diff --git a/exercicios/para-casa/CafeECodigo.db b/exercicios/para-casa/CafeECodigo.db
new file mode 100644
index 0000000..d8cc8d9
Binary files /dev/null and b/exercicios/para-casa/CafeECodigo.db differ
diff --git a/exercicios/para-casa/CoffeeAndCode.csv b/exercicios/para-casa/CoffeeAndCode.csv
new file mode 100644
index 0000000..87f8949
--- /dev/null
+++ b/exercicios/para-casa/CoffeeAndCode.csv
@@ -0,0 +1,101 @@
+CodingHours,CoffeeCupsPerDay,CoffeeTime,CodingWithoutCoffee,CoffeeType,CoffeeSolveBugs,Gender,Country,AgeRange
+8,2,Before coding,Yes,Caffè latte,Sometimes,Female,Lebanon,18 to 29
+3,2,Before coding,Yes,Americano,Yes,Female,Lebanon,30 to 39
+5,3,While coding,No,Nescafe,Yes,Female,Lebanon,18 to 29
+8,2,Before coding,No,Nescafe,Yes,Male,Lebanon,NA
+10,3,While coding,Sometimes,Turkish,No,Male,Lebanon,18 to 29
+8,2,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,30 to 39
+5,2,While coding,Yes,Nescafe,Sometimes,Male,Lebanon,NA
+10,4,Before coding,Sometimes,Turkish,Sometimes,Male,Lebanon,18 to 29
+10,2,While coding,Yes,American Coffee,Sometimes,Male,Lebanon,30 to 39
+10,2,While coding,Yes,Nescafe,No,Male,Lebanon,30 to 39
+10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,30 to 39
+2,3,Before coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39
+8,2,Before and while coding,No,Nescafe,Yes,Male,Lebanon,30 to 39
+9,3,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,30 to 39
+6,1,Before coding,Yes,Nescafe,Sometimes,Male,Lebanon,18 to 29
+6,3,While coding,No,Americano,Yes,Female,Lebanon,30 to 39
+10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,30 to 39
+6,4,While coding,Yes,Turkish,Sometimes,Male,Lebanon,30 to 39
+8,3,While coding,No,Nescafe,No,Male,Lebanon,18 to 29
+3,1,In the morning,Yes,Nescafe,Sometimes,Female,Lebanon,30 to 39
+3,3,While coding,Yes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29
+9,5,While coding,Sometimes,Nescafe,Yes,Male,Lebanon,18 to 29
+4,2,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29
+8,3,While coding,No,Turkish,No,Male,Lebanon,40 to 49
+5,3,While coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,Under 18
+6,3,While coding,Yes,Nescafe,No,Female,Lebanon,30 to 39
+7,4,Before coding,Sometimes,Espresso (Short Black),No,Male,Lebanon,30 to 39
+8,4,While coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39
+7,1,Before coding,Yes,Nescafe,Yes,Male,Lebanon,30 to 39
+2,2,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,40 to 49
+10,6,All the time,No,American Coffee,Yes,Male,Lebanon,30 to 39
+2,2,While coding,No,Nescafe,Sometimes,Female,Lebanon,18 to 29
+9,2,Before coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29
+4,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39
+6,2,While coding,Sometimes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29
+2,2,While coding,Yes,American Coffee,No,Male,Lebanon,30 to 39
+2,3,All the time,Sometimes,Turkish,Sometimes,Male,Lebanon,30 to 39
+7,7,After coding,Yes,Turkish,Yes,Female,Lebanon,40 to 49
+1,1,While coding,Yes,Cappuccino,Yes,Male,Lebanon,40 to 49
+4,2,While coding,Yes,Turkish,No,Male,Lebanon,50 to 59
+8,2,While coding,Sometimes,Turkish,No,Male,Lebanon,30 to 39
+8,4,While coding,Sometimes,Turkish,Yes,Male,Lebanon,30 to 39
+3,2,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,30 to 39
+10,6,While coding,Sometimes,NA,No,Male,Lebanon,18 to 29
+3,2,After coding,Yes,Espresso (Short Black),Sometimes,Male,Lebanon,40 to 49
+7,2,While coding,Sometimes,American Coffee,No,Female,Lebanon,18 to 29
+2,1,While coding,Sometimes,Caffè latte,No,Female,Lebanon,18 to 29
+9,1,In the morning,Yes,Nescafe,No,Female,Lebanon,18 to 29
+8,2,Before coding,No,Nescafe,No,Female,Lebanon,18 to 29
+6,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39
+6,1,Before coding,Yes,Nescafe,No,Male,Lebanon,30 to 39
+8,3,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29
+8,2,Before coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29
+3,3,While coding,No,Turkish,No,Male,Lebanon,18 to 29
+7,4,No specific time,Yes,Cappuccino,Sometimes,Male,Lebanon,18 to 29
+4,7,Before and while coding,No,Double Espresso (Doppio),Sometimes,Male,Lebanon,18 to 29
+6,4,While coding,Sometimes,Espresso (Short Black),Sometimes,Male,Lebanon,18 to 29
+10,8,While coding,No,Turkish,Sometimes,Male,Lebanon,18 to 29
+8,2,While coding,Sometimes,Turkish,No,Male,Lebanon,18 to 29
+2,4,While coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,40 to 49
+6,2,Before coding,Yes,Caffè latte,No,Male,Lebanon,18 to 29
+7,4,While coding,No,American Coffee,Sometimes,Male,Lebanon,18 to 29
+8,2,While coding,Sometimes,Turkish,Sometimes,Male,Lebanon,18 to 29
+7,4,Before coding,No,Turkish,Yes,Male,Lebanon,18 to 29
+8,4,While coding,No,American Coffee,Sometimes,Male,Lebanon,18 to 29
+8,2,Before coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29
+6,1,In the morning,Yes,Nescafe,No,Female,Lebanon,30 to 39
+10,2,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29
+8,4,Before and while coding,No,Turkish,Yes,Male,Lebanon,18 to 29
+10,6,While coding,Sometimes,Double Espresso (Doppio),No,Male,Lebanon,30 to 39
+6,4,While coding,Sometimes,Espresso (Short Black),Yes,Male,Lebanon,18 to 29
