEI0eOIC4uTi/xS1XmEZJjx44hODgYrVq1wv79+9G2bVuEh4cjMjISw4YNgxAC77//Ptzc3LB48eIyrQZAREREle9+4X0AQJGyyMCREFF57d69G2lpaRg9enSJ59LS0nDz5k3V46CgIGzcuBFxcXFo0aIFNm/ejK1bt6JZs2aVGXIJZR4h6devH2bMmIHNmzejVq1aWvdLSUnBihUrsHTpUsyePVsfMRIREZEeHLl7FNtu/IjruTfQGz3w7qlZ6OjSAb3dekJuzqsbiHQh5b4hZelTV127doUQQuNzycnJJdr69++v9caJhlLmd33hwgVMnDix1GIEeHxt2qZNmzBjxoxn9vm83O6eiIjI2O3KSMSnl1YiLS9d1ZaneIifbu7AonMfIV/BKxuIdKH3JX8r4BKwqqLMBYmFhYVOHZdl/+fldvdERETG7O/8u9iY9g0AQED9X1IFBK48uIrEW0maXkpEVOF0ntSemZmJNWvWICUlRXUTFRcXFwQFBWHkyJFwdHQsc19P77to0aIy3+4eACIjI5GYmIiYmBjExsbq+laIiIhMwr47v0IGWYlipJiAwO5be9DTrUclR0ZUdSkrYNlfffdXVehUkBw+fBihoaGwsbFBcHAwXnjhBQCP1y/+5JNPsGjRIvz8888ICAjQOZDi291Pnz691NvdT58+Xa0tNDQUW7du1dpvfn6+2gT7nJwcAEBhYSEKCwt1jlNXxceojGNVNcyNZsyLdsyNZsyLdszNYxkPMmCuNIfZPxdGVFNWU/svANzPv48H+XmwNNPtiojnDc8ZzSo7L8y/aZEJbbNgNGjXrh1atGiB2NjYEkWDEALjx4/HqVOnkJKSonMgCQkJGDJkCNLS0rTeYdLS0hLr1q3D4MGDVW0rV65EREREiZu8FJs3bx4iIiJKtG/cuBE2NjY6x0lEREREFSsvLw9DhgxBdna2TjcIrAw5OTmws7ND2M9jYVHdUq99Fz4owE+hnxvl+65IOo2QnDx5EvHx8RpHMGQyGaZNm4ZWrVpJCqSibnc/a9YstVGVnJwcuLu7o2vXrpXyiy4sLERiYiJCQkJ0nofzvGNuNGNetGNuNGNetGNuHjuT/SdiLq1UPa6mrIYed7piu+MuFJkVwQxmaGXfEuM8Sy4bamp4zmhW2XkpvqKFTINOBYmLiwt+//13NGnSROPzv//+O5ydnXUOoiJvdy+XyzXeqNHCwqJSP2gq+3hVCXOjGfOiHXOjGfOinannpkXt5qiXUQ9peWlQQqlqLzIrgsJMASETCKvXzaRz9DRTP2e0qay8VIXcG8uNEZ8HOhUk77zzDt58800cPXoUXbp0URUft27dQlJSElavXo2PP/5Y5yB0vd19eHi4qs0YbndPRERkzMxkZnjbOxwrLn6KS7mXVXNJAMDK3AoTGr0Fj+oeBoyQiEyZTgXJpEmTUKdOHURHR2PlypVQKBQAAHNzc/j7+yM+Ph4DBgzQKYDn4Xb3RERExs7Woib+z2cWLuVexvG/TwC3gBEeQ9HWqS1vikgkAUdI9EfnZX8HDhyIgQMHorCwEJmZmQCAOnXqSB5ae9bt7s3M/v1XnOLb3f/f//0fZs+ejcaNGxvF7e6JiIiqAplMhsY1vdDAygPbT25HYJ1AWJgb/6UxRMaIBYn+6FyQFLOwsICrq2u5A3gebndPRERERETSlPlO7WVx+fJlvPLKK/rskoiIiIjI6Aj8e3NEfW1lvhfHc0avBUlubi727t2rzy6JiIiIiOg5ptMlW5988kmpz1+/fr1cwRARERERVQWcQ6I/OhUk4eHhcHV1haWl5rtSFhQU6CUoIiIiIiIyDToVJB4eHli8eLHWpX1PnDgBf39/vQRGRERERGSsOEKiPzrNIfH398fRo0e1Pi+TybSumEVERERERPQ0nUZI5s+fj7y8PK3PN23aFFeuXCl3UERERERExowjJPqjU0HStGnTUp+3sLCAh4dHuQIiIiIiIjJ2LEj0R6/L/hIREREREelC8p3aiYiIiIhMlRAyCD2PaOi7v6qCIyRERERERGQwHCEhIiIiItKREjIooec5JHrur6rgCAkRERERERmM5BGSBw8eYO/evUhLSytxh/YpU6aUOzAiIiIiImPFVbb0R1JBcvz4cfTo0QN5eXl48OABHBwckJmZCRsbGzg5ObEgISIiIiJ6zuXk5GDPnj3w9vaGj4+P5H4kXbI1bdo09OrVC/fu3YO1tTUOHTqE1NRU+Pv74+OPP5YcDBERERFRVVC8ypa+N2M2YMAAxMTEAAAePnyIgIAADBgwAH5+ftiyZYvkfiUVJCdOnMDbb78NMzMzmJubIz8/H+7u7liyZAlmz54tORgiIiIiIjJO+/btQ4cOHQAA33//PYQQyMrKwieffIIPP/xQcr+SChILCwuYmT1+qZOTE9LS0gAAdnZ2SE9PlxwMEREREVFVUDyHRN+bMcvOzoaDgwMAYOfOnejXrx9sbGwQFhaGixcvSu5X0hySVq1a4fDhw2jcuDE6deqEOXPmIDMzE+vXr0ezZs0kB0NEREREVBWY4o0R3d3dkZKSAgcHB+zcuRObNm0CANy7dw9WVlaS+5U0QrJw4UK4uroCABYsWAB7e3tMmDABd+7cQVxcnORgiIiIiIjIOIWHh2Po0KGoV68e3Nzc0LlzZwCPL+Vq3ry55H4ljZAEBASofnZycsLOnTslB0BEREREVNWICrjEythHSCZOnIi2bdsiLS0NISEhqikcnp6e5ZpDwju1ExERERFRmfj7+8Pf31+tLSwsrFx9siAhIiIiItKRACCE/vs0dteuXcO2bds03hx92bJlkvpkQUJERERERM+UlJSE3r17w9PTE+fOnUOzZs1w9epVCCHQunVryf1KmtRORERERGTKlJBVyGbMZs2ahXfeeQenT5+GlZUVtmzZgvT0dHTq1An9+/eX3C8LEiIiIiIieqazZ89i+PDhAIBq1arh4cOHqFGjBubPn4/FixdL7levBcmtW7cwf/58fXZJRERERGR0iu9Dou/NmFWvXl01b8TV1RWXL19WPZeZmSm5X70WJBkZGYiIiNBnl0RERERERscU79Terl077N+/HwDQo0cPvP3221iwYAFGjx6Ndu3aSe5Xp0ntp06dKvX58+fPSw6EiIiIiIiM17Jly5CbmwsAiIiIQG5uLr755hs0btxY8gpbgI4FScuWLSGTySA0rHFW3C6TGXdlR0RERERUXkJUwLK/Rr7ur6enp+rn6tWrIzY2Vi/96lSQODg4YMmSJejSpYvG5//44w/06tVLL4EREREREZFxys3NhVKpVGuztbWV1JdOBYm/vz9u3LgBDw8Pjc9nZWVpHD0hIiIiInqeVMQkdGOf1H7lyhVMnjwZycnJePTokaq9+CophUIhqV+dCpLx48fjwYMHWp+vX78+1q5dKykQIiIiIiIyXsOGDYMQAmvWrIGzs7PepmroVJD07du31Oft7e0xYsSIcgVERERERGTsTHGE5OTJkzh69Ci8vb312i9vjEhERERERM/04osvIj09Xe/9lnmEZNGiRZgyZQpsbGyeue9vv/2GzMxMhIWFlSs4IiIiIiJjpBQyyPQ8omHs9yH5/PPPMX78eFy/fh3NmjWDhYWF2vN+fn6S+i3zCMmff/4JDw8PTJw4ETt27MCdO3dUzxUVFeHUqVNYuXIlgoKCMHDgQNSsWVNSQERERERExq542V99b7q6fv06hg0bhtq1a8Pa2hrNmzfHkSNHtO6fnJwMmUxWYsvIyHjmse7cuYPLly9j1KhRePHFF9GyZUu0atVK9V+pyjxC8uWXX+LkyZOIiYnBkCFDkJOTA3Nzc8jlcuTl5QEAWrVqhbFjx2LkyJGwsrKSHBQREREREZXu3r17aN++PV5++WXs2LEDjo6OuHjxIuzt7Z/52vPnz6st0+vk5PTM14wePRqtWrXC119/bbhJ7S1atMDq1auxatUqnDp1CqmpqXj48CHq1KmDli1bok6dOnoJioiIiIjImD0e0dD3pHbd9l+8eDHc3d3VVrlt2LBhmV7r5OSEWrVq6XS81NRUbNu2DV5eXjq97lkkTWo3MzNDy5Yt8eqrr2LQoEEIDg5mMUJEREREpAc5OTlqW35+vsb9tm3bhoCAAPTv3x9OTk5o1aoVVq9eXaZjtGzZEq6urggJCcGBAwfK9JpXXnkFJ0+eLPP7KCudRkiIiIiIiKhil/11d3dXa587dy7mzZtXYv+//voLn332GaZPn47Zs2fj8OHDmDJlCiwtLbXeisPV1RWxsbEICAhAfn4+Pv/8c3Tu3Bm//fYbWrduXWp8vXr1wrRp03D69Gk0b968xKT23r176/Bu/2XwguT69et47733sGPHDuTl5cHLywtr165FQECAxv2Tk5Px8ssvl2i/efMmXFxcKjpcIiIiIqIKlZ6erja/Qy6Xa9xPqVQiICAACxcuBPB4PveZM2cQGxurtSDx9vZWu49IUFAQLl++jOjoaKxfv77UuMaPHw8AmD9/fonnKu1O7fpW2RNxiIiIiIj0Qfyz6btPALC1tVX7nquNq6srmjZtqtbm4+ODLVu26HTcNm3aYP/+/c/cT6lU6tRvWRm0IKnsiThERERERM+L9u3b4/z582ptFy5cgIeHh079nDhxAq6urvoMTSd6KUhycnKwZ88eeHt7w8fHp8yv27ZtG0JDQ9G/f3/s3bsXdevWxcSJEzFu3LhnvrZly5bIz89Hs2bNMG/ePLRv317jfvn5+WoTgXJycgAAhYWFKCwsLHOsUhUfozKOVdUwN5oxL9oxN5oxL9oxN5oxL9oxN5pVdl6qQv4rcg5JWU2bNg1BQUFYuHAhBgwYgN9//x1xcXGIi4tT7TNr1ixcv34dX375JQBg+fLlaNiwIXx9ffHo0SN8/vnn2LNnD3bt2lWmY+7duxcff/wxzp49CwBo2rQpZsyYgQ4dOugU+5NkQuh+C5YBAwagY8eOmDx5Mh4+fIgWLVrg6tWrEEJg06ZN6NevX5n6Kb5XyfTp09G/f38cPnwYU6dOLfW6t/PnzyM5OVltIs769eu1TsSZN28eIiIiSrRv3LixTHedJyIiIqLKlZeXhyFDhiA7O7tMly5VppycHNjZ2cFz3WyY2+j3vnuKvEf4a8RCnd73jz/+iFmzZuHixYto2LAhpk+frvaP+yNHjsTVq1eRnJwMAFiyZAni4uJw/fp12NjYwM/PD3PmzNE4R/tpGzZswKhRo/Daa6+pBgMOHDiA77//HvHx8RgyZIjubxoSCxIXFxf8/PPPaNGiBTZu3Ii5c+fi5MmTWLduHeLi4nD8+PEy9WNpaYmAgAAcPHhQ1TZlyhQcPnwYKSkpZY6nU6dOqF+/vsaJOJpGSNzd3ZGZmVkpJ3hhYSESExMREhJSYiUCU8fcaMa8aMfcaMa8aMfcaMa8aMfcaFbZecnJyUGdOnVYkBgZHx8fvPnmm5g2bZpa+7Jly7B69WrVqImuJF2ylZ2dDQcHBwDAzp070a9fP9jY2CAsLAwzZswocz+VMRFHLpdrXJnAwsKiUj9oKvt4VQlzoxnzoh1zoxnzoh1zoxnzoh1zo1ll5aVK5L4CLtmCvvvTs7/++gu9evUq0d67d2/Mnj1bcr+Sbozo7u6OlJQUPHjwADt37kTXrl0BPF41q/gyrLJ4XibiEBERERE979zd3ZGUlFSifffu3SXunaILSSMk4eHhGDp0KGrUqAEPDw907twZALBv3z40b968zP0YYiIOEREREVF5CfF403efxuztt9/GlClTcOLECQQFBQF4PIckPj4eK1askNyvpIJk4sSJaNu2LdLS0hASEgIzs8cDLZ6envjwww/L3M+LL76I77//HrNmzcL8+fPRsGFDLF++HEOHDlXtc/PmTaSlpakeFxQU4O2331abiLN79+4yTcQhIiIiIiJpJkyYABcXFyxduhQJCQkAHk+3+Oabb/Dqq69K7lfysr/+/v7w9/dXawsLC9O5n549e6Jnz55an4+Pj1d7/O677+Ldd9/V+ThERERERPpiDMv+GkLfvn3Rt29fvfYpuSC5du0atm3bhrS0NBQUFKg9t2zZsnIHRkRERERExsPT0xOHDx9G7dq11dqzsrLQunVr/PXXX5L6lVSQJCUloXfv3vD09MS5c+fQrFkz1X1INN0LhIiIiIjouSJk+l8Vy8hHSK5evQqFQlGiPT8/H9evX5fcr6SCZNasWXjnnXcQERGBmjVrYsuWLXBycsLQoUPRrVs3ycEQEREREVUFpjSpfdu2baqff/75Z9jZ2akeKxQKJCUloUGDBpL7l1SQnD17Fl9//fXjDqpVw8OHD1GjRg3Mnz8fr776KiZMmCA5ICIiIiIiMh59+vQBAMhkMowYMULtOQsLCzRo0ABLly6V3L+kgqR69eqqeSOurq64fPkyfH19AQCZmZmSgyEiIiIiqhLEP5u++zRCSqUSANCwYUMcPnwYderU0Wv/kgqSdu3aYf/+/fDx8UGPHj3w9ttv4/Tp0/juu+/Qrl07vQZIRERERESGd+XKlQrpV1JBsmzZMuTm5gIAIiIikJubi2+++QaNGzfmCltERERE9Nwz1WV/k5KSkJSUhNu3b6tGToqtWbNGUp+SChJPT0/Vz9WrV0dsbKykgxMRERERUdUQERGB+fPnIyAgAK6urpDJ9FNASb4PSbHc3NwS1ZGtrW15uyUiIiIiMm5GOuejosTGxiI+Ph5vvPGGXvs1k/KiK1euICwsDNWrV4ednR3s7e1hb2+PWrVqwd7eXq8BEhERERGR4RUUFCAoKEjv/UoaIRk2bBiEEFizZg2cnZ31NlxDRERERFQVmOIckrFjx2Ljxo344IMP9NqvpILk5MmTOHr0KLy9vfUaDBERERFRlWBCy/4We/ToEeLi4rB79274+fnBwsJC7Xmpi1tJKkhefPFFpKensyAhIiIiIjIRp06dQsuWLQEAZ86cUXuuPFdMSSpIPv/8c4wfPx7Xr19Hs2bNSlRHfn5+kgMiIiIiIjJ+sn82ffdpvH755ZcK6VdSQXLnzh1cvnwZo0aNUrXJZDIIISCTyaBQKPQWIBERERERPb8kFSSjR49Gq1at8PXXX3NSOxERERGZHhOZQ/Laa68hPj4etra2eO2110rd97vvvpN0DEkFSWpqKrZt2wYvLy9JByUiIiIiIuNnZ2enGnyws7OrkGNIKkheeeUVnDx5kgUJEREREZkmExkhWbt2rcaf9UlSQdKrVy9MmzYNp0+fRvPmzUtMau/du7degiMiIiIiIuO0d+9ePHjwAIGBgeW6ObqkgmT8+PEAgPnz55d4jpPaiYiIiOi5J2SPN333aYQWL16M3NxcREZGAgCEEOjevTt27doFAHByckJSUhJ8fX0l9W8m5UVKpVLrxmKEiIiIiJ53QlTMZoy++eYbNGvWTPV48+bN2LdvH3799VdkZmYiICAAERERkvuXVJAQEREREZFpuHLlitp9Brdv347XX38d7du3h4ODA/7v//4PKSkpkvuXXJDs3bsXvXr1gpeXF7y8vNC7d2/8+uuvkgMhIiIiIqoyRAVtRqioqAhyuVz1OCUlBUFBQarHbm5uyMzMlNy/pIJkw4YNCA4Oho2NDaZMmYIpU6bA2toaXbp0wcaNGyUHQ0RERERExqVRo0bYt28fACAtLQ0XLlxAx44dVc9fu3YNtWvXlty/pEntCxYswJIlSzBt2jRV25QpU7Bs2TJERkZiyJAhkgMiIiIiIjJ6JjSpfdKkSZg8eTJ+/fVXHDp0CIGBgWjatKnq+T179qBVq1aS+5c0QvLXX3+hV69eJdp79+6NK1euSA6GiIiIiIiMy7hx4/DJJ5/g7t276NixI7Zs2aL2/I0bNzB69GjJ/UsaIXF3d0dSUlKJGyPu3r0b7u7ukoMhIiIiIqoKZOLxpu8+jdXo0aO1Fh0rV64sV9+SCpK3334bU6ZMwYkTJ1QTWg4cOID4+HisWLGiXAEREREREZHpkFSQTJgwAS4uLli6dCkSEhIAAD4+Pvjmm2/w6quv6jVAIiIiIiKjUxGrYhnxCElFklSQAEDfvn3Rt29ffcZCRERERFQ1mNCk9oomaVK7p6cn/v777xLtWVlZ8PT0LHdQRERERERkGiQVJFevXoVCoSjRnp+fj+vXr5c7KCIiIiIio2ZCN0bUJicnB1u3bsXZs2fL1Y9Ol2xt27ZN9fPPP/8MOzs71WOFQoGkpCQ0aNCgXAEREREREZHxGTBgADp27IjJkyfj4cOHCAgIwNWrVyGEwKZNm9CvXz9J/epUkPTp0wcAIJPJMGLECLXnLCws0KBBAyxdulRSIEREREREVYYJTmrft28f3n//fQDA999/DyEEsrKysG7dOnz44YeSCxKdLtlSKpVQKpWoX78+bt++rXqsVCqRn5+P8+fPo2fPnpICISIiIiIi45WdnQ0HBwcAwM6dO9GvXz/Y2NggLCwMFy9elNyvpFW2eDd2IiIiIjJpJjhC4u7ujpSUFDg4OGDnzp3YtGkTAODevXuwsrKS3K/kZX+TkpKQlJSkGil50po1ayQHRERERERExic8PBxDhw5FjRo14OHhgc6dOwN4fClX8+bNJfcrqSCJiIjA/PnzERAQAFdXV8hkprlmMhERERGZKBO8D8nEiRPRpk0bpKenIyQkBGZmj2d/eHp64sMPP5Tcr6SCJDY2FvHx8XjjjTckH5iIiIiIiKqWgIAABAQEQAgBIQRkMhnCwsLK1aek+5AUFBQgKCioXAcmIiIiIqqqZKJiNmP35Zdfonnz5rC2toa1tTX8/Pywfv36cvUpqSAZO3YsNm7cWK4DExEREZHxE0LgTt4DAIDiqXnDJs0Eb4y4bNkyTJgwAT169EBCQgISEhLQrVs3jB8/HtHR0ZL7lXTJ1qNHjxAXF4fdu3fDz88PFhYWJYIlIiIioqpLCIHvzv2J2CO/41rWPSyq3wjBG9ZisF8rvNk6ABbm5oYOkSrZp59+is8++wzDhw9XtfXu3Ru+vr6YN28epk2bJqlfSSMkp06dQsuWLWFmZoYzZ87g+PHjqu3EiRM69XX9+nUMGzYMtWvXhrW1NZo3b44jR46U+prk5GS0bt0acrkcXl5eiI+Pl/I2iIiIiEiLpSkHMCNxJ/66d1fV9ndeHpal7MdbP/6AIo6WGIXK/C598+ZNjdM2goKCcPPmTSnhA5A4QvLLL79IPuCT7t27h/bt2+Pll1/Gjh074OjoiIsXL8Le3l7ra65cuYKwsDCMHz8eX331FZKSkjB27Fi4uroiNDRUL3ERERERmbI/79zGyiO/ASh5FZEAkJx6Bd+f+xP9mzar9NjoX5X9XdrLywsJCQmYPXu2Wvs333yDxo0bS34fku9Dog+LFy+Gu7s71q5dq2pr2LBhqa+JjY1Fw4YNsXTpUgCAj48P9u/fj+joaBYkRERERHqw8cwpmMtkUAjNkxrMIMP6UydMuiCRQf+T0HVd9Leyv0tHRERg4MCB2LdvH9q3bw8AOHDgAJKSkpCQkKBj9P8qc0Hy2muvIT4+Hra2tnjttddK3fe7774rU5/btm1DaGgo+vfvj71796Ju3bqYOHEixo0bp/U1KSkpCA4OVmsLDQ1FeHi4xv3z8/ORn5+vepyTkwMAKCwsRGFhYZniLI/iY1TGsaoa5kYz5kU75kYz5kU75kYz5kU75uaxK39nohqAav/ca07+1H8B4Nq9exWWJ1PPf/H31WJyuRxyubzEfpXxXfpJ/fr1w2+//Ybo6Ghs3boVwOOC5vfff0erVq2e/ca0KHNBYmdnp7oBop2dneQDPumvv/7CZ599hunTp2P27Nk4fPgwpkyZAktLS4wYMULjazIyMuDs7KzW5uzsjJycHDx8+BDW1tZqz0VFRSEiIqJEP7t27YKNjY1e3kdZJCYmVtqxqhrmRjPmRTvmRjPmRTvmRjPmRTtTz80AC2sMqN+oRHuEu6fa4+3bt1fI8fPy8iqkX72qwBsjuru7qzXPnTsX8+bNK7F7ZXyXfpq/vz82bNigw5t6tjIXJE8OBT35c3kolUoEBARg4cKFAIBWrVrhzJkziI2N1ZpEXc2aNQvTp09XPc7JyYG7uzu6du0KW1tbvRyjNIWFhUhMTERISEiJ1chMHXOjGfOiHXOjGfOiHXOjGfOiHXPz2A/nzuKD5N2qx3KZDBHunpib/hfyhYC5TIZBzVvgvaAOFXL8p0cITE16erra91RNoyNA5XyX1uV3IfW7tV7mkOzduxcPHjxAYGBgqZNonubq6oqmTZuqtfn4+GDLli1aX+Pi4oJbt26ptd26dQu2trYaKzptQ1wWFhaV+kFT2cerSpgbzZgX7ZgbzZgX7ZgbzZgX7Uw9Nz19fBBz9Hdcv5+jNo8kXwgUAjA3M8fwlq0rLEdVIvcVcd+Qf/qztbUt05f7yvguXatWLdVVUs+iUCjKtN/TdCpIFi9ejNzcXERGRgJ4vD519+7dsWvXLgCAk5MTkpKS4OvrW6b+2rdvj/Pnz6u1XbhwAR4eHlpfExgYWGJ4MDExEYGBgbq8FSIiIiLSwqqaBb56bQDG/O97XPg7E9Vk/94pwt7KCqt69kF9u1qGC9AYVGBBUlaV8V36ydV1r169ipkzZ2LkyJGq/VNSUrBu3TpERUXpFvwTdCpIvvnmG7z33nuqx5s3b8a+ffvw66+/wsfHB8OHD0dERESZZ9lPmzYNQUFBWLhwIQYMGIDff/8dcXFxiIuLU+0za9YsXL9+HV9++SUAYPz48YiJicG7776L0aNHY8+ePUhISMBPP/2ky1shIiIiolLUtbXFjiHDcSA9DQeuXgHuZWNJcChCX2gCS94U0ShUxnfpTp06qX6eP38+li1bhsGDB6vaevfujebNmyMuLk7yZWI63RjxypUr8PPzUz3evn07Xn/9dbRv3x4ODg74v//7P6SkpJS5vxdffBHff/89vv76azRr1gyRkZFYvnw5hg4dqtrn5s2bSEtLUz1u2LAhfvrpJyQmJqJFixZYunQpPv/8cy75S0RERKRnMpkML9X3wPTAx0u8dvN6gcXIP2SiYjZdVPZ36ZSUFAQEBJRoDwgIwO+//65b8E/QaYSkqKhIbT5GSkqK2hJhbm5uyMzM1CmAnj17omfPnlqf13TnyM6dO+P48eM6HYeIiIiI6HlTmd+l3d3dsXr1aixZskSt/fPPPy+xMpgudCpIGjVqhH379sHT0xNpaWm4cOECOnbsqHr+2rVrqF27tuRgiIiIiIiqBCOYQ1LZoqOj0a9fP+zYsQNt27YFAPz++++4ePFiqRPpn0WngmTSpEmYPHkyfv31Vxw6dAiBgYFqM/v37NlTrpuiEBERERGRcerRowcuXryIzz77DGfPngUA9OrVC+PHj6+8EZJx48bB3Nwc//vf/9CxY0fMnTtX7fkbN25g9OjRkoMhIiIiIqoSTHCEBADq1auHBQsW6LVPne9DMnr0aK1Fx8qVK8sdEBERERERmQ693BiRiIiIiMiUSFkVqyx9miIWJEREREREuhKyx5u++zRBOt2HhIiIiIiISJ9YkBARERER6UpU0GbkioqKsHv3bqxatQr3798H8Hhhq9zcXMl96uWSrZycHOzZswfe3t7w8fHRR5dERERERGREUlNT0a1bN6SlpSE/Px8hISGoWbMmFi9ejPz8fMTGxkrqV9IIyYABAxATEwMAePjwIQICAjBgwAD4+fmV66YoRERERERVQfGkdn1vxmzq1KkICAjAvXv3YG1trWrv27cvkpKSJPcrqSDZt28fOnToAAD4/vvvIYRAVlYWPvnkE3z44YeSgyEiIiIiIuP066+/4v/+7/9gaWmp1t6gQQNcv35dcr+SCpLs7Gw4ODgAAHbu3Il+/frBxsYGYWFhuHjxouRgiIiIiIiqBBOcQ6JUKqFQKEq0X7t2DTVr1pTcr6SCxN3dHSkpKXjw4AF27tyJrl27AgDu3bsHKysrycEQEREREZFx6tq1K5YvX656LJPJkJubi7lz56JHjx6S+5U0qT08PBxDhw5FjRo14OHhgc6dOwN4fClX8+bNJQdDRERERFQlVMScDyMfIVm6dClCQ0PRtGlTPHr0CEOGDMHFixdRp04dfP3115L7lVSQTJw4EW3atEF6ejpCQkJgZvZ4oMXT05NzSIiIiIjo+VcRl1gZeUFSr149nDx5Eps2bcKpU6eQm5uLMWPGYOjQoWqT3HUlednfgIAABAQEQAgBIQRkMhnCwsIkB0JERERERMatWrVqGDZsmH77lPrCL7/8Eh999JFqEvsLL7yAGTNm4I033tBbcERERERERslERki2bdtW5n179+4t6RiSCpJly5bhgw8+wOTJk9G+fXsAwP79+zF+/HhkZmZi2rRpkoIhIiIiIiLj0adPH7XHMpkMQogSbQA0rsBVFpJW2fr000/x2WefYfHixejduzd69+6NJUuWYOXKlfjkk08kBUJEREREVFWYyo0RlUqlatu1axdatmyJHTt2ICsrC1lZWdixYwdat26NnTt3Sj6GpBGSmzdvIigoqER7UFAQbt68KTkYIiIiIiIyTuHh4YiNjcVLL72kagsNDYWNjQ3efPNNnD17VlK/kkZIvLy8kJCQUKL9m2++QePGjSUFQkRERERExuvy5cuoVatWiXY7OztcvXpVcr+SRkgiIiIwcOBA7Nu3TzWH5MCBA0hKStJYqBARERERUdX24osvYvr06Vi/fj2cnZ0BALdu3cKMGTPQpk0byf1KKkj69euH3377DdHR0di6dSsAwMfHB7///jtatWolORgiIiIioirBRFbZetKaNWvQt29f1K9fH+7u7gCA9PR0NG7cWFUTSCF52V9/f39s2LBB8oGJiIiIiKqqipiEboyT2p/k5eWFU6dOITExEefOnQPweFAiODhYtdKWFGUuSHJycsrcqa2traRgiIiIiIjIeMlkMnTt2hVdu3bVW59lLkhq1apV5spH6hrERERERERVhpGPaFSEBw8eYO/evUhLS0NBQYHac1OmTJHUZ5kLkl9++UX189WrVzFz5kyMHDkSgYGBAICUlBSsW7cOUVFRkgIhIiIiIiLjdfz4cfTo0QN5eXl48OABHBwckJmZCRsbGzg5OVV8QdKpUyfVz/Pnz8eyZcswePBgVVvv3r3RvHlzxMXFYcSIEZKCISIiIiKqEkxwUvu0adPQq1cvxMbGws7ODocOHYKFhQWGDRuGqVOnSu5X0n1IUlJSEBAQUKI9ICAAv//+u+RgiIiIiIjIOJ04cQJvv/02zMzMYG5ujvz8fLi7u2PJkiWYPXu25H4lFSTu7u5YvXp1ifbPP/9ctQQYEREREdHzqniVLX1vxszCwgJmZo/LBycnJ6SlpQF4fGPE9PR0yf1KWvY3Ojoa/fr1w44dO9C2bVsAwO+//46LFy9iy5YtkoMhIiIiIiLj1KpVKxw+fBiNGzdGp06dMGfOHGRmZmL9+vVo1qyZ5H4ljZD06NEDFy9eRO/evXH37l3cvXsXvXr1woULF9CjRw/JwRARERERVQmigjYjtnDhQri6ugIAFixYAHt7e0yYMAF37txBXFyc5H4l3xixXr16WLBggeQDExERERFVVaZ4Y8Qn55A7OTlh586deulX0ggJERERERGRPkgeISEiIiIiMlkmsuxvq1atynxz9GPHjkk6BgsSIiIiIiLSqE+fPqqfHz16hJUrV6Jp06aqm6MfOnQIf/zxByZOnCj5GCxIiIiIiIh0ZSIjJHPnzlX9PHbsWEyZMgWRkZEl9inPsr+S55AUFRVh9+7dWLVqFe7fvw8AuHHjBnJzcyUHQ0RERERExunbb7/F8OHDS7QPGzasXLf+kDRCkpqaim7duiEtLQ35+fkICQlBzZo1sXjxYuTn5yM2NlZyQERERERExs4UV9mytrbGgQMH0LhxY7X2AwcOwMrKSnK/kgqSqVOnIiAgACdPnkTt2rVV7X379sW4ceMkB0NERERERMYpPDwcEyZMwLFjx9CmTRsAwG+//YY1a9bggw8+kNyvpILk119/xcGDB2FpaanW3qBBA1y/fl1yMEREREREVYKJzCF50syZM+Hp6YkVK1Zgw4YNAAAfHx+sXbsWAwYMkNyvpDkkSqUSCoWiRPu1a9dQs2bNMvczb948yGQyta1JkyZa94+Pjy+xf3mGh4iIiIiIJDHBO7UDwIABA3DgwAHcvXsXd+/exYEDB8pVjAASR0i6du2K5cuXq24RL5PJkJubi7lz56JHjx469eXr64vdu3f/G1C10kOytbXF+fPnVY/Lui4yEREREREZH0kFydKlSxEaGoqmTZvi0aNHGDJkCC5evIg6derg66+/1i2AatXg4uJS5v1lMplO+xMRERER6ZupTGp3cHDAhQsXUKdOHdjb25c6GHD37l1Jx5BUkNSrVw8nT57Epk2bcOrUKeTm5mLMmDEYOnQorK2tderr4sWLcHNzg5WVFQIDAxEVFYX69etr3T83NxceHh5QKpVo3bo1Fi5cCF9fX6375+fnIz8/X/U4JycHAFBYWIjCwkKdYpWi+BiVcayqhrnRjHnRjrnRjHnRjrnRjHnRjrnRrLLzwvwbj+joaNWUjOjo6Aq5OkkmhDBYLbZjxw7k5ubC29sbN2/eREREBK5fv44zZ85onIuSkpKCixcvws/PD9nZ2fj444+xb98+/PHHH6hXr57GY8ybNw8REREl2jdu3AgbGxu9vyciIiIiKp+8vDwMGTIE2dnZsLW1NXQ4anJycmBnZ4cm/1kIc7l+5zIr8h/h3KezjfJ9V6QyFyTbtm0rc6e9e/eWFExWVhY8PDywbNkyjBkz5pn7FxYWwsfHB4MHDy5xx8himkZI3N3dkZmZWSm/6MLCQiQmJiIkJAQWFhYVfryqhLnRjHnRjrnRjHnRjrnRjHnRjrnRrLLzkpOTgzp16hjlF3NTLkiCg4MxbNgwvPbaa3qNr8yXbPXp00ftsUwmw9O1TPEQjqYVuMqiVq1aeOGFF3Dp0qUy7W9hYYFWrVqVur9cLodcLtf42sr8oKns41UlzI1mzIt2zI1mzIt2zI1mzIt2zI1mlZWXqpB7Y5hDoulKIG9vb5w7d07j/vHx8Rg1apRam1wux6NHj8p0PF9fX8yaNQsTJ05EWFgYhg0bhh49epT791XmZX+VSqVq27VrF1q2bIkdO3YgKysLWVlZ2LFjB1q3bo2dO3dKDiY3NxeXL1+Gq6trmfZXKBQ4ffp0mfcnIiIiInqe+Pr64ubNm6pt//79pe5va2urtn9qamqZj7VixQpcv34dW7duRfXq1TF8+HA4OzvjzTffxN69eyW/B0mT2sPDwxEbG4uXXnpJ1RYaGgobGxu8+eabOHv2bJn6eeedd9CrVy94eHjgxo0bmDt3LszNzTF48GAAwPDhw1G3bl1ERUUBAObPn4927drBy8sLWVlZ+Oijj5CamoqxY8dKeRtERERERNJU4I0RixdhKqbtih+g8lesNTMzQ9euXdG1a1fExsbif//7HxYsWIAvvvhC8lVSkm6MePnyZdSqVatEu52dHa5evVrmfq5du4bBgwfD29sbAwYMQO3atXHo0CE4OjoCANLS0nDz5k3V/vfu3cO4cePg4+ODHj16ICcnBwcPHkTTpk2lvA0iIiIiImkq8MaI7u7usLOzU23F/zivSfGKtZ6enhg6dCjS0tJKDbt4xVp3d3e8+uqr+OOPPyS9/YyMDMTGxmLx4sU4deoUXnzxRUn9ABJHSF588UVMnz4d69evh7OzMwDg1q1bmDFjBtq0aVPmfjZt2lTq88nJyWqPo6OjER0drXO8RERERERVRXp6utqkcW2jI23btkV8fLzairUdOnTQumKtt7c31qxZo7ZibVBQUKkr1j4pJycHW7ZswcaNG5GcnKwqgr755hs0atRI8vuVVJCsWbMGffv2Rf369eHu7g7gceIaN26MrVu3Sg6GiIiIiKgqkP2z6btP4PE8j7KsYtW9e3fVz35+fmjbti08PDyQkJCgccXawMBABAYGqh4HBQXBx8cHq1at0rpi7ZOcnZ1hb2+PgQMHIioqCgEBAc9+U2UgqSDx8vLCqVOnkJiYqJrF7+Pjg+Dg4Aq5WQoREREREZWuIlasfdK2bdvQpUsXmJlJmvWhlaSCBHg8IaZ4QgsRERERkUmpwEntUhWvWPvGG2+Uaf/iFWt79OhRpv1DQkLKE55WkguSBw8eYO/evUhLS0NBQYHac1OmTCl3YEREREREpJ0hVqzdvHkzEhISNNYAx44dk/Q+JBUkx48fR48ePZCXl4cHDx7AwcEBmZmZsLGxgZOTEwsSIiIiInquGcONEYtXrP3777/h6OiIl156qcSKtU9eXlW8Ym1GRgbs7e3h7++v04q1n3zyCd5//32MHDkSP/zwA0aNGoXLly/j8OHDmDRpkm7BP0FSQTJt2jT06tULsbGxsLOzw6FDh2BhYYFhw4Zh6tSpkoMhIiIiIqKyqewVa1euXIm4uDgMHjwY8fHxePfdd+Hp6Yk5c+bg7t27kvuVNCPlxIkTePvtt2FmZgZzc3Pk5+fD3d0dS5YswezZsyUHQ0RERERUJVTgfUiMVVpaGoKCggAA1tbWuH//PgDgjTfewNdffy25X0kFiYWFhWr4x8nJSXUDFjs7O6Snp0sOhoiIiIioyjChYgQAXFxcVCMh9evXx6FDhwAAV65cgRDS34CkS7ZatWqFw4cPo3HjxujUqRPmzJmDzMxMrF+/Hs2aNZMcDBERERERGadXXnkF27ZtQ6tWrTBq1ChMmzYNmzdvxpEjR/Daa69J7ldSQbJw4ULVEM2CBQswfPhwTJgwAY0bN8aaNWskB0NEREREVBUYw6T2yhYXFwelUgkAmDRpEmrXro2DBw+id+/eeOuttyT3K6kgefKujE5OTti5c6fkAIiIiIiIyLgVFRVh4cKFGD16NOrVqwcAGDRoEAYNGlTuvvV7m0UiIiIiIlNgYpPaq1WrhiVLlqCoqEj/fZd1x1atWkEmk5VpX6k3RSEiIiIiIuPUpUsX7N27Fw0aNNBrv2UuSPr06aP6+dGjR1i5ciWaNm2KwMBAAMChQ4fwxx9/YOLEiXoNkIiIiIjI2JjiHJLu3btj5syZOH36NPz9/VG9enW153v37i2p3zIXJHPnzlX9PHbsWEyZMgWRkZEl9uGyv0REREREz5/igYdly5aVeE4mk0GhUEjqV9Ickm+//RbDhw8v0T5s2DBs2bJFUiBERERERFWGic0hAQClUql1k1qMABILEmtraxw4cKBE+4EDB2BlZSU5GCIiIiIiMi2Slv0NDw/HhAkTcOzYMbRp0wYA8Ntvv2HNmjX44IMP9BogEREREZGxMaU5JA8fPkRSUhJ69uwJAJg1axby8/NVz5ubmyMyMlLywISkgmTmzJnw9PTEihUrsGHDBgCAj48P1q5diwEDBkgKhIiIiIioyqiIS6yMtCBZt24dfvrpJ1VBEhMTA19fX1hbWwMAzp07Bzc3N0ybNk1S/5IKEgAYMGAAiw8iIiIioufcV199hXfffVetbePGjfD09AQAbNiwAf/9738lFyS8MSIRERERka5MaFL7pUuX0Lx5c9VjKysrmJn9W0a0adMGf/75p+T+yzxC4uDggAsXLqBOnTqwt7cv9SaJd+/elRwQEREREREZj6ysLLU5I3fu3FF7XqlUqj2vqzIXJNHR0ahZs6bq57LetZ2IiIiI6HljSpPa69WrhzNnzsDb21vj86dOnUK9evUk91/mgmTEiBGqn0eOHCn5gEREREREVHX06NEDc+bMQVhYWImVtB4+fIiIiAiEhYVJ7l/SpPbg4GAMGzYMr732GmxtbSUfnIiIiIioSjKhVbZmz56NhIQEeHt7Y/LkyXjhhRcAAOfPn0dMTAyKioowe/Zsyf1LmtTu6+uLWbNmwcXFBf3798cPP/yAwsJCyUEQEREREZFxcnZ2xsGDB+Hj44OZM2eib9++6Nu3L2bNmoWmTZti//79cHZ2lty/pIJkxYoVuH79OrZu3Yrq1atj+PDhcHZ2xptvvom9e/dKDoaIiIiIqCqQCVEhm7Fq2LAhdu7ciTt37uDQoUM4dOgQ7ty5g507d6qW/5VK8rK/ZmZm6Nq1K+Lj43Hr1i2sWrUKv//+O1555ZVyBUREREREZPRMaNnfJzk4OKBNmzZo06YNHBwc9NKn5BsjFsvIyMCmTZuwYcMGnDp1Cm3atNFHXEREREREZAIkjZDk5ORg7dq1CAkJgbu7Oz777DP07t0bFy9exKFDh/QdIxERERGRUSle9lffmymSNELi7OwMe3t7DBw4EFFRUQgICNB3XEREREREZAIkFSTbtm1Dly5d1G4ZT0RERERkMkxo2d+KJqkgCQkJ0XccRERERERkgiRPat+8eTMSEhKQlpaGgoICteeOHTtW7sCIiIiIiIxVRcz5MNU5JJKuufrkk08watQoODs74/jx42jTpg1q166Nv/76C927d9d3jERERERE9JySVJCsXLkScXFx+PTTT2FpaYl3330XiYmJmDJlCrKzs/UdIxERERGRcTHR+5BUBEkFSVpaGoKCggAA1tbWuH//PgDgjTfewNdff62/6IiIiIiIjBCX/dUfSQWJi4sL7t69CwCoX7++6t4jV65cgTDiW94TEREREZFxkVSQvPLKK9i2bRsAYNSoUZg2bRpCQkIwcOBA9O3bV68BEhEREREZHV6ypTeSVtmKi4uDUqkEAEyaNAm1a9fGwYMH0bt3b7z11lt6DZCIiIiIiJ5fOhckRUVFWLhwIUaPHo169eoBAAYNGoRBgwbpPbjnQd7Dx0siP3xUAAsLCwNHQ0RERET6YqpzPvRN50u2qlWrhiVLlqCoqKgi4nluXE69g9mLt6LfW6sAAP3eikPE8p9wPSPLsIERERERERkRSXNIunTpgr1795b74PPmzYNMJlPbmjRpUuprvv32WzRp0gRWVlZo3rw5tm/fXu449O3spZt4c+ZX2H/kMpT/VM4KpcCeA+cwZsZ6XL32t2EDJCIiIqLyEaJiNhMkaQ5J9+7dMXPmTJw+fRr+/v6oXr262vO9e/cuc1++vr7YvXv3vwFV0x7SwYMHMXjwYERFRaFnz57YuHEj+vTpg2PHjqFZs2a6v5EKIITAh5/sQGGRAkqlAMxlqucUSoG8RwX4aFUi/hvJS9yIiIiIiCQVJBMnTgQALFu2rMRzMpkMCoWi7AFUqwYXF5cy7btixQp069YNM2bMAABERkYiMTERMTExiI2NLfMxK9KZ8zeQev2u1ueVSoGTf15D2o27qO/mUImREREREZG+VMR9Q0x1ToqkgqR4hS19uHjxItzc3GBlZYXAwEBERUWhfv36GvdNSUnB9OnT1dpCQ0OxdetWrf3n5+cjPz9f9TgnJwcAUFhYiMLCwvK/gadcvZYJS4t/r4SzrCZ74r//tl9NvwNXx5p6P35VUpz/ivg9VGXMi3bMjWbMi3bMjWbMi3bMjWaVnZcqkf+KWKbXRAsSmTDgnQx37NiB3NxceHt74+bNm4iIiMD169dx5swZ1KxZ8su6paUl1q1bh8GDB6vaVq5ciYiICNy6dUvjMebNm4eIiIgS7Rs3boSNjY3+3gwRERER6UVeXh6GDBmC7Oxs2NraGjocNTk5ObCzs0NAvw9RzcJKr30XFT7CkS3/Z5TvuyLpNELy8OFDJCUloWfPngCAWbNmqY0+mJubIzIyElZWZfvldO/eXfWzn58f2rZtCw8PDyQkJGDMmDG6hKbVrFmz1EZVcnJy4O7ujq5du1bIL/pBXgEGTVqNgqLHo0iW1WSY0N8Tn337FwqKHtd+tjXk2PjJGFhYmOv9+FVJYWEhEhMTERISwiWRn8C8aMfcaMa8aMfcaMa8aMfcaFbZeSm+osWYyZSPN333aYp0KkjWrVuHn376SVWQxMTEwNfXF9bW1gCAc+fOwc3NDdOmTZMUTK1atfDCCy/g0qVLGp93cXEpMRJy69atUuegyOVyyOXyEu0WFhYV8gdVy84Cr/UIwJdbDv3T8vgyrYIigYLCx2fZkD7tYGOj34q6Kquo30VVx7xox9xoxrxox9xoxrxox9xoVll5Ye5Ni07L/n711Vd488031do2btyIX375Bb/88gs++ugjJCQkSA4mNzcXly9fhqurq8bnAwMDkZSUpNaWmJiIwMBAycesCGMGBuH1Hq0AAGb/ZNjMDDAzk2HUgED0D2ttwOiIiIiIqNxEBW0mSKcRkkuXLqF58+aqx1ZWVjAz+7emadOmDSZNmlTm/t555x306tULHh4euHHjBubOnQtzc3PVHJHhw4ejbt26iIqKAgBMnToVnTp1wtKlSxEWFoZNmzbhyJEjiIuL0+VtVDhzczOEj+mCgb0CsGvfHwDuYNSAIIR2bIY6DjUMHR4RERERkdHQaYQkKytLbc7InTt30KBBA9VjpVKp9vyzXLt2DYMHD4a3tzcGDBiA2rVr49ChQ3B0dAQApKWl4ebNm6r9g4KCsHHjRsTFxaFFixbYvHkztm7dajT3IHmaq5Mdhrz6IgBgQJg/ixEiIiKi50Txsr/63kyRTgVJvXr1cObMGa3Pnzp1CvXq1Stzf5s2bcKNGzeQn5+Pa9euYdOmTWjUqJHq+eTkZMTHx6u9pn///jh//jzy8/Nx5swZ9OjRQ5e3QERERET0XJg3bx5kMpna1qRJk1Jf8+2336JJkyawsrJC8+bNsX379kqKVjudCpIePXpgzpw5ePToUYnnHj58iIiICISFhektOCIiIiIioyRExWw68vX1xc2bN1Xb/v37te578OBBDB48GGPGjMHx48fRp08f9OnTp9QBh8qg0xyS2bNnIyEhAd7e3pg8eTJeeOEFAMD58+cRExODoqIizJ49u0ICJSIiIiIyFsZyp/Zq1aqVuuLsk1asWIFu3bphxowZAIDIyEgkJiYiJiYGsbGxuh9cT3QqSJydnXHw4EFMmDABM2fORPE9FWUyGUJCQrBy5Uo4OztXSKBERERERKbg6fuwaLuNBQBcvHgRbm5usLKyQmBgIKKiolC/fn2N+6akpKjdnw8AQkNDsXXrVr3ELZVOBQkANGzYEDt37sTdu3dV9wvx8vKCg4OD3oMjIiIiIjJKFbFM7z/9ubu7qzXPnTsX8+bNK7F727ZtER8fD29vb9y8eRMRERHo0KEDzpw5g5o1a5bYPyMjo8TggbOzMzIyMvT2FqTQuSAp5uDggDZt2ugzFiIiIiIik5eeng5bW1vVY22jI927d1f97Ofnh7Zt28LDwwMJCQkYM2ZMhcepL5ILEiIiIiIiU1WRc0hsbW3VCpKyqlWrFl544QXVVUxPc3Fxwa1bt9Tabt26VeY5KBVFp1W2iIiIiIjIOOXm5uLy5ctwdXXV+HxgYCCSkpLU2hITExEYGFgZ4WnFgoSIiIiISFdGsOzvO++8g7179+Lq1as4ePAg+vbtC3NzcwwePBgAMHz4cMyaNUu1/9SpU7Fz504sXboU586dw7x583DkyBFMnjxZr6nRFS/ZIiIiIiKqgq5du4bBgwfj77//hqOjI1566SUcOnQIjo6OAIC0tDSYmf07/hAUFISNGzfi//7v/zB79mw0btwYW7duRbNmzQz1FgCwICEiIiIi0pkx3Idk06ZNpT6fnJxcoq1///7o37+/bgeqYCxIiIiIiIh0VYHL/poaziEhIiIiIiKD4QgJEREREZGOjOGSrecFR0iIiIiIiMhgOEJCRERERKQrpXi86btPE8QREiIiIiIiMhiOkBARERER6YqrbOkNR0iIiIiIiMhgOEJCRERERKQjGSpglS39dldlsCAhIiIiItKVEI83ffdpgnjJFhERERERGQxHSIiIiIiIdMQbI+oPR0iIiIiIiMhgOEJCRERERKQrLvurNxwhISIiIiIig+EICRERERGRjmRCQKbnVbH03V9VwRESIiIiIiIyGI6QEBERERHpSvnPpu8+TRALEiIiIiIiHfGSLf3hJVtERERERGQwHCEhIiIiItIVl/3VG46QEBERERGRwXCEhIiIiIhIV0I83vTdpwniCAkRERERERkMR0iIiIiIiHQkE483ffdpijhCQkREREREBsMREiIiIiIiXXEOid5whISIiIiIiAyGIyRERERERDqSKR9v+u7TFLEgISIiIiLSFS/Z0hteskVERERERAZjNAXJokWLIJPJEB4ernWf+Ph4yGQytc3KyqrygiQiIiIiAgBRQZsJMopLtg4fPoxVq1bBz8/vmfva2tri/PnzqscymawiQyMiIiIiogpk8BGS3NxcDB06FKtXr4a9vf0z95fJZHBxcVFtzs7OlRAlEREREdG/ZEJUyGaKDD5CMmnSJISFhSE4OBgffvjhM/fPzc2Fh4cHlEolWrdujYULF8LX11fr/vn5+cjPz1c9zsnJAQAUFhaisLCw/G/gGYqPURnHqmqYG82YF+2YG82YF+2YG82YF+2YG80qOy/Mv2kxaEGyadMmHDt2DIcPHy7T/t7e3lizZg38/PyQnZ2Njz/+GEFBQfjjjz9Qr149ja+JiopCREREifZdu3bBxsamXPHrIjExsdKOVdUwN5oxL9oxN5oxL9oxN5oxL9oxN5pVVl7y8vIq5TjlwlW29MZgBUl6ejqmTp2KxMTEMk9MDwwMRGBgoOpxUFAQfHx8sGrVKkRGRmp8zaxZszB9+nTV45ycHLi7u6Nr166wtbUt35sog8LCQiQmJiIkJAQWFhYVfryqhLnRjHnRjrnRjHnRjrnRjHnRjrnRrLLzUnxFC5kGgxUkR48exe3bt9G6dWtVm0KhwL59+xATE4P8/HyYm5uX2oeFhQVatWqFS5cuad1HLpdDLpdrfG1lftBU9vGqEuZGM+ZFO+ZGM+ZFO+ZGM+ZFO+ZGs8rKS5XIvQCg7xsZmuYAieEKki5duuD06dNqbaNGjUKTJk3w3nvvPbMYAR4XMKdPn0aPHj0qKkwiIiIiohIqYhI6J7VXspo1a6JZs2ZqbdWrV0ft2rVV7cOHD0fdunURFRUFAJg/fz7atWsHLy8vZGVl4aOPPkJqairGjh1b6fETEREREVH5GXyVrdKkpaXBzOzflYnv3buHcePGISMjA/b29vD398fBgwfRtGlTA0ZJRERERCZHoAImteu3u6rCqAqS5OTkUh9HR0cjOjq68gIiIiIiIqIKZVQFCRERERFRlcBlf/XG4HdqJyIiIiIi08UREiIiIiIiXSkByCqgTxPEERIiIiIiIjIYjpAQEREREemI9yHRH46QEBERERHpqnhSu763cli0aBFkMhnCw8O17hMfHw+ZTKa2WVlZleu45cUREiIiIiKiKu7w4cNYtWoV/Pz8nrmvra0tzp8/r3osk+l7MoxuOEJCRERERKQrIxohyc3NxdChQ7F69WrY29s/c3+ZTAYXFxfV5uzsLOm4+sKChIiIiIjIiOTk5Kht+fn5pe4/adIkhIWFITg4uEz95+bmwsPDA+7u7nj11Vfxxx9/6CNsyViQEBERERHpqgJHSNzd3WFnZ6faoqKitIaxadMmHDt2rNR9nuTt7Y01a9bghx9+wIYNG6BUKhEUFIRr167pJS1ScA4JEREREZERSU9Ph62treqxXC7Xut/UqVORmJhY5onpgYGBCAwMVD0OCgqCj48PVq1ahcjIyPIFLhELEiIiIiIiXVXgjRFtbW3VChJtjh49itu3b6N169aqNoVCgX379iEmJgb5+fkwNzcvtQ8LCwu0atUKly5dKlfo5cGChIiIiIioCurSpQtOnz6t1jZq1Cg0adIE77333jOLEeBxAXP69Gn06NGjosJ8JhYkREREREQ6MoYbI9asWRPNmjVTa6tevTpq166tah8+fDjq1q2rmmMyf/58tGvXDl5eXsjKysJHH32E1NRUjB07Vj9vQgIWJEREREREutLDjQw19qlnaWlpMDP7dx2re/fuYdy4ccjIyIC9vT38/f1x8OBBNG3aVO/HLisWJEREREREz4nk5ORSH0dHRyM6OrryAioDFiRERERERLpSCkCm5xENpf5HSKoC3oeEiIiIiIgMhiMkRERERES6qiJzSKoCjpAQEREREZHBcISEiIiIiEhnFTBCAo6QEBERERERVSqOkBARERER6YpzSPSGBQkRERERka6UAnq/xIrL/hIREREREVUujpAQEREREelKKB9v+u7TBHGEhIiIiIiIDIYjJEREREREuuKkdr3hCAkRERERERkMR0iIiIiIiHTFVbb0hiMkRERERERkMBwhISIiIiLSFeeQ6A0LEiIiIiIiXQlUQEGi3+6qCl6yRUREREREBsMREiIiIiIiXfGSLb3hCAkRERERERkMR0iIiIiIiHSlVAJQVkCfpocjJEREREREZDAcISEiIiIi0hXnkOgNR0iIiIiIiMhgOEJCZGRysx4AAPIfFsDCwsLA0RAREZFGHCHRG46QEBmJPw9dwKxuH2Jow4kAgKENJyD6zVhk3rhr4MiIiIioBKWomM0EGU1BsmjRIshkMoSHh5e637fffosmTZrAysoKzZs3x/bt2ysnQKIKdPjnE5jecQ6OJZ1W3aW18FERdsb/gsltZiLz+t+GDZCIiIioghhFQXL48GGsWrUKfn5+pe538OBBDB48GGPGjMHx48fRp08f9OnTB2fOnKmkSIn0r6iwCIuHfwqlQgmlQn25P2WRElm3s7FqxpcGio6IiIg0EUJZIZspMnhBkpubi6FDh2L16tWwt7cvdd8VK1agW7dumDFjBnx8fBAZGYnWrVsjJiamkqIl0r9DPx5F9p0cCC3XjSqKlPh18yFkZ+ZUcmREREREFc/gk9onTZqEsLAwBAcH48MPPyx135SUFEyfPl2tLTQ0FFu3btX6mvz8fOTn56se5+Q8/lJXWFiIwsJC6YGXUfExKuNYVQ1z81ja+euwqmkJRdHjgsTCupraf4tdu3QDNnbWlR6fMeE5oxnzoh1zoxnzoh1zo1ll56VK5F9UwJwPE53UbtCCZNOmTTh27BgOHz5cpv0zMjLg7Oys1ubs7IyMjAytr4mKikJERESJ9l27dsHGxka3gMshMTGx0o5V1Zh6bqr7mGPslwNKtI/+op/a40u3zuPS9vOVFZZRM/VzRhvmRTvmRjPmRTvmRrPKykteXl6lHIeMg8EKkvT0dEydOhWJiYmwsrKqsOPMmjVLbVQlJycH7u7u6Nq1K2xtbSvsuMUKCwuRmJiIkJAQLuH6FObmsTvXMjGu+duqfxSxsK6G0V/0w5oxW1D4sAiQAW6ezlh5ZDFkMplhgzUwnjOaMS/aMTeaMS/aMTeaVXZeiq9oMWpCQLUSjV77ND0GK0iOHj2K27dvo3Xr1qo2hUKBffv2ISYmBvn5+TA3N1d7jYuLC27duqXWduvWLbi4uGg9jlwuh1wuL9FuYWFRqR80lX28qsTUc+PW0BUvD3wJu9YlQzwx9Fv4sAiFDx8PWQ+d/TosLS0NFaLRMfVzRhvmRTvmRjPmRTvmRrPKygtzb1oMNqm9S5cuOH36NE6cOKHaAgICMHToUJw4caJEMQIAgYGBSEpKUmtLTExEYGBgZYVNVCGmrByHVwa/BAAwM388CiIzA6pZmGPSitF