+10,8,All the time,No,American Coffee,Yes,Male,Lebanon,18 to 29
+5,5,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29
+8,4,Before and while coding,Sometimes,Turkish,Sometimes,Male,Lebanon,30 to 39
+4,2,While coding,No,Nescafe,Yes,Female,Lebanon,18 to 29
+6,6,While coding,Sometimes,Cappuccino,No,Male,Lebanon,18 to 29
+8,2,While coding,Sometimes,Nescafe,Sometimes,Male,Lebanon,30 to 39
+9,3,While coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29
+7,2,While coding,Sometimes,Caffè latte,No,Male,Lebanon,18 to 29
+4,5,While coding,Sometimes,American Coffee,Yes,Male,Lebanon,18 to 29
+2,1,While coding,Yes,Nescafe,No,Male,Lebanon,18 to 29
+7,3,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29
+6,3,While coding,Yes,Nescafe,Yes,Female,Lebanon,18 to 29
+3,3,While coding,Sometimes,Caffè latte,Sometimes,Male,Lebanon,18 to 29
+10,2,While coding,Yes,American Coffee,No,Male,Lebanon,18 to 29
+10,3,While coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29
+3,1,While coding,Sometimes,Nescafe,Sometimes,Male,Lebanon,18 to 29
+3,3,While coding,Sometimes,American Coffee,Sometimes,Female,Lebanon,18 to 29
+9,7,All the time,No,Espresso (Short Black),Yes,Male,Lebanon,18 to 29
+10,1,Before coding,Sometimes,American Coffee,Sometimes,Male,Lebanon,18 to 29
+7,1,Before coding,Sometimes,Cappuccino,Sometimes,Male,Lebanon,18 to 29
+5,3,While coding,Sometimes,Turkish,Sometimes,Female,Lebanon,18 to 29
+5,2,Before coding,Sometimes,Nescafe,Yes,Female,Lebanon,18 to 29
+3,1,Before coding,Yes,Nescafe,No,Female,Lebanon,18 to 29
+4,2,While coding,Yes,Nescafe,Sometimes,Male,Lebanon,18 to 29
+6,2,Before coding,Yes,Nescafe,Yes,Male,Lebanon,18 to 29
+4,1,Before coding,Sometimes,Nescafe,Sometimes,Female,Lebanon,18 to 29
+10,3,Before coding,Yes,Cappuccino,Yes,Male,Lebanon,Under 18
+2,2,While coding,Sometimes,Espresso (Short Black),Sometimes,Female,Lebanon,18 to 29
+10,4,Before coding,Sometimes,Double Espresso (Doppio),Sometimes,Male,Lebanon,18 to 29
\ No newline at end of file
diff --git a/exercicios/para-casa/Projeto Guiado S13.ipynb b/exercicios/para-casa/Projeto Guiado S13.ipynb
new file mode 100644
index 0000000..04fe768
--- /dev/null
+++ b/exercicios/para-casa/Projeto Guiado S13.ipynb	
@@ -0,0 +1,652 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Análise dos dados: café e código\n",
+    "\n",
+    "A presente análise investiga o conjunto de dados públicos disponiblizado através da plataforma Kaggle (www.kaggle.com). Os  dados escolhido trazem como contexto o consumo de café no cotidiano de pessoas que trabalham na área de TI. \n",
+    "\n",
+    "### O conjunto de dados apresenta 9 colunas:\n",
+    "\n",
+    "1. CodingHours: horas codificando;\n",
+    "2. CoffeeCupsPerDay: xícaras de cafés por dia;\n",
+    "3. CoffeeTime: se o consumo de café se dá antes, durante ou depois de codar;\n",
+    "4. CodingWithoutCoffee: se coda sem café;\n",
+    "5. CoffeeType: o tipo de café que é consumido;\n",
+    "6. CoffeeSolveBugs: se o consumo de café ajuda na solução de bugs;\n",
+    "7. Gender: gênero; \n",
+    "8. Country: país;\n",
+    "9. AgeRange: faixa etária.\n",
+    "\n",
+    "Referência: https://www.kaggle.com/datasets/shrutikunapuli/coffee-and-code-dataset\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### De perto e de dentro: observando os dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importando as bibliotecas: Pandas e Matplotlib\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CodingHours</th>\n",
+       "      <th>CoffeeCupsPerDay</th>\n",
+       "      <th>CoffeeTime</th>\n",
+       "      <th>CodingWithoutCoffee</th>\n",
+       "      <th>CoffeeType</th>\n",
+       "      <th>CoffeeSolveBugs</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>AgeRange</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Caffè latte</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Americano</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>30 to 39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>While coding</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Nescafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Nescafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>While coding</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Turkish</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Nescafe</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Nescafe</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Cappuccino</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>Under 18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>While coding</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Espresso (Short Black)</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Before coding</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Double Espresso (Doppio)</td>\n",
+       "      <td>Sometimes</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Lebanon</td>\n",
+       "      <td>18 to 29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    CodingHours  CoffeeCupsPerDay     CoffeeTime CodingWithoutCoffee  \\\n",
+       "0             8                 2  Before coding                 Yes   \n",
+       "1             3                 2  Before coding                 Yes   \n",
+       "2             5                 3   While coding                  No   \n",
+       "3             8                 2  Before coding                  No   \n",
+       "4            10                 3   While coding           Sometimes   \n",
+       "..          ...               ...            ...                 ...   \n",
+       "95            6                 2  Before coding                 Yes   \n",
+       "96            4                 1  Before coding           Sometimes   \n",
+       "97           10                 3  Before coding                 Yes   \n",
+       "98            2                 2   While coding           Sometimes   \n",
+       "99           10                 4  Before coding           Sometimes   \n",
+       "\n",
+       "                  CoffeeType CoffeeSolveBugs  Gender  Country  AgeRange  \n",
+       "0                Caffè latte       Sometimes  Female  Lebanon  18 to 29  \n",
+       "1                  Americano             Yes  Female  Lebanon  30 to 39  \n",
+       "2                    Nescafe             Yes  Female  Lebanon  18 to 29  \n",
+       "3                    Nescafe             Yes    Male  Lebanon       NaN  \n",
+       "4                    Turkish              No    Male  Lebanon  18 to 29  \n",
+       "..                       ...             ...     ...      ...       ...  \n",
+       "95                   Nescafe             Yes    Male  Lebanon  18 to 29  \n",
+       "96                   Nescafe       Sometimes  Female  Lebanon  18 to 29  \n",
+       "97                Cappuccino             Yes    Male  Lebanon  Under 18  \n",
+       "98    Espresso (Short Black)       Sometimes  Female  Lebanon  18 to 29  \n",
+       "99  Double Espresso (Doppio)       Sometimes    Male  Lebanon  18 to 29  \n",
+       "\n",
+       "[100 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Usando a função \"read_csv\" para ler o arquivo, as 5 primeiras e 5 últimas linhas. \n",
+    "df = pd.read_csv(\"CoffeeAndCode.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CodingHours             int64\n",
+       "CoffeeCupsPerDay        int64\n",
+       "CoffeeTime             object\n",
+       "CodingWithoutCoffee    object\n",
+       "CoffeeType             object\n",
+       "CoffeeSolveBugs        object\n",
+       "Gender                 object\n",
+       "Country                object\n",
+       "AgeRange               object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Verificando os tipos de dados:\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "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>CodingHours</th>\n",
+       "      <th>CoffeeCupsPerDay</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>6.410000</td>\n",
+       "      <td>2.890000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>2.644205</td>\n",
+       "      <td>1.613673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       CodingHours  CoffeeCupsPerDay\n",
+       "count   100.000000        100.000000\n",
+       "mean      6.410000          2.890000\n",
+       "std       2.644205          1.613673\n",
+       "min       1.000000          1.000000\n",
+       "25%       4.000000          2.000000\n",
+       "50%       7.000000          2.500000\n",
+       "75%       8.000000          4.000000\n",
+       "max      10.000000          8.000000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Verificando o resumo estatístico dos dados:\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Valores nulos por coluna do dataframe:\n",
+      "CodingHours            0\n",
+      "CoffeeCupsPerDay       0\n",
+      "CoffeeTime             0\n",
+      "CodingWithoutCoffee    0\n",
+      "CoffeeType             1\n",
+      "CoffeeSolveBugs        0\n",
+      "Gender                 0\n",
+      "Country                0\n",
+      "AgeRange               2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Verificando valores nulos no dataset:\n",
+    "print(\"Valores nulos por coluna do dataframe:\")\n",
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(97, 9)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Removendo entradas (linhas) contendo valores nulos em colunas específicas:\n",
+    "df = df.dropna(subset=['CoffeeType', 'AgeRange'])\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(59, 9)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Filtrando informações, nesse caso verifico a faixa etária contendo apenas os valore de 18 to 29 da coluna AgeRange:\n",
+    "df_filtrado = df[df['AgeRange'].str.contains('18 to 29')]\n",
+    "df_filtrado.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lCjJ9hUnQAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdFycXQAA4M4UHx+vpKQkZ5dRKD4+Pqpevbqzy8BtQAACADhcfHy86tarr6tX0p1dSqG4uZfX0SMxhCATIAABABwuKSlJV6+ky7v7C3L1DnJ2OQWSmXxKyetnKykpiQBkAgQgAECxcfUOktU/1NllADkwCRoAAJgOAQgAAJiOUwPQjh071KNHDwUGBspisWjt2rV2/RaLJdfljTfeyHOfkydPzjG+Xr16xXwmAACgNHFqALp8+bKaNm2qhQsX5tp/9uxZu2Xp0qWyWCx69NFH891vw4YN7bbbuXNncZQPAABKKadOgo6IiFBERESe/f7+/nbr69atU6dOnVSzZs189+vi4pJjWwAAgOtKzRygxMREbdiwQU8//fRNxx47dkyBgYGqWbOm+vXrp/j4+NtQIQAAKC1KzWPwK1askIeHh/7+97/nOy4sLEzLly9X3bp1dfbsWU2ZMkX33XefDh06JA8Pj1y3ycjIUEZGhm09NTXVobUDAICSpdQEoKVLl6pfv35yc3PLd9xfb6k1adJEYWFhCg4O1ieffJLn1aMZM2ZoypQpDq0XAACUXKXiFth3332no0ePasiQIYXe1svLS3Xq1NHx48fzHDN+/HilpKTYllOnTt1KuQAAoIQrFQHo/fffV4sWLdS0adNCb5uWlqbY2FgFBATkOcZqtapSpUp2CwAAuHM5NQClpaUpOjpa0dHRkqQTJ04oOjrabtJyamqqPv300zyv/nTu3FkLFiywrY8ZM0bbt29XXFycdu3apUceeURly5ZV3759i/VcAABA6eHUOUD79+9Xp06dbOuRkZGSpIEDB2r58uWSpNWrV8swjDwDTGxsrJKSkmzrp0+fVt++fZWcnCxfX1+1a9dOe/bska+vb/GdCAAAKFWcGoA6duwowzDyHTNs2DANGzYsz/64uDi79dWrVzuiNAAAcAcrFXOAAAAAHIkABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATIcABAAATMfF2QUAAICii4+PV1JSkrPLKBQfHx9Vr17dqTUQgAAAKKXi4+NVt159Xb2S7uxSCsXNvbyOHolxaggiAAEAUEolJSXp6pV0eXd/Qa7eQc4up0Ayk08pef1sJSUlEYAAAEDRuXoHyeof6uwyShUmQQMAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANMhAAEAANNxagDasWOHevToocDAQFksFq1du9auf9CgQbJYLHZL165db7rfhQsXqkaNGnJzc1NYWJh++OGHYjoDAABQGjk1AF2+fFlNmzbVwoUL8xzTtWtXnT171rZ89NFH+e7z448/VmRkpCZNmqSDBw+qadOmCg8P17lz5xxdPgAAKKVcnHnwiIgIRURE5DvGarXK39+/wPucM2eOhg4dqsGDB0uSFi9erA0bNmjp0qUaN27cLdULAADuDCV+DtC2bdtUpUoV1a1bV88++6ySk5PzHHvt2jUdOHBAXbp0sbWVKVNGXbp00e7du/PcLiMjQ6mpqXYLAAC4c5XoANS1a1etXLlSUVFRev3117V9+3ZFREQoKysr1/FJSUnKysqSn5+fXbufn58SEhLyPM6MGTPk6elpW4KCghx6HgAAoGRx6i2wm+nTp4/t58aNG6tJkyaqVauWtm3bps6dOzvsOOPHj1dkZKRtPTU1lRAEAMAdrERfAbpRzZo15ePjo+PHj+fa7+Pjo7JlyyoxMdGuPTExMd95RFarVZUqVbJbAADAnatUBaDTp08rOTlZAQEBufaXK1dOLVq0UFRUlK0tOztbUVFRat269e0qEwAAlHBODUBpaWmKjo5WdHS0JOnEiROKjo5WfHy80tLSNHbsWO3Zs0dxcXGKiorSww8/rNDQUIWHh9v20blzZy1YsMC2HhkZqXfffVcrVqxQTEyMnn32WV2+fNn2VBgAAIBT5wDt379fnTp1sq1fn4czcOBALVq0SD/99JNWrFihS5cuKTAwUA888ICmTZsmq9Vq2yY2NlZJSUm29d69e+v8+fOaOHGiEhIS1KxZM23cuDHHxGgAAGBeTg1AHTt2lGEYefZ/8803N91HXFxcjrYRI0ZoxIgRt1IaAAC4g5WqOUAAAACOQAACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACm49QvQwUAZ4iPj1dSUpKzyygUHx8fVa9e3dllAHcMAhAAU4mPj1fdevV19Uq6s0spFDf38jp6JIYQBDgIAQiAqSQlJenqlXR5d39Brt5Bzi6nQDKTTyl5/WwlJSURgAAHIQABMCVX7yBZ/UOdXQYAJ2ESNAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB2nBqAdO3aoR48eCgwMlMVi0dq1a219mZmZeumll9S4cWNVqFBBgYGBGjBggM6cOZPvPidPniyLxWK31KtXr5jPBAAAlCZODUCXL19W06ZNtXDhwhx96enpOnjwoCZMmKCDBw/q888/19GjR/XQQw/ddL8NGzbU2bNnbcvOnTuLo3wAAFBKuTjz4BEREYqIiMi1z9PTU5s2bbJrW7BggVq1aqX4+HhVr149z/26uLjI39/fobUCAIA7R6maA5SSkiKLxSIvL698xx07dkyBgYGqWbOm+vXrp/j4+HzHZ2RkKDU11W4BAAB3rlITgK5evaqXXnpJffv2VaVKlfIcFxYWpuXLl2vjxo1atGiRTpw4ofvuu09//PFHntvMmDFDnp6etiUoKKg4TgEAAJQQpSIAZWZm6vHHH5dhGFq0aFG+YyMiItSrVy81adJE4eHh+uqrr3Tp0iV98skneW4zfvx4paSk2JZTp045+hQAAEAJ4tQ5QAVxPfycPHlSW7ZsyffqT268vLxUp04dHT9+PM8xVqtVVqv1VksFAAClRIm+AnQ9/Bw7dkybN2+Wt7d3ofeRlpam2NhYBQQEFEOFAACgNHJqAEpLS1N0dLSio6MlSSdOnFB0dLTi4+OVmZmpxx57TPv379cHH3ygrKwsJSQkKCEhQdeuXbPto3PnzlqwYIFtfcyYMdq+fbvi4uK0a9cuPfLIIypbtqz69u17u08PAACUUE69BbZ//3516tTJth4ZGSlJGjhwoCZPnqwvv/xSktSsWTO77bZu3aqOHTtKkmJjY5WUlGTrO336tPr27avk5GT5+vqqXbt22rNnj3x9fYv3ZAAAQKnh1ADUsWNHGYaRZ39+fdfFxcXZra9evfpWywIAAHe4Ej0HCAAAoDgQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkUOQBdunRJ7733nsaPH68LFy5Ikg4ePKjff//dYcUBAAAUB5eibPTTTz+pS5cu8vT0VFxcnIYOHarKlSvr888/V3x8vFauXOnoOgEAABymSFeAIiMjNWjQIB07dkxubm629gcffFA7duxwWHEAAADFoUgBaN++fXrmmWdytFetWlUJCQm3XBQAAEBxKlIAslqtSk1NzdH+66+/ytfX95aLAgAAKE5FCkAPPfSQpk6dqszMTEmSxWJRfHy8XnrpJT366KMOLRAAAMDRihSAZs+erbS0NFWpUkVXrlxRhw4dFBoaKg8PD02fPt3RNQIAADhUkZ4C8/T01KZNm7Rz50799NNPSktLU/PmzdWlSxdH1wcAAOBwRQpA17Vr107t2rVzVC0AAAC3RYED0Pz58wu805EjRxapGAAAgNuhwAFo7ty5duvnz59Xenq6vLy8JP35Zujy5curSpUqBCAAAFCiFXgS9IkTJ2zL9OnT1axZM8XExOjChQu6cOGCYmJi1Lx5c02bNq046wUAALhlRXoKbMKECXrrrbdUt25dW1vdunU1d+5cvfrqqw4rDgAAoDgUKQCdPXtW//vf/3K0Z2VlKTEx8ZaLAgAAKE5FCkCdO3fWM888o4MHD9raDhw4oGeffZZH4QEAQIlXpAC0dOlS+fv7q2XLlrJarbJarWrVqpX8/Pz03nvvObpGAAAAhyrSe4B8fX311Vdf6ddff9WRI0ckSfXq1VOdOnUcWhwAAEBxuKUXIdapU4fQAwAASp0iB6DTp0/ryy+/VHx8vK5du2bXN2fOnFsuDAAAoLgUKQBFRUXpoYceUs2aNXXkyBE1atRIcXFxMgxDzZs3d3SNAAAADlWkSdDjx4/XmDFj9PPPP8vNzU2fffaZTp06pQ4dOqhXr16OrhEAAMChihSAYmJiNGDAAEmSi4uLrly5oooVK2rq1Kl6/fXXHVogAACAoxUpAFWoUME27ycgIECxsbG2vqSkJMdUBgAAUEyKFIDuvfde7dy5U5L04IMP6oUXXtD06dP11FNP6d577y3wfnbs2KEePXooMDBQFotFa9eutes3DEMTJ05UQECA3N3d1aVLFx07duym+124cKFq1KghNzc3hYWF6YcffijU+QEAgDtbkQLQnDlzFBYWJkmaMmWKOnfurI8//lg1atTQ+++/X+D9XL58WU2bNtXChQtz7Z81a5bmz5+vxYsXa+/evapQoYLCw8N19erVPPf58ccfKzIyUpMmTdLBgwfVtGlThYeH69y5c4U7SQAAcMcq0lNgNWvWtP1coUIFLV68uEgHj4iIUERERK59hmFo3rx5evXVV/Xwww9LklauXCk/Pz+tXbtWffr0yXW7OXPmaOjQoRo8eLAkafHixdqwYYOWLl2qcePGFalOAABwZ7mlFyEWpxMnTighIcHuu8U8PT0VFham3bt35xqArl27pgMHDmj8+PG2tjJlyqhLly7avXt3nsfKyMhQRkaGbT01NdVBZwFnio+PL3Vz0nx8fFS9enVnl4ESKiYmxtklFFhpqhXmVOAAdNddd8lisRRo7IULF4pc0HUJCQmSJD8/P7t2Pz8/W9+NkpKSlJWVles217+yIzczZszQlClTbrFilCTx8fGqW6++rl5Jd3YpheLmXl5Hj8QQgmAnK+2iZLGof//+zi4FuGMUOADNmzfP9nNycrJee+01hYeHq3Xr1pKk3bt365tvvtGECRMcXmRxGz9+vCIjI23rqampCgoKcmJFuFVJSUm6eiVd3t1fkKt36fhdZiafUvL62UpKSiIAwU52RppkGKXqn+crv+1XynernF0GkKcCB6CBAwfafn700Uc1depUjRgxwtY2cuRILViwQJs3b9bzzz9/y4X5+/tLkhITExUQEGBrT0xMVLNmzXLdxsfHR2XLllViYqJde2Jiom1/ubn+jfa487h6B8nqH+rsMgCHKE3/PGcmn3J2CUC+ivQU2DfffKOuXbvmaO/atas2b958y0VJUkhIiPz9/RUVFWVrS01N1d69e21XnW5Urlw5tWjRwm6b7OxsRUVF5bkNAAAwnyIFIG9vb61bty5H+7p16+Tt7V3g/aSlpSk6OlrR0dGS/pz4HB0drfj4eFksFo0ePVqvvfaavvzyS/38888aMGCAAgMD1bNnT9s+OnfurAULFtjWIyMj9e6772rFihWKiYnRs88+q8uXL9ueCgMAACjSU2BTpkzRkCFDtG3bNtv7gPbu3auNGzfq3XffLfB+9u/fr06dOtnWr8/DGThwoJYvX64XX3xRly9f1rBhw3Tp0iW1a9dOGzdulJubm22b2NhYuyd9evfurfPnz2vixIlKSEhQs2bNtHHjxhwTowEAgHkVKQANGjRI9evX1/z58/X5559LkurXr6+dO3faAlFBdOzYUYZh5NlvsVg0depUTZ06Nc8xcXFxOdpGjBhhNz8JAADgr4r8HqCwsDB98MEHjqwFAADgtihwAEpNTVWlSpVsP+fn+jgAAICSqFAvQjx79qyqVKkiLy+vXF+KaBiGLBaLsrKyHFokAACAIxU4AG3ZskWVK1eWJG3durXYCgIAAChuBQ5AHTp0sP0cEhKioKCgHFeBDMPQqVO8/AoAAJRsRXoPUEhIiM6fP5+j/cKFCwoJCbnlogAAAIpTkQLQ9bk+N0pLS7N7Rw8AAEBJVKjH4K+/qNBisWjChAkqX768rS8rK0t79+7N83u6AAAASopCBaAff/xR0p9XgH7++WeVK1fO1leuXDk1bdpUY8aMcWyFAAAADlaoAHT96a/BgwfrzTff5H0/AACgVCrSm6CXLVvm6DoAAABumyIFoMuXL2vmzJmKiorSuXPnlJ2dbdf/22+/OaQ4AACA4lCkADRkyBBt375dTz75pAICAnJ9Igx3lvj4eCUlJTm7jAKLiYlxdgkAgBKsSAHo66+/1oYNG9S2bVtH14MSKD4+XnXr1dfVK+nOLgUAAIcoUgC66667bF+LgTtfUlKSrl5Jl3f3F+TqHeTscgrkym/7lfLdKmeXAQAooYoUgKZNm6aJEydqxYoVdu8Cwp3N1TtIVv9QZ5dRIJnJfCULACBvRQpAs2fPVmxsrPz8/FSjRg25urra9R88eNAhxQEAABSHIgWgnj17OrgMAACA26dIAWjSpEmOrgMAAOC2KdKXoQIAAJRmRboClJWVpblz5+qTTz5RfHy8rl27Ztd/4cIFhxQHAABQHIp0BWjKlCmaM2eOevfurZSUFEVGRurvf/+7ypQpo8mTJzu4RAAAAMcqUgD64IMP9O677+qFF16Qi4uL+vbtq/fee08TJ07Unj17HF0jAACAQxUpACUkJKhx48aSpIoVKyolJUWS1L17d23YsMFx1QEAABSDIgWgatWq6ezZs5KkWrVq6dtvv5Uk7du3T1ar1XHVAQAAFIMiBaBHHnlEUVFRkqR//OMfmjBhgmrXrq0BAwboqaeecmiBAAAAjlakp8Bmzpxp+7l3796qXr26du/erdq1a6tHjx4OKw4AAKA4FCkA3ah169Zq3bq1I3YFAABQ7IoUgFauXJlv/4ABA4pUDAAAwO1QpAA0atQou/XMzEylp6erXLlyKl++PAEIAACUaEWaBH3x4kW7JS0tTUePHlW7du300UcfObpGAAAAh3LIHCBJql27tmbOnKn+/fvryJEjjtotYDoxMTHOLqFQfHx8VL16dWeXAQCF4rAAJEkuLi46c+aMI3cJmEZW2kXJYlH//v2dXUqhuLmX19EjMYQgAKVKkQLQl19+abduGIbOnj2rBQsWqG3btg4pDDCb7Iw0yTDk3f0FuXoHObucAslMPqXk9bOVlJREAAJQqhQpAPXs2dNu3WKxyNfXV3/72980e/ZsR9QFmJard5Cs/qHOLgMA7mhFCkDZ2dmSpPPnz6tcuXLy9PR0aFEAAADFqdBPgV26dEnDhw+Xj4+P/P39VblyZfn7+2v8+PFKT08vjhoBAAAcqlBXgC5cuKDWrVvr999/V79+/VS/fn1J0i+//KK33npLmzZt0s6dO/XTTz9pz549GjlyZLEUDQAAcCsKdQVo6tSpKleunGJjY/XOO+9o9OjRGj16tJYsWaLjx4/r2rVrevLJJ3X//fc77LZYjRo1ZLFYcizDhw/Pdfzy5ctzjHVzc3NILQAA4M5QqCtAa9eu1TvvvCM/P78cff7+/po1a5YefPBBTZo0SQMHDnRIgfv27VNWVpZt/dChQ7r//vvVq1evPLepVKmSjh49alu3WCwOqQUAANwZChWAzp49q4YNG+bZ36hRI5UpU0aTJk265cKu8/X1tVufOXOmatWqpQ4dOuS5jcVikb+/v8NqAAAAd5ZC3QLz8fFRXFxcnv0nTpxQlSpVbrWmPF27dk2rVq3SU089le9VnbS0NAUHBysoKEgPP/ywDh8+nO9+MzIylJqaarcAAIA7V6ECUHh4uF555RVdu3YtR19GRoYmTJigrl27Oqy4G61du1aXLl3SoEGD8hxTt25dLV26VOvWrdOqVauUnZ2tNm3a6PTp03luM2PGDHl6etqWoKDS8RI6AABQNIW6BTZ16lS1bNlStWvX1vDhw1WvXj0ZhqGYmBi9/fbbysjI0MqVK4urVr3//vuKiIhQYGBgnmNat26t1q1b29bbtGmj+vXr65133tG0adNy3Wb8+PGKjIy0raemphKCAAC4gxUqAFWrVk27d+/Wc889p/Hjx8swDEl/zrm5//77tWDBgmJ7Hf7Jkye1efNmff7554XaztXVVXfffbeOHz+e5xir1Sqr1XqrJQIAgFKi0G+CDgkJ0ddff62LFy/q2LFjkqTQ0FBVrlzZ4cX91bJly1SlShV169atUNtlZWXp559/1oMPPlhMlQEAgNKmyN8Gf9ddd6lVq1aOrCVP2dnZWrZsmQYOHCgXF/uSBwwYoKpVq2rGjBmS/rxNd++99yo0NFSXLl3SG2+8oZMnT2rIkCG3pVYAAFDyFTkA3U6bN29WfHy8nnrqqRx98fHxKlPm/+ZyX7x4UUOHDlVCQoLuuusutWjRQrt27VKDBg1uZ8kAAKAEKxUB6IEHHrDNN7rRtm3b7Nbnzp2ruXPn3oaqAABAaVXoL0MFAAAo7QhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdFycXQAAACVJTEyMs0sosNJUa0lDAAIAQFJW2kXJYlH//v2dXQpuAwIQAACSsjPSJMOQd/cX5Ood5OxyCuTKb/uV8t0qZ5dRKhGAAAD4C1fvIFn9Q51dRoFkJp9ydgmlFpOgAQCA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6RCAAACA6ZToADR58mRZLBa7pV69evlu8+mnn6pevXpyc3NT48aN9dVXX92magEAQGlRogOQJDVs2FBnz561LTt37sxz7K5du9S3b189/fTT+vHHH9WzZ0/17NlThw4duo0VAwCAkq7EByAXFxf5+/vbFh8fnzzHvvnmm+ratavGjh2r+vXra9q0aWrevLkWLFhwGysGAAAlnYuzC7iZY8eOKTAwUG5ubmrdurVmzJih6tWr5zp29+7dioyMtGsLDw/X2rVr8z1GRkaGMjIybOupqam3XDdgJjExMc4uocBKU60Aik+JDkBhYWFavny56tatq7Nnz2rKlCm67777dOjQIXl4eOQYn5CQID8/P7s2Pz8/JSQk5HucGTNmaMqUKQ6tHTCDrLSLksWi/v37O7sUACiUEh2AIiIibD83adJEYWFhCg4O1ieffKKnn37aYccZP3683ZWj1NRUBQUFOWz/wJ0qOyNNMgx5d39Brt6l49+ZK7/tV8p3q5xdBgAnK9EB6EZeXl6qU6eOjh8/nmu/v7+/EhMT7doSExPl7++f736tVqusVqvD6gTMxtU7SFb/UGeXUSCZyaecXQKAEqDET4L+q7S0NMXGxiogICDX/tatWysqKsqubdOmTWrduvXtKA8AAJQSJToAjRkzRtu3b1dcXJx27dqlRx55RGXLllXfvn0lSQMGDND48eNt40eNGqWNGzdq9uzZOnLkiCZPnqz9+/drxIgRzjoFAABQApXoW2CnT59W3759lZycLF9fX7Vr10579uyRr6+vJCk+Pl5lyvxfhmvTpo0+/PBDvfrqq3r55ZdVu3ZtrV27Vo0aNXLWKQAAgBKoRAeg1atX59u/bdu2HG29evVSr169iqkiAABwJyjRt8AAAACKAwEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYTokOQDNmzNA999wjDw8PValSRT179tTRo0fz3Wb58uWyWCx2i5ub222qGAAAlAYlOgBt375dw4cP1549e7Rp0yZlZmbqgQce0OXLl/PdrlKlSjp79qxtOXny5G2qGAAAlAYuzi4gPxs3brRbX758uapUqaIDBw6offv2eW5nsVjk7+9f3OUBAIBSqkRfAbpRSkqKJKly5cr5jktLS1NwcLCCgoL08MMP6/Dhw/mOz8jIUGpqqt0CAADuXKUmAGVnZ2v06NFq27atGjVqlOe4unXraunSpVq3bp1WrVql7OxstWnTRqdPn85zmxkzZsjT09O2BAUFFccpAACAEqLUBKDhw4fr0KFDWr16db7jWrdurQEDBqhZs2bq0KGDPv/8c/n6+uqdd97Jc5vx48crJSXFtpw6dcrR5QMAgBKkRM8Bum7EiBFav369duzYoWrVqhVqW1dXV9199906fvx4nmOsVqusVuutlgkAAEqJEn0FyDAMjRgxQl988YW2bNmikJCQQu8jKytLP//8swICAoqhQgAAUBqV6CtAw4cP14cffqh169bJw8NDCQkJkiRPT0+5u7tLkgYMGKCqVatqxowZkqSpU6fq3nvvVWhoqC5duqQ33nhDJ0+e1JAhQ5x2HgAAoGQp0QFo0aJFkqSOHTvatS9btkyDBg2SJMXHx6tMmf+7kHXx4kUNHTpUCQkJuuuuu9SiRQvt2rVLDRo0uF1lAwCAEq5EByDDMG46Ztu2bXbrc+fO1dy5c4upIgAAcCco0XOAAAAAigMBCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmA4BCAAAmI6Lswswo/j4eCUlJTm7jAKLiYlxdgkAADgUAeg2i4+PV9169XX1SrqzSwEAwLQIQLdZUlKSrl5Jl3f3F+TqHeTscgrkym/7lfLdKmeXAQCAwxCAnMTVO0hW/1Bnl1EgmcmnnF0CAAAOxSRoAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOqUiAC1cuFA1atSQm5ubwsLC9MMPP+Q7/tNPP1W9evXk5uamxo0b66uvvrpNlQIAgNKgxAegjz/+WJGRkZo0aZIOHjyopk2bKjw8XOfOnct1/K5du9S3b189/fTT+vHHH9WzZ0/17NlThw4dus2VAwCAkqrEB6A5c+Zo6NChGjx4sBo0aKDFixerfPnyWrp0aa7j33zzTXXt2lVjx45V/fr1NW3aNDVv3lwLFiy4zZUDAICSqkQHoGvXrunAgQPq0qWLra1MmTLq0qWLdu/enes2u3fvthsvSeHh4XmOBwAA5uPi7ALyk5SUpKysLPn5+dm1+/n56ciRI7luk5CQkOv4hISEPI+TkZGhjIwM23pKSookKTU1tail5yktLe3PYyYcV/a1qw7ff3HITD4liZqLGzXfHtR8e1Dz7VEqa75wWtKffw8d/Xf2+v4Mw7j5YKME+/333w1Jxq5du+zax44da7Rq1SrXbVxdXY0PP/zQrm3hwoVGlSpV8jzOpEmTDEksLCwsLCwsd8By6tSpm2aMEn0FyMfHR2XLllViYqJde2Jiovz9/XPdxt/fv1DjJWn8+PGKjIy0rWdnZ+vChQvy9vaWxWK5hTO4c6WmpiooKEinTp1SpUqVnF2O6fH7KFn4fZQs/D5KluL8fRiGoT/++EOBgYE3HVuiA1C5cuXUokULRUVFqWfPnpL+DCdRUVEaMWJErtu0bt1aUVFRGj16tK1t06ZNat26dZ7HsVqtslqtdm1eXl63Wr4pVKpUif+glCD8PkoWfh8lC7+PkqW4fh+enp4FGleiA5AkRUZGauDAgWrZsqVatWqlefPm6fLlyxo8eLAkacCAAapatapmzJghSRo1apQ6dOig2bNnq1u3blq9erX279+vJUuWOPM0AABACVLiA1Dv3r11/vx5TZw4UQkJCWrWrJk2btxom+gcHx+vMmX+72G2Nm3a6MMPP9Srr76ql19+WbVr19batWvVqFEjZ50CAAAoYUp8AJKkESNG5HnLa9u2bTnaevXqpV69ehVzVeZmtVo1adKkHLcO4Rz8PkoWfh8lC7+PkqWk/D4shlGQZ8UAAADuHCX6RYgAAADFgQAEAABMhwAEAABMhwAEAABMhwCEApsxY4buueceeXh4qEqVKurZs6eOHj3q7LLw/82cOVMWi8XuJaC4/X7//Xf1799f3t7ecnd3V+PGjbV//35nl2VKWVlZmjBhgkJCQuTu7q5atWpp2rRpBfueKNyyHTt2qEePHgoMDJTFYtHatWvt+g3D0MSJExUQECB3d3d16dJFx44du231EYBQYNu3b9fw4cO1Z88ebdq0SZmZmXrggQd0+fJlZ5dmevv27dM777yjJk2aOLsUU7t48aLatm0rV1dXff311/rll180e/Zs3XXXXc4uzZRef/11LVq0SAsWLFBMTIxef/11zZo1S2+99ZazSzOFy5cvq2nTplq4cGGu/bNmzdL8+fO1ePFi7d27VxUqVFB4eLiuXr09X+rKY/AosvPnz6tKlSravn272rdv7+xyTCstLU3NmzfX22+/rddee03NmjXTvHnznF2WKY0bN07ff/+9vvvuO2eXAkndu3eXn5+f3n//fVvbo48+Knd3d61atcqJlZmPxWLRF198YftaK8MwFBgYqBdeeEFjxoyRJKWkpMjPz0/Lly9Xnz59ir0mrgChyFJSUiRJlStXdnIl5jZ8+HB169ZNXbp0cXYppvfll1+qZcuW6tWrl6pUqaK7775b7777rrPLMq02bdooKipKv/76qyTpv//9r3bu3KmIiAgnV4YTJ04oISHB7r9bnp6eCgsL0+7du29LDaXiTdAoebKzszV69Gi1bduWrxlxotWrV+vgwYPat2+fs0uBpN9++02LFi1SZGSkXn75Ze3bt08jR45UuXLlNHDgQGeXZzrjxo1Tamqq6tWrp7JlyyorK0vTp09Xv379nF2a6SUkJEiS7WutrvPz87P1FTcCEIpk+PDhOnTokHbu3OnsUkzr1KlTGjVqlDZt2iQ3NzdnlwP9+T8GLVu21D//+U9J0t13361Dhw5p8eLFBCAn+OSTT/TBBx/oww8/VMOGDRUdHa3Ro0crMDCQ3we4BYbCGzFihNavX6+tW7eqWrVqzi7HtA4cOKBz586pefPmcnFxkYuLi7Zv36758+fLxcVFWVlZzi7RdAICAtSgQQO7tvr16ys+Pt5JFZnb2LFjNW7cOPXp00eNGzfWk08+qeeff14zZsxwdmmm5+/vL0lKTEy0a09MTLT1FTcCEArMMAyNGDFCX3zxhbZs2aKQkBBnl2RqnTt31s8//6zo6Gjb0rJlS/Xr10/R0dEqW7ass0s0nbZt2+Z4NcSvv/6q4OBgJ1Vkbunp6SpTxv7PXNmyZZWdne2kinBdSEiI/P39FRUVZWtLTU3V3r171bp169tSA7fAUGDDhw/Xhx9+qHXr1snDw8N2n9bT01Pu7u5Ors58PDw8csy/qlChgry9vZmX5STPP/+82rRpo3/+8596/PHH9cMPP2jJkiVasmSJs0szpR49emj69OmqXr26GjZsqB9//FFz5szRU0895ezSTCEtLU3Hjx+3rZ84cULR0dGqXLmyqlevrtGjR+u1115T7dq1FRISogkTJigwMND2pFixM4ACkpTrsmzZMmeXhv+vQ4cOxqhRo5xdhqn95z//MRo1amRYrVajXr16xpIlS5xdkmmlpqYao0aNMqpXr264ubkZNWvWNF555RUjIyPD2aWZwtatW3P9mzFw4EDDMAwjOzvbmDBhguHn52dYrVajc+fOxtGjR29bfbwHCAAAmA5zgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgACgEGrUqKF58+YV+3EGDRp0+96IC5gQAQhAgeT1B3nbtm2yWCy6dOnSba8JAIqKAATA6a5du+bsEgCYDAEIgMN99tlnatiwoaxWq2rUqKHZs2fb9deoUUPTpk3TgAEDVKlSJQ0bNkyS9NJLL6lOnToqX768atasqQkTJigzM9O23X//+1916tRJHh4eqlSpklq0aKH9+/fnWcelS5f0zDPPyM/PT25ubmrUqJHWr19f4DrPnTunHj16yN3dXSEhIfrggw9yHGPOnDlq3LixKlSooKCgID333HNKS0uz9S9fvlxeXl765ptvVL9+fVWsWFFdu3bV2bNnbWOysrIUGRkpLy8veXt768UXX9SN31KUkZGhkSNHqkqVKnJzc1O7du20b9++/H4NAPJBAALgUAcOHNDjjz+uPn366Oeff9bkyZM1YcIELV++3G7cv/71LzVt2lQ//vijJkyYIOnPb7hfvny5fvnlF7355pt69913NXfuXNs2/fr1U7Vq1bRv3z4dOHBA48aNk6ura651ZGdnKyIiQt9//71WrVqlX375RTNnzlTZsmULXOegQYN06tQpbd26VWvWrNHbb7+tc+fO2R2nTJkymj9/vg4fPqwVK1Zoy5YtevHFF+3GpKen61//+pf+/e9/a8eOHYqPj9eYMWNs/bNnz9by5cu1dOlS7dy5UxcuXNAXX3xht48XX3xRn332mVasWKGDBw8qNDRU4eHhunDhQsF+MQDs3bavXQVQqg0cONAoW7asUaFCBbvFzc3NkGRcvHjRMAzDeOKJJ4z777/fbtuxY8caDRo0sK0HBwcbPXv2vOkx33jjDaNFixa2dQ8PD2P58uUFqvebb74xypQpk+e3S9+szqNHjxqSjB9++MHWHxMTY0gy5s6dm+dxP/30U8Pb29u2vmzZMkOScfz4cVvbwoULDT8/P9t6QECAMWvWLNt6ZmamUa1aNePhhx82DMMw0tLSDFdXV+ODDz6wjbl27ZoRGBhotx2AguMKEIAC69Spk6Kjo+2W9957z25MTEyM2rZta9fWtm1bHTt2TFlZWba2li1b5tj/xx9/rLZt28rf318VK1bUq6++qvj4eFt/ZGSkhgwZoi5dumjmzJmKjY3Ns9bo6GhVq1ZNderUybX/ZnXGxMTIxcVFLVq0sPXXq1dPXl5edtts3rxZnTt3VtWqVeXh4aEnn3xSycnJSk9Pt40pX768atWqZVsPCAiwXUlKSUnR2bNnFRYWZut3cXGx+3xiY2OVmZlpV6+rq6tatWqlmJiYPD8DAHkjAAEosAoVKig0NNRuqVq1apH39Ve7d+9Wv3799OCDD2r9+vX68ccf9corr9hNkJ48ebIOHz6sbt26acuWLWrQoEGOW0XXubu7F6muwoiLi1P37t3VpEkTffbZZzpw4IAWLlwoyX5i94236SwWS445PgBuLwIQAIeqX7++vv/+e7u277//XnXq1LHNv8nNrl27FBwcrFdeeUUtW7ZU7dq1dfLkyRzj6tSpo+eff17ffvut/v73v2vZsmW57q9JkyY6ffq0fv311yLVWa9ePf3vf//TgQMHbP1Hjx61e9z/wIEDys7O1uzZs3XvvfeqTp06OnPmTJ7nmBtPT08FBARo7969trYbj1urVi2VK1fOrt7MzEzt27dPDRo0KNTxAPzJxdkFALizvPDCC7rnnns0bdo09e7dW7t379aCBQv09ttv57td7dq1FR8fr9WrV+uee+7Rhg0b7K7uXLlyRWPHjtVjjz2mkJAQnT59Wvv27dOjjz6a6/46dOig9u3b69FHH9WcOXMUGhqqI0eOyGKxqGvXrjets27duurataueeeYZLVq0SC4uLho9erTdlaXQ0FBlZmbqrbfeUo8ePfT9999r8eLFhf7MRo0apZkzZ6p27dqqV6+e5syZYxe0KlSooGeffVZjx45V5cqVVb16dc2aNUvp6el6+umnC308AGISNICCGThwoG1S7l9t3brVbhK0YRjGmjVrjAYNGhiurq5G9erVjTfeeMNum+Dg4FwnEo8dO9bw9vY2KlasaPTu3duYO3eu4enpaRiGYWRkZBh9+vQxgoKCjHLlyhmBgYHGiBEjjCtXruRZc3JysjF48GDD29vbcHNzMxo1amSsX7++wHWePXvW6Natm2G1Wo3q1asbK1euzFH7nDlzjICAAMPd3d0IDw83Vq5cafd5LFu2zHYO133xxRfGX//zm5mZaYwaNcqoVKmS4eXlZURGRhoDBgyw+7yvXLli/OMf/zB8fHwMq9VqtG3b1m6CNoDCsRgGN6IBAIC5MAcIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYzv8DCloTMNzA1MIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Utilizando gráfico para verificar a distribuição de horas \n",