4eVB7Q4ZHRERET1MqK2YzQQYbIalZsyaaNWum1la9enXUrl1b1T58+HDUrVsXUVFRAICpU6eiU6dOWLp0KcLCwrBp0yYcOXIEcXFxlR4/kT5Zyi0wc/0UDHm/H5ITDgAAxnw4BF2GdoRdnYq/tJCIiIh0xEu29Mbgy/6WJi0tDTdv3lQ9DgoKwsaNGxEXF4cWLVpg8+bN2Lp1a4nChqiqqt+kLgbP6gsA6DUxlMUIERERPfcMvuzvk5KTk0t9DAD9+/dH//79KycgIiIiIiINhFIJIdPvJVa8MSIREREREVElM6oREiIiIiKiKoFzSPSGIyRERERERGQwHCEhIiIiItKVUgAyjpDoA0dIiIiIiIjIYDhCQkRERESkKyEA6HlVLI6QEBERERERVS6OkBARERER6UgoBYSe55AIEx0hYUFCRERERKQroYT+L9nijRGJiIiIiIgqFQsSIiIiIiIdCaWokK08Fi1aBJlMhvDw8FL3+/bbb9GkSRNYWVmhefPm2L59e7mOW14sSIiIiIiIqrjDhw9j1apV8PPzK3W/gwcPYvDgwRgzZgyOHz+OPn36oE+fPjhz5kwlRVoSCxIiIiIiIl0JZcVsEuTm5mLo0KFYvXo17O3tS913xYoV6NatG2bMmAEfHx9ERkaidevWiImJkXRsfTC5Se3Fqxfk5ORUyvEKCwuRl5eHnJwcWFhYVMoxqwrmRjPmRTvmRjPmRTvmRjPmRTvmRrPKzkvx9zRjXnWqCIWAnsMrQiGAkt9T5XI55HK51tdNmjQJYWFhCA4OxocffljqMVJSUjB9+nS1ttDQUGzdulVa0HpgcgXJ/fv3AQDu7u4GjoSIiIiISnP//n3Y2dkZOgw1lpaWcHFxwf6Mipl3UaNGjRLfU+fOnYt58+Zp3H/Tpk04duwYDh8+XKb+MzIy4OzsrNbm7OyMjIwMSfHqg8kVJG5ubkhPT0fNmjUhk8kq/Hg5OTlwd3dHeno6bG1tK/x4VQlzoxnzoh1zoxnzoh1zoxnzoh1zo1ll50UIgfv378PNza3Cj6UrKysrXLlyBQUFBRXSvxCixHdUbaMj6enpmDp1KhITE2FlZVUh8VQGkytIzMzMUK9evUo/rq2tLT/YtGBuNGNetGNuNGNetGNuNGNetGNuNKvMvBjbyMiTrKysjKIAOHr0KG7fvo3WrVur2hQKBfbt24eYmBjk5+fD3Nxc7TUuLi64deuWWtutW7fg4uJSKTFrwkntRERERERVUJcuXXD69GmcOHFCtQUEBGDo0KE4ceJEiWIEAAIDA5GUlKTWlpiYiMDAwMoKuwSTGyEhIiIiInoe1KxZE82aNVNrq169OmrXrq1qHz58OOrWrYuoqCgAwNSpU9GpUycsXboUYWFh2LRpE44cOYK4uLhKj78YR0gqmFwux9y5c0tdGcFUMTeaMS/aMTeaMS/aMTeaMS/aMTeaMS9VV1paGm7evKl6HBQUhI0bNyIuLg4tWrTA5s2bsXXr1hKFTWWSCWNeT42IiIiIiJ5rHCEhIiIiIiKDYUFCREREREQGw4KEiIiIiIgMhgUJEREREREZDAsSHURFReHFF19EzZo14eTkhD59+uD8+fNq+3Tu3BkymUxtGz9+fKn9CiEwZ84cuLq6wtraGsHBwbh48WJFvhW9e1Zurl69WiIvxdu3336rtd+RI0eW2L9bt26V8Zb04rPPPoOfn5/qRlKBgYHYsWOH6vlHjx5h0qRJqF27NmrUqIF+/fqVuFnR056H8wUoPTd3797Ff/7zH3h7e8Pa2hr169fHlClTkJ2dXWqfVf18AZ59zpjqZ0xpeTHVzxdtFi1aBJlMhvDwcFWbKX/WFHs6L6b8OfMkTeeLqX7OkAEJKrPQ0FCxdu1acebMGXHixAnRo0cPUb9+fZGbm6vap1OnTmLcuHHi5s2bqi07O7vUfhctWiTs7OzE1q1bxcmTJ0Xv3r1Fw4YNxcOHDyv6LenNs3JTVFSklpObN2+KiIgIUaNGDXH//n2t/Y4YMUJ069ZN7XV3796trLdVbtu2bRM//fSTuHDhgjh//ryYPXu2sLCwEGfOnBFCCDF+/Hjh7u4ukpKSxJEjR0S7du1EUFBQqX0+D+eLEKXn5vTp0+K1114T27ZtE5cuXRJJSUmicePGol+/fqX2WdXPFyGefc6Y6mdMaXkx1c8XTX7//XfRoEED4efnJ6ZOnapqN+XPGiE058WUP2eKaTtfTPVzhgyHBUk53L59WwAQe/fuVbV16tRJ7Y/6WZRKpXBxcREfffSRqi0rK0vI5XLx9ddf6zPcSqUpN09r2bKlGD16dKn9jBgxQrz66qt6js6w7O3txeeffy6ysrKEhYWF+Pbbb1XPnT17VgAQKSkpGl/7vJ4vxYpzo0lCQoKwtLQUhYWFWl//PJ4vQqjnhZ8x/yrtfDHFz5f79++Lxo0bi8TERLXzxNQ/a7TlRRNT+pwpLS/8nKHKxku2yqF4WNfBwUGt/auvvkKdOnXQrFkzzJo1C3l5eVr7uHLlCjIyMhAcHKxqs7OzQ9u2bZGSklIxgVcCbbkpdvToUZw4cQJjxox5Zl/JyclwcnKCt7c3JkyYgL///luvsVYWhUKBTZs24cGDBwgMDMTRo0dRWFio9rtv0qQJ6tevr/V3/7yeL0/nRpPs7GzY2tqiWrVqpfb1vJwvgPa8mPpnzLPOF1P8fAGASZMmISwsTO13DcDkP2u05UUTU/qceVZeTP1zhipX6X9xpJVSqUR4eDjat2+vdmfLIUOGwMPDA25ubjh16hTee+89nD9/Ht99953GfjIyMgAAzs7Oau3Ozs6q56oabbl50hdffAEfHx8EBQWV2le3bt3w2muvoWHDhrh8+TJmz56N7t27IyUlBebm5hURvt6dPn0agYGBePToEWrUqIHvv/8eTZs2xYkTJ2BpaYlatWqp7V/a7/55O1+05eZpmZmZiIyMxJtvvllqf8/D+QKUnhdT/owp6/liSp8vxTZt2oRjx47h8OHDJZ7LyMgw2c+a0vLyNFP6nHlWXkz5c4YMgwWJRJMmTcKZM2ewf/9+tfYnP8iaN28OV1dXdOnSBZcvX0ajRo0qO0yD0JabYg8fPsTGjRvxwQcfPLOvQYMGqX5u3rw5/Pz80KhRIyQnJ6NLly56i7kieXt748SJE8jOzsbmzZsxYsQI7N2719BhGQVtuXnyS2ZOTg7CwsLQtGlTzJs3r9T+nofzBSg9L6b8GVOW88XUPl8AID09HVOnTkViYiKsrKwMHY7R0CUvpvQ5U5a8mPLnDBkGL9mSYPLkyfjxxx/xyy+/oF69eqXu27ZtWwDApUuXND7v4uICACVWO7l165bquaqkLLnZvHkz8vLyMHz4cJ379/T0RJ06dbTm0xhZWlrCy8sL/v7+iIqKQosWLbBixQq4uLigoKAAWVlZavuX9rt/3s4Xbbkpdv/+fXTr1g01a9bE999/DwsLC536r4rnC/DsvDzJlD5jypIXU/t8AR5fknX79m20bt0a1apVQ7Vq1bB371588sknqFatGpydnU3ys+ZZeVEoFABM73OmrHl5kil9zpBhsCDRgRACkydPxvfff489e/agYcOGz3zNiRMnAACurq4an2/YsCFcXFyQlJSkasvJycFvv/2m9Vp6Y6RLbr744gv07t0bjo6OOh/n2rVr+Pvvv7XmsypQKpXIz8+Hv78/LCws1H7358+fR1pamtbf/fNyvmhTnBvg8fvq2rUrLC0tsW3bNkn/8vs8nC+Ael6eZiqfMZpoyospfr506dIFp0+fxokTJ1RbQEAAhg4dqvrZFD9rnpUXc3Nzk/ycKUtenmbKnzNUSQw9q74qmTBhgrCzsxPJyclqS+Hl5eUJIYS4dOmSmD9/vjhy5Ii4cuWK+OGHH4Snp6fo2LGjWj/e3t7iu+++Uz1etGiRqFWrlvjhhx/EqVOnxKuvvlrllsp7Vm6KXbx4UchkMrFjxw6N/TyZm/v374t33nlHpKSkiCtXrojdu3eL1q1bi8aNG4tHjx5V+HvSh5kzZ4q9e/eKK1euiFOnTomZM2cKmUwmdu3aJYR4vBRn/fr1xZ49e8SRI0dEYGCgCAwMVOvjeTxfhCg9N9nZ2aJt27aiefPm4tKlS2rnVFFRkaqP5+18EaL0vJjyZ8yz/paEML3Pl9I8vUqSKX/WPOnJvJjy58zTnsyLKX/OkOGwINEBAI3b2rVrhRBCpKWliY4dOwoHBwchl8uFl5eXmDFjRom1u598jRCPl8v74IMPhLOzs5DL5aJLly7i/PnzlfjOyu9ZuSk2a9Ys4e7uLhQKhdZ+il+Tl5cnunbtKhwdHYWFhYXw8PAQ48aNExkZGRX8bvRn9OjRwsPDQ1haWgpHR0fRpUsXtS9QDx8+FBMnThT29vbCxsZG9O3bV9y8eVOtj+fxfBGi9Nz88ssvWs+pK1euqPp43s4XIUrPiyl/xjzrb0kI0/t8Kc3TBYkpf9Y86cm8mPLnzNOezIspf86Q4ciEEKLCh2GIiIiIiIg04BwSIiIiIiIyGBYkRERERERkMCxIiIiIiIjIYFiQEBERERGRwbAgISIiIiIig2FBQkREREREBsOChIiIiIiIDIYFCRERERERGQwLEiKq8g4dOoTatWtj7NixOHv2LMLCwirkOJ07d0Z4eHiF9G2MZDIZtm7dWiF9FxQUwMvLCwcPHixXPyNHjkSfPn30E1QFyszMhJOTE65du2boUIiIjA4LEiLSauTIkZDJZJDJZLCwsICzszNCQkKwZs0aKJVKQ4ensm3bNixevBh16tRBjx498NZbbxk6pFJdunQJo0aNQr169SCXy9GwYUMMHjwYR44cMXRoWl29ehUymQwnTpzQS3+xsbFo2LAhgoKCytXPihUrEB8fr5eYiiUnJ0MmkyErK0tvfdapUwfD/7+du4/q8f4fOP4M3U1Fkm6MUCGd7iQcJMJyl+Zmc5aTstYc2WomuTnUxpc1Y1NsxplhdHJzSGYrt3FiZ1byEfVRk7JzJuamUewQvX9/OK6fz1Sy2drs9fir6313vd/X9T6d6/V5X9d78mQSExOfWZtCCPG8kIBECNGg4cOHU1FRQXl5OZmZmQwePJjY2FhGjx7NvXv3/nC7Sqk/Vf9RS5Ys4Y033iApKYmysjLGjBnzTNr9K+Tl5eHr60tJSQlr1qyhqKiI9PR0unfvzsyZM5u6e38LpRSrVq0iMjLyT7fVqlUrWrdu/ec79TeYMmUKqampXL9+vam7IoQQ/ygSkAghGmRqaoq9vT3t27enZ8+ezJs3j4yMDDIzM7Vfpuv69fzXX3/FyMiIw4cPA///q3NmZia+vr6Ymppy9OhRSktLCQkJwc7ODgsLC/z8/Dhw4IBBH+7cucPs2bPp0KEDpqamuLi4sG7dOgDu379PZGQknTt3xtzcnG7dupGcnGxQv7a2loULF2orEt7e3mRlZTU47lu3bjF58mQsLCxwcHBg+fLlj5XZtGkTvXr1wtLSEnt7e0JDQ/nll1/qbVMpRUREBK6uruTk5DBq1CicnZ3x9vYmMTGRjIwMrezp06cJDAzE3NwcGxsb3nzzTaqrq7X8h68qLVu2DAcHB2xsbJg+fTo1NTVamc8++wxXV1fMzMyws7NjwoQJWl6nTp1YsWKFQf+8vb1577336ux7586dAfDx8cHIyIhBgwYBkJuby7Bhw2jbti2tWrUiICCA/Pz8eq8BwIkTJygtLTV4te7hHNq2bRv+/v6Ym5vj5+dHSUkJubm59OrVCwsLC0aMGMGVK1ceuw4PDRo0iJiYGOLj42nTpg329vYGY3rSXC0vL2fw4MEAWFtbY2RkREREBPBgHsbExNCuXTvMzMwYMGAAubm5WjuVlZVMmjQJW1tbzM3NcXV1Zf369Vq+u7s7jo6OpKenN3h9hBDiv0YCEiHEUwsMDMTLy4udO3c+dd05c+aQlJSEXq/H09OT6upqRo4cycGDBzl58iTDhw8nODiYn376SaszefJk0tLSSElJQa/Xs2bNGiwsLIAHwcaLL77I9u3bKSoqIiEhgXnz5rFt2zatfnJyMsuXL2fZsmUUFBQQFBTEmDFj+PHHH+vt56xZszhy5AgZGRns27ePw4cPP/agXVNTw6JFizh16hS7du2ivLxce3iti06no7CwkJkzZ9Ks2eP/fh/+0n/r1i2CgoKwtrYmNzeX7du3c+DAAd566y2D8tnZ2ZSWlpKdnc3GjRvZsGGDFiTm5eURExPDwoULKS4uJisri4EDB9bbtyf54YcfADhw4AAVFRXava+qqiI8PJyjR4/y/fff4+rqysiRI6mqqqq3rZycHLp27YqlpeVjeYmJicyfP5/8/HxatGhBaGgo8fHxJCcnk5OTw7lz50hISGiwrxs3bqRly5YcP36cpUuXsnDhQvbv39+ocXbo0IEdO3YAUFxcTEVFhRbgxsfHs2PHDjZu3Eh+fj4uLi4EBQVpKx4LFiygqKiIzMxM9Ho9q1evpm3btgbt9+7dm5ycnEb1RQgh/jOUEELUIzw8XIWEhNSZN3HiROXm5qaUUqqsrEwB6uTJk1p+ZWWlAlR2drZSSqns7GwFqF27dj3xvO7u7mrlypVKKaWKi4sVoPbv39/ofk+fPl2NHz9eO3Z0dFSLFy82KOPn56eio6PrrF9VVaVMTEzUtm3btLRr164pc3NzFRsbW+95c3NzFaCqqqrqzN+6dasCVH5+foP9X7t2rbK2tlbV1dVa2jfffKOaNWumLl26pJR6cG+cnJzUvXv3tDKvvPKKmjhxolJKqR07digrKyt18+bNOs/h5OSkPvnkE4M0Ly8vlZiYqB0DKj09XSlV9z2uy/3795WlpaX6+uuv6y0TGxurAgMDDdIetv/FF19oaWlpaQpQBw8e1NI++OAD1a1bN+3493M0ICBADRgwwKBtPz8/NXv27HrHUd9crays1MpUV1crY2NjlZqaqqXdvXtXOTo6qqVLlyqllAoODlZTpkypd9xKKTVjxgw1aNCgBssIIcR/jayQCCH+EKUURkZGT12vV69eBsfV1dXExcXh5uZG69atsbCwQK/XayskOp2O5s2bExAQUG+bn376Kb6+vtja2mJhYcHatWu1+jdv3uTixYv079/foE7//v3R6/V1tldaWsrdu3fp06ePltamTRu6detmUO7EiRMEBwfTsWNHLC0ttT4+urrzKKVUvWN4lF6vx8vLi5YtWxr0t7a2luLiYi3N3d2d5s2ba8cODg7aK2PDhg3DycmJLl26EBYWRmpqKrdv327U+Z/G5cuXiYqKwtXVlVatWmFlZUV1dXW91wDgt99+w8zMrM48T09P7W87OzsAPDw8DNIaei3u922A4XX5o0pLS6mpqTGYR8bGxvTu3VubR9OmTWPLli14e3sTHx9f5w5i5ubmf8l9EEKIfzMJSIQQf4her9e+K3j4+tGjD9yPfsvwqEcfsgHi4uJIT09nyZIl5OTkoNPp8PDw4O7du8CDB7iGbNmyhbi4OCIjI9m3bx86nY4pU6Zo9f8qD1+rsrKyIjU1ldzcXO3bgPrO3bVrVwDOnj37TPpgbGxscGxkZKTtfmZpaUl+fj5paWk4ODiQkJCAl5eXtnNUs2bNHguQ6rtnDQkPD0en05GcnMx3332HTqfDxsamwevftm1bKisrnzimhwHv79OetMNbQ9flaebq0xoxYgQXLlxgxowZXLx4kSFDhhAXF2dQ5vr169ja2j6T8wkhxPNCAhIhxFM7dOgQp0+fZvz48QDaA1ZFRYVWprHbwx47doyIiAjGjh2Lh4cH9vb2lJeXa/keHh7U1tZy5MiReuv369eP6OhofHx8cHFxobS0VMu3srLC0dGRY8eOPVavR48edbbp7OyMsbExx48f19IqKyspKSnRjs+ePcu1a9dISkrC39+f7t27P/FXeG9vb3r06MHy5cvrfKh+GCy4ublx6tQpbt26ZdDfZs2aPbZK05AWLVowdOhQli5dSkFBAeXl5Rw6dAh4cM8evV83b96krKys3rZMTEyAB5sIPOrYsWPExMQwcuRI3N3dMTU15erVqw32y8fHh7NnzzZ6xehZasxcrWuszs7OmJiYGMyjmpoacnNzDeaRra0t4eHhbN68mRUrVrB27VqDts+cOYOPj88zG48QQjwPJCARQjTozp07XLp0iZ9//pn8/HyWLFlCSEgIo0ePZvLkycCDVYy+fftqH6sfOXKE+fPnN6p9V1dXdu7ciU6n49SpU4SGhho8rHfq1Inw8HBef/11du3aRVlZGYcPH9Y+Wnd1dSUvL4+9e/dSUlLCggULDHY+ggcfqH/44Yds3bqV4uJi5syZg06nIzY2ts4+WVhYEBkZyaxZszh06BBnzpwhIiLC4EP0jh07YmJiwsqVKzl//jy7d+9m0aJFDY7VyMiI9evXU1JSgr+/P99++y3nz5+noKCAxYsXExISAsCkSZMwMzMjPDycM2fOkJ2dzdtvv01YWJj2GtOT7Nmzh5SUFHQ6HRcuXOCrr76itrZWC2gCAwPZtGkTOTk5nD59mvDwcIPXv36vXbt2mJubk5WVxeXLl7lx4wbw4Ppv2rQJvV7P8ePHmTRp0hNXtQYPHkx1dTWFhYWNGsuz1Ji56uTkhJGREXv27OHKlStUV1fTsmVLpk2bxqxZs8jKyqKoqIioqChu376tbV+ckJBARkYG586do7CwkD179uDm5qa1e/v2bU6cOMFLL730t45ZCCH+6SQgEUI0KCsrCwcHBzp16sTw4cPJzs4mJSWFjIwMgwfYL7/8knv37uHr68s777zD//73v0a1//HHH2NtbU2/fv0IDg4mKCiInj17GpRZvXo1EyZMIDo6mi5duhAVFaWtHkydOpVx48YxceJE+vTpw7Vr14iOjjaoHxMTw7vvvsvMmTPx8PAgKyuL3bt34+rqWm+/PvroI/z9/QkODmbo0KEMGDAAX19fLd/W1pYNGzawfft2evToQVJSEsuWLXvieHv37k1eXh4uLi5ERUXh5ubGmDFjKCws1LbhfeGFF9i7dy/Xr1/Hz8+PCRMmMGTIEFatWtWoawoPduzauXMngYGBuLm58fnnn5OWloa7uzsAc+fOJSAggNGjRzNq1ChefvllnJ2d622vRYsWpKSksGbNGhwdHbXgad26dVRWVtKzZ0/CwsK0bXEbYmNjw9ixY0lNTW30eJ6lJ83V9u3b8/777zNnzhzs7Oy03c2SkpIYP348YWFh9OzZk3PnzrF3716sra2BBysrc+fOxdPTk4EDB9K8eXO2bNmitZuRkUHHjh3x9/f/+wYrhBD/AkaqKdbMhRDiD5o6dSqvvvoqQ4YMaequiD+hoKCAYcOGUVpaqm3h/Lzr27cvMTExhIaGNnVXhBDiH0VWSIQQ/wo3btygtLQUExMTdu/e3dTdEX+Sp6cnH374YYPfrTxPrl69yrhx43jttdeauitCCPGPIyskQoh/haKiIvr27YuZmRmbN2+W9/CFEEKI54QEJEIIIYQQQogmI69sCSGEEEIIIZqMBCRCCCGEEEKIJiMBiRBCCCGEEKLJSEAihBBCCCGEaDISkAghhBBCCCGajAQkQgghhBBCiCYjAYkQQgghhBCiyUhAIoQQQgghhGgy/wdcXQ9wX3eBOwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = df['Gravidade_Sintomas']\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "scatter = plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], c=colors, cmap='viridis')\n",
+    "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n",
+    "plt.xlabel('Duração da Consulta (minutos)')\n",
+    "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n",
+    "plt.colorbar(scatter, label='Gravidade dos Sintomas')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/exercicio1.ipynb b/exercicios/para-sala/exercicio1.ipynb
new file mode 100644
index 0000000..fdda3d9
--- /dev/null
+++ b/exercicios/para-sala/exercicio1.ipynb
@@ -0,0 +1,939 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('dados_ficticios.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "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>Categoria</th>\n",
+       "      <th>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>53.417560</td>\n",
+       "      <td>126.034826</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>D</td>\n",
+       "      <td>68.761708</td>\n",
+       "      <td>131.230224</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A</td>\n",
+       "      <td>59.504238</td>\n",
+       "      <td>100.640083</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C</td>\n",
+       "      <td>44.230963</td>\n",
+       "      <td>84.931643</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-01-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.015853</td>\n",
+       "      <td>109.199443</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>D</td>\n",
+       "      <td>54.919192</td>\n",
+       "      <td>86.445693</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-01-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>A</td>\n",
+       "      <td>36.797668</td>\n",
+       "      <td>140.267745</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.314588</td>\n",
+       "      <td>102.730707</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>C</td>\n",
+       "      <td>61.794401</td>\n",
+       "      <td>92.693569</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>B</td>\n",
+       "      <td>45.308243</td>\n",
+       "      <td>103.693606</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "0         C  53.417560  126.034826        1  2023-01-01\n",
+       "1         D  68.761708  131.230224        2  2023-01-02\n",
+       "2         A  59.504238  100.640083        6  2023-01-03\n",
+       "3         C  44.230963   84.931643        5  2023-01-04\n",
+       "4         C  41.015853  109.199443        9  2023-01-05\n",
+       "5         D  54.919192   86.445693        5  2023-01-06\n",
+       "6         A  36.797668  140.267745        2  2023-01-07\n",
+       "7         A  68.314588  102.730707        4  2023-01-08\n",
+       "8         C  61.794401   92.693569        9  2023-01-09\n",
+       "9         B  45.308243  103.693606        1  2023-01-10"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DataFrame.info of     Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "0           C  53.417560  126.034826        1  2023-01-01\n",
+       "1           D  68.761708  131.230224        2  2023-01-02\n",
+       "2           A  59.504238  100.640083        6  2023-01-03\n",
+       "3           C  44.230963   84.931643        5  2023-01-04\n",
+       "4           C  41.015853  109.199443        9  2023-01-05\n",
+       "..        ...        ...         ...      ...         ...\n",
+       "995         A  45.120886   80.856982        4  2025-09-22\n",
+       "996         A  71.573082  106.875758        7  2025-09-23\n",
+       "997         D  43.942851   99.026953        6  2025-09-24\n",
+       "998         D  57.420954  100.655940        2  2025-09-25\n",
+       "999         C  52.992926   84.830093        8  2025-09-26\n",
+       "\n",
+       "[1000 rows x 5 columns]>"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "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>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.401664</td>\n",
+       "      <td>100.759179</td>\n",
+       "      <td>5.014000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>10.012925</td>\n",
+       "      <td>19.673028</td>\n",
+       "      <td>2.588533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>21.037446</td>\n",
+       "      <td>39.609757</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>43.749954</td>\n",
+       "      <td>87.668313</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>50.366335</td>\n",
+       "      <td>100.416802</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>56.815984</td>\n",
+       "      <td>113.760305</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>80.788808</td>\n",
+       "      <td>163.862151</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Valor_1      Valor_2      Valor_3\n",
+       "count  1000.000000  1000.000000  1000.000000\n",
+       "mean     50.401664   100.759179     5.014000\n",
+       "std      10.012925    19.673028     2.588533\n",
+       "min      21.037446    39.609757     1.000000\n",
+       "25%      43.749954    87.668313     3.000000\n",
+       "50%      50.366335   100.416802     5.000000\n",
+       "75%      56.815984   113.760305     7.000000\n",
+       "max      80.788808   163.862151     9.000000"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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>Categoria</th>\n",
+       "      <th>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.314588</td>\n",
+       "      <td>102.730707</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>A</td>\n",
+       "      <td>65.511520</td>\n",
+       "      <td>101.140262</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>A</td>\n",
+       "      <td>67.553408</td>\n",
+       "      <td>121.450147</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-02-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>A</td>\n",
+       "      <td>69.647251</td>\n",
+       "      <td>97.230880</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-03-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>A</td>\n",
+       "      <td>63.668743</td>\n",
+       "      <td>100.293764</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-03-29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.062928</td>\n",
+       "      <td>76.601663</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-06-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>A</td>\n",
+       "      <td>62.012139</td>\n",
+       "      <td>114.736878</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-06-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>A</td>\n",
+       "      <td>66.764373</td>\n",
+       "      <td>74.914212</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2023-07-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>A</td>\n",
+       "      <td>63.556379</td>\n",
+       "      <td>84.016160</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-08-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>A</td>\n",
+       "      <td>74.457520</td>\n",
+       "      <td>96.390402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2023-08-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.941915</td>\n",
+       "      <td>117.055474</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2023-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>A</td>\n",
+       "      <td>61.593298</td>\n",
+       "      <td>90.501927</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-11-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>A</td>\n",
+       "      <td>67.547942</td>\n",
+       "      <td>137.997639</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-11-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>A</td>\n",
+       "      <td>66.654744</td>\n",
+       "      <td>119.006152</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-12-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.124486</td>\n",
+       "      <td>111.029708</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2024-01-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.807807</td>\n",
+       "      <td>70.848970</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2024-02-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>A</td>\n",
+       "      <td>64.469779</td>\n",
+       "      <td>132.244413</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2024-03-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.318445</td>\n",
+       "      <td>94.629387</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2024-03-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>A</td>\n",
+       "      <td>65.795721</td>\n",
+       "      <td>78.192018</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2024-03-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>A</td>\n",
+       "      <td>74.553001</td>\n",
+       "      <td>82.676503</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2024-04-06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "7           A  68.314588  102.730707        4  2023-01-08\n",
+       "30          A  65.511520  101.140262        2  2023-01-31\n",
+       "35          A  67.553408  121.450147        6  2023-02-05\n",
+       "74          A  69.647251   97.230880        5  2023-03-16\n",
+       "87          A  63.668743  100.293764        1  2023-03-29\n",
+       "152         A  60.062928   76.601663        5  2023-06-02\n",
+       "178         A  62.012139  114.736878        1  2023-06-28\n",
+       "185         A  66.764373   74.914212        7  2023-07-05\n",
+       "217         A  63.556379   84.016160        9  2023-08-06\n",
+       "228         A  74.457520   96.390402        3  2023-08-17\n",
+       "243         A  60.941915  117.055474        7  2023-09-01\n",
+       "305         A  61.593298   90.501927        9  2023-11-02\n",
+       "321         A  67.547942  137.997639        6  2023-11-18\n",
+       "352         A  66.654744  119.006152        6  2023-12-19\n",
+       "383         A  68.124486  111.029708        2  2024-01-19\n",
+       "399         A  60.807807   70.848970        6  2024-02-04\n",
+       "426         A  64.469779  132.244413        2  2024-03-02\n",
+       "428         A  60.318445   94.629387        6  2024-03-04\n",
+       "440         A  65.795721   78.192018        5  2024-03-16\n",
+       "461         A  74.553001   82.676503        7  2024-04-06"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filter = df[(df['Categoria'] == 'A') & (df['Valor_1'] > 60)]\n",
+    "filter.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Data'] = pd.to_datetime(df['Data'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "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>Categoria</th>\n",
+       "      <th>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121</th>\n",
+       "      <td>A</td>\n",
+       "      <td>41.360092</td>\n",
+       "      <td>119.106104</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>B</td>\n",
+       "      <td>50.485216</td>\n",
+       "      <td>101.768138</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-05-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.690499</td>\n",
+       "      <td>129.550602</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>A</td>\n",
+       "      <td>52.704568</td>\n",
+       "      <td>77.166218</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>D</td>\n",
+       "      <td>49.497619</td>\n",
+       "      <td>96.126811</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>B</td>\n",
+       "      <td>47.610520</td>\n",
+       "      <td>85.663554</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>A</td>\n",
+       "      <td>40.924363</td>\n",
+       "      <td>62.669268</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.232287</td>\n",
+       "      <td>98.346386</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>D</td>\n",
+       "      <td>57.553912</td>\n",
+       "      <td>97.565050</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-05-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Categoria    Valor_1     Valor_2  Valor_3       Data\n",
+       "120         D  44.076061  113.927749        6 2023-05-01\n",
+       "121         A  41.360092  119.106104        5 2023-05-02\n",
+       "122         B  50.485216  101.768138        9 2023-05-03\n",
+       "123         C  41.690499  129.550602        2 2023-05-04\n",
+       "124         A  52.704568   77.166218        8 2023-05-05\n",
+       "125         D  49.497619   96.126811        5 2023-05-06\n",
+       "126         B  47.610520   85.663554        2 2023-05-07\n",
+       "127         A  40.924363   62.669268        6 2023-05-08\n",
+       "128         D  44.232287   98.346386        8 2023-05-09\n",
+       "129         D  57.553912   97.565050        4 2023-05-10"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[(df['Data'] >= '2023-05-01') & (df['Data'] <= '2023-05-31')]\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agrupado_media = df.groupby('Categoria')[['Valor_1', 'Valor_2']].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "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>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>47.012307</td>\n",
+       "      <td>101.616545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>49.047868</td>\n",
+       "      <td>93.715846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>39.910509</td>\n",
+       "      <td>103.563094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>48.450760</td>\n",
+       "      <td>101.268104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Valor_1     Valor_2\n",
+       "Categoria                       \n",
+       "A          47.012307  101.616545\n",
+       "B          49.047868   93.715846\n",
+       "C          39.910509  103.563094\n",
+       "D          48.450760  101.268104"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_media"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agrupado_max = df.groupby('Categoria')[['Valor_1', 'Valor_2']].max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "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>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>57.513871</td>\n",
+       "      <td>137.081851</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>50.485216</td>\n",
+       "      <td>101.768138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>49.339202</td>\n",
+       "      <td>129.550602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>57.553912</td>\n",
+       "      <td>137.691726</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Valor_1     Valor_2\n",
+       "Categoria                       \n",
+       "A          57.513871  137.081851\n",
+       "B          50.485216  101.768138\n",
+       "C          49.339202  129.550602\n",
+       "D          57.553912  137.691726"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_max "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Valor_3  Contagem\n",
+      "Categoria                   \n",
+      "A               58        12\n",
+      "B               11         2\n",
+      "C               38         7\n",
+      "D               50        10\n"
+     ]
+    }
+   ],
+   "source": [
+    "agrupado_custom = df.groupby('Categoria').agg({\n",
+    "    'Valor_3': 'sum',\n",
+    "    'Categoria': 'count'\n",
+    "}).rename(columns={'Categoria': 'Contagem'})\n",
+    "print(agrupado_custom)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "media_valor_1 = df.groupby('Categoria')['Valor_1'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "media_valor_1.plot(kind='bar', color='#4c7', title='Média de Valor_1 por categoria')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Média de Valor_1')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}