+    "df['CodingHours'].plot.hist(bins=10,edgecolor='black')\n",
+    "\n",
+    "plt.xlabel('Horas codando')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Distribuição de Horas')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Qual é o gênero que mais consome café enquanto coda, segundo os dados: \n",
+    "taxa_gender = df.groupby('Gender')['CoffeeCupsPerDay'].mean()\n",
+    "taxa_gender.plot.bar()\n",
+    "\n",
+    "plt.xlabel('Gênero')\n",
+    "plt.ylabel('Quantidade de horas')\n",
+    "plt.title('Taxa de consumo por gênero')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CoffeeType\n",
+       "Nescafe                     30\n",
+       "American Coffee             23\n",
+       "Turkish                     19\n",
+       "Espresso (Short Black)       8\n",
+       "Cappuccino                   7\n",
+       "Caffè latte                  5\n",
+       "Double Espresso (Doppio)     3\n",
+       "Americano                    2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Criando uma função para verificar o tipo de café mais consumido:\n",
+    "tipo_cafe = df['CoffeeType'].value_counts()\n",
+    "tipo_cafe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iIpIUywcRERFJiuWDiIiIJMXyQURERJJi+SAiIiJJsXwQERGRpFg+iIiISFIsH0RERCQplg8iIiKSFMsHERERSYrlg4iIiCTF8kFERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSLB9EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5ICIiIkmxfBAREZGkWD6IiIhIUiwfREREJCmWDyIiIpIUywcRERFJiuWDiIiIJMXyQURERJJi+SAiIiJJsXwQERGRpFg+iIiISFIsH0RERCQplg8iIiKSFMsHERERSYrlg4iIiCRV5PIRGxuLDh06wMXFBSqVClu2bNHYL4TAxIkTUbZsWVhaWsLPzw9XrlzRVl4iIiLSc0UuH48fP0bt2rURGhpa6P7Zs2djwYIFWLRoEY4cOYKSJUuiTZs2ePr06UeHJSIiIv1nUtQP8Pf3h7+/f6H7hBCYN28exo8fj44dOwIAVq9eDScnJ2zZsgU9evT4uLRERESk97Q65iMxMRGpqanw8/NTb7O1tcUnn3yCuLi4Qj8mNzcXWVlZGg8iIiJSLq2Wj9TUVACAk5OTxnYnJyf1vjfNmDEDtra26oebm5s2IxEREVExI/vdLmPHjkVmZqb6kZKSInckIiIi0iGtlg9nZ2cAQFpamsb2tLQ09b43mZubw8bGRuNBREREyqXV8uHh4QFnZ2dER0ert2VlZeHIkSPw9fXV5ksRERGRniry3S7Z2dm4evWq+nliYiJOnz4NBwcHlC9fHsOGDcOPP/6IypUrw8PDAxMmTICLiws6deqkzdxERESkp4pcPo4fP47mzZurnwcHBwMA+vTpg5UrV2LUqFF4/PgxvvvuO2RkZKBJkyaIjIyEhYWF9lITERGR3ipy+WjWrBmEEG/dr1KpMGXKFEyZMuWjghEREZEyyX63CxERERkWlg8iIiKSFMsHERERSYrlg4iIiCTF8kFERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSLB9EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5ICIiIkmxfBAREZGkWD6IiIhIUiZyByCiD1NhzF+yvXbSzPayvTYR6T+e+SAiIiJJsXwQERGRpFg+iIiISFIsH0RERCQplg8iIiKSFMsHERERSYrlg4iIiCTF8kFERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSXNuFiPQK17Qh0n8880FERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSLB9EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5ICIiIkmxfBAREZGkWD6IiIhIUiwfREREJCmWDyIiIpIUywcRERFJiuWDiIiIJMXyQURERJJi+SAiIiJJab18TJ48GSqVSuNRrVo1bb8MERER6SkTXXzSGjVqICoq6v+9iIlOXoaIiIj0kE5agYmJCZydnXXxqYmIiEjP6WTMx5UrV+Di4gJPT0/06tULycnJbz02NzcXWVlZGg8iIiJSLq2f+fjkk0+wcuVKVK1aFXfu3EFISAg+++wznDt3DtbW1gWOnzFjBkJCQrQdg4hIUSqM+Uu2106a2V621yZl0vqZD39/f3Tr1g21atVCmzZtsHPnTmRkZGDDhg2FHj927FhkZmaqHykpKdqORERERMWIzkeC2tnZoUqVKrh69Wqh+83NzWFubq7rGERERFRM6Hyej+zsbFy7dg1ly5bV9UsRERGRHtB6+RgxYgRiYmKQlJSEf/75B//zP/8DY2Nj9OzZU9svRURERHpI65ddbt68iZ49e+LBgwdwdHREkyZNcPjwYTg6Omr7pYiIiEgPab18hIeHa/tTEhERkYJwbRciIiKSFMsHERERSYrlg4iIiCTF8kFERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSLB9EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5ICIiIkmxfBAREZGkWD6IiIhIUiwfREREJCmWDyIiIpKUidwBiIiI3qbCmL9ke+2kme1le22l45kPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5ICIiIkmxfBAREZGkWD6IiIhIUiwfREREJCmWDyIiIpIUywcRERFJiuWDiIiIJMW1XYiIiIoZpa9pwzMfREREJCmWDyIiIpIUywcRERFJiuWDiIiIJMXyQURERJJi+SAiIiJJsXwQERGRpFg+iIiISFIsH0RERCQplg8iIiKSFMsHERERSYrlg4iIiCTF8kFERESSYvkgIiIiSbF8EBERkaRYPoiIiEhSLB9EREQkKZYPIiIikpTOykdoaCgqVKgACwsLfPLJJzh69KiuXoqIiIj0iE7Kx/r16xEcHIxJkybh5MmTqF27Ntq0aYO7d+/q4uWIiIhIj+ikfMyZMwcDBgxAv3794OXlhUWLFqFEiRJYvny5Ll6OiIiI9IiJtj/hs2fPcOLECYwdO1a9zcjICH5+foiLiytwfG5uLnJzc9XPMzMzAQBZWVkfnCE/N+eDP/ZjfUzuj8X3LT2+b+nxfUuP71t6+vi+X32cEOLfDxZaduvWLQFA/PPPPxrbR44cKRo2bFjg+EmTJgkAfPDBBx988MGHAh4pKSn/2hW0fuajqMaOHYvg4GD18/z8fKSnp6NUqVJQqVSSZsnKyoKbmxtSUlJgY2Mj6WvLie+b79sQ8H3zfRsCOd+3EAKPHj2Ci4vLvx6r9fJRunRpGBsbIy0tTWN7WloanJ2dCxxvbm4Oc3NzjW12dnbajlUkNjY2BvXN+grft2Hh+zYsfN+GRa73bWtr+17HaX3AqZmZGerVq4fo6Gj1tvz8fERHR8PX11fbL0dERER6RieXXYKDg9GnTx/Ur18fDRs2xLx58/D48WP069dPFy9HREREekQn5ePLL7/EvXv3MHHiRKSmpqJOnTqIjIyEk5OTLl5Oa8zNzTFp0qQCl4GUju+b79sQ8H3zfRsCfXnfKiHe554YIiIiIu3g2i5EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJh4J4+fSp3BCIiMjCyT69O0svPz8e0adOwaNEipKWl4fLly/D09MSECRNQoUIFfPPNN3JH1Jno6GhER0fj7t27yM/P19hnCKsu5+bmFvtb8OjjZWRkICIiAteuXcPIkSPh4OCAkydPwsnJCa6urnLH04nExEQcOHAAN27cQE5ODhwdHeHj4wNfX19YWFjIHY/ewPLxhqdPnyr+G/XHH3/EqlWrMHv2bAwYMEC93dvbG/PmzVNs+QgJCcGUKVNQv359lC1bVvK1g+Swa9cuhIeH48CBA0hJSUF+fj5KliwJHx8ftG7dGv369XuvdRj03YkTJ3Dx4kUAgJeXF+rWrStzIt05c+YM/Pz8YGtri6SkJAwYMAAODg7YtGkTkpOTsXr1arkjatUff/yB+fPn4/jx43BycoKLiwssLS2Rnp6Oa9euwcLCAr169cLo0aPh7u4ud1yduHbtGubNm6fxPR4UFISKFSvKnOwdtLOWrX7Ly8sTU6ZMES4uLsLY2Fhcu3ZNCCHE+PHjxdKlS2VOp30VK1YUUVFRQgghrKys1O/34sWLws7OTs5oOuXs7CxWr14tdwxJbNq0SVSuXFk4OzuL/v37i0WLFolt27aJv//+W6xfv15MmDBBNGvWTJibm4vvv/9e3L17V+7IOpGWliaaN28uVCqVsLe3F/b29kKlUokWLVoo9j23bNlSjBw5Ugih+f/70KFDwt3dXcZk2lenTh3RsGFDERoaKpKTkwvsf/r0qdi3b5/4/vvvRenSpcWGDRtkSKlbkZGRwszMTDRs2FAMHz5cDB8+XDRs2FCYm5uLPXv2yB3vrVg+hBAhISHC09NT/P7778LS0lL9nzU8PFw0atRI5nTaZ2FhIZKSkoQQmj+czp8/L0qWLClnNJ1ycHAQV69elTuGJBo1aiR27Ngh8vLy3nnczZs3xejRo8WcOXMkSiat7t27i/r164sLFy6ot50/f17Ur19f9OjRQ8ZkumNjY6P+Pn/9/3dSUpIwNzeXM5rWRUZGvvex9+/fF8ePH9dhGnnUqVNHjB49usD20aNHCx8fHxkSvR+WD2F4ZwLq1q0r1qxZI4TQfL8hISGiSZMmckbTqVGjRokpU6bIHYMkZGNjI44ePVpg+5EjR4Stra30gSTg6OgoTp48KYTQ/P+9Z88eUa5cOTmjkQ6Ym5uLy5cvF9iekJBQrMsmx3wAuHXrFipVqlRge35+Pp4/fy5DIt2aOHEi+vTpg1u3biE/Px+bNm1CQkICVq9ejR07dsgdT6uCg4PV/87Pz8dvv/2GqKgo1KpVC6amphrHzpkzR+p4kjh37hy8vb0L3bdlyxZ06tRJ2kASys/PL/B1BgBTU9MCA46VIiAgAFOmTMGGDRsAACqVCsnJyRg9ejS6dOkiczrdysvLw5YtWzTGPnTs2BHGxsYyJ9MdR0dHnD59GpUrV9bYfvr0aZQpU0amVP+O5QMvv0EPHDhQYDBSREQEfHx8ZEqlOx07dsT27dsxZcoUlCxZEhMnTkTdunWxfft2tGrVSu54WnXq1CmN53Xq1AHw8hfy65Q8+LRNmzY4ePAgPDw8NLZv3LgRvXv3xuPHj2VKpnstWrRAUFAQ1q1bpx5Ye+vWLQwfPhwtW7aUOZ1u/Pzzz+jatSvKlCmDJ0+e4PPPP0dqaip8fX0xbdo0uePpzNWrV9G+fXvcvHkTVatWBQDMmDEDbm5u+Ouvv4r34MuPMGDAAHz33Xe4fv06Pv30UwDAoUOHMGvWLI0/vooduU+9FAdbtmwRtra2YubMmaJEiRLiv//9r/j222+FmZlZsR6wQ/Q+Jk6cKDw9PcWdO3fU28LDw0WJEiUUOQDvdcnJyaJOnTrC1NRUeHp6Ck9PT2Fqaip8fHxESkqK3PF06uDBgyI0NFTMmjVL/P3333LH0Tl/f3/Rtm1b8eDBA/W2+/fvi7Zt24p27drJmEy38vPzxZw5c4Srq6tQqVRCpVIJV1dXMW/ePJGfny93vLfiqrb/vwMHDmDKlCmIj49HdnY26tati4kTJ6J169ZyR9OJV/MAXL9+HSNGjDCIeQDelJWVhb1796JatWqoVq2a3HF0KjAwEPv27UNsbCwiIyPx7bffYs2aNYo/DQ8AQghERUXh0qVLAIDq1avDz89P5lS6s3r1anz55ZcF5nN59uwZwsPD0bt3b5mS6VbJkiVx+PBh1KxZU2N7fHw8GjdujOzsbJmSSefRo0cAAGtra5mTvAeZy49s5s+fL548eSKEEOLGjRvFuiFqW3x8vHB0dBSVKlUSJiYm6gFp48aNE19//bXM6XSnW7du4pdffhFCCJGTkyMqV64sTE1NhYmJiYiIiJA5ne599dVXonLlyqJEiRJiy5YtcschHTEyMhJpaWkFtt+/f18YGRnJkEga9vb24tChQwW2Hzx4UNjb28uQiN7FYMd8BAcHo0ePHrCwsICHhwfu3LlTrAfnaFNwcDD69u2L2bNnazTkdu3a4auvvpIxmW7FxsZi3LhxAIDNmzdDCIGMjAysWrUKP/74o6LOAmzbtq3Ats6dO+PAgQPo2bMnVCqV+piAgACp40nK0Ga1FUIUOobp5s2bsLW1lSGRNL744gt89913WLZsGRo2bAgAOHLkCAYOHKjo7/G0tDSMGDFC/T0u3riYkZeXJ1OydzPY8uHi4oKNGzeiXbt2EELg5s2bb13npHz58hKn061jx45h8eLFBba7uroiNTVVhkTSyMzMhIODAwAgMjISXbp0QYkSJdC+fXuMHDlS5nTa9a47WJYvX67+patSqYrtDydtMKRZbX18fKBSqaBSqdCyZUuYmPy/H+95eXlITExE27ZtZUyoWwsWLECfPn3g6+urvsPpxYsXCAgIwPz582VOpzt9+/ZFcnIyJkyYoFff4wZbPsaPH4/AwEAMGTIEKpUKDRo0KHDMq78glPbD2dzcHFlZWQW2X758GY6OjjIkkoabmxvi4uLg4OCAyMhIhIeHAwAePnyouCn1lXobaVEtWrQIK1euxNdffy13FJ17VThPnz6NNm3awMrKSr3PzMwMFSpUUNTZvTfZ2dlh69atuHLlisb4nsKmUVCSgwcP4sCBA+o7+fSFwZaP7777Dj179sSNGzdQq1YtREVFoVSpUnLHkoShzgMwbNgw9OrVC1ZWVnB3d0ezZs0AvLwc8+YgNVKGZ8+eqW8/VLpJkyYBACpUqIAePXoY7AKClStXLjDnhZK5ubkVuNSiD3i3C4BVq1YZ1H/WzMxMdO3aFcePH8ejR4/g4uKingdg586dKFmypNwRdeb48eNISUlBq1at1H8Z/vXXX7Czs0Pjxo1lTqcbQ4cORaVKlTB06FCN7QsXLsTVq1cxb948eYJJYPTo0bCyssKECRPkjiIZT09PHDt2rMAfUxkZGahbty6uX78uUzLtCw4OxtSpU1GyZMl/ndNCqZMI7tmzBz///DMWL16MChUqyB3nvbF8GLBDhw5p3Fqs5NsPDZmrqyu2bduGevXqaWw/efIkAgICcPPmTZmS6V5QUBBWr16NWrVqGcystkZGRkhNTS0wgD4tLQ3ly5dHbm6uTMm0r3nz5ti8eTPs7OzQvHnztx6nUqmwd+9eCZNJx97eHjk5OXjx4gVKlChR4Hs8PT1dpmTvZrCXXV6Xl5eHuXPnYsOGDUhOTsazZ8809hfXL15RODg44PLlyyhdujT69++P+fPno3Hjxor9a/8V/mUEPHjwoNC7HGxsbHD//n0ZEknnzJkzBjOr7et3OO3evVvja56Xl4fo6Gi9+sv4fezbt6/QfxsSfT1zyfKBlyPily5dih9++AHjx4/HuHHjkJSUhC1btmDixIlyx9OKZ8+eISsrC6VLl8aqVaswa9Ys/ZiI5iOdOnVKvT7Pm1OtG4pKlSohMjISQ4YM0di+a9cueHp6ypRKGob0C+nVgFOVSoU+ffpo7DM1NUWFChXw888/y5BMeikpKQBejodQuje/1vqCl10AVKxYEQsWLED79u1hbW2N06dPq7cdPnwYa9eulTviR2vVqhXS0tJQr149rFq1Cl9++SUsLS0LPVaJcx8YsuXLl2PIkCEYOXIkWrRoAeDl3Bc///wz5s2bhwEDBsickLTJw8MDx44dQ+nSpeWOIqkXL14gJCQECxYsUM9mamVlhcDAQEyaNKnQBQaV5unTpwXO3NvY2MiU5t145gNAamqq+m4HKysrZGZmAng5aY1SBqr9/vvvmDt3Lq5duwbg5aDTt81rolT79u1763Xh0NBQDB48WOJE0ujfvz9yc3Mxbdo0TJ06FcDLOyLCwsIUOdV2586dsXLlStjY2KBz587vPHbTpk0SpZJOYmKi3BFkERgYiE2bNmH27Nnw9fUFAMTFxWHy5Ml48OABwsLCZE6oG48fP8bo0aOxYcMGPHjwoMD+4jpVBMsHgHLlyuHOnTsoX748KlasiD179qBu3bo4duyYYu6AcXJywsyZMwG8/MtozZo1BnNr8SudO3dGVFRUgYGX8+fPx4QJExRbPgBg0KBBGDRoEO7duwdLS0uNOSCUxtbWVj2eQ8kzer7L48ePERMTU+gYtjfvelKKtWvXIjw8HP7+/upttWrVgpubG3r27KnY8jFq1Cjs27cPYWFh+PrrrxEaGopbt25h8eLF6p/5xZI8s7oXL6NHjxbTpk0TQrxc7dPExERUqlRJmJmZidGjR8ucTjvs7e3FvXv3hBBC9OvXT2RlZcmcSHpLliwRjo6O4uLFi+ptP/30k7CxsRGxsbEyJiPSnpMnTwpnZ2dhY2MjjI2NhaOjo1CpVKJkyZLCw8ND7ng64+joKC5cuFBg+4ULF0Tp0qVlSCQNNzc3sW/fPiGEENbW1uLKlStCCCFWr14t/P39ZUz2bhzzUYjDhw/jn3/+QeXKldGhQwe542iFlZUVzpw5A09PTxgbGyM1NVXRs5m+zezZs7FgwQIcPHgQ69evx/Tp07Fz507F3/UTERHx1ru5Tp48KVMq3UtMTMSLFy8KTDp15coV9SBMpWnWrBmqVKmCRYsWwdbWFvHx8TA1NcV//vMfBAUF/eulKH01ZcoUXLp0CStWrFCfsc7NzcU333yDypUrqydhUxorKytcuHAB5cuXR7ly5bBp0yY0bNgQiYmJqFmzZrFdzZeXXQrRqFEjNGrUSO4YWuXr64tOnTqhXr16EEJg6NChBjngdNSoUXjw4AHq16+PvLw87N69W3Ff6zctWLAA48aNQ9++fbF161b069cP165dw7FjxxR9qQl4ue5F//79C5SPI0eOYOnSpdi/f788wXTo9OnTWLx4MYyMjGBsbIzc3Fx4enpi9uzZ6NOnj2LLx6lTpxAdHY1y5cqhdu3aAID4+Hg8e/YMLVu21HjfShrr4+npicTERJQvXx7VqlXDhg0b0LBhQ2zfvh12dnZyx3srlg8AM2bMgJOTE/r376+xffny5bh37x5Gjx4tUzLteX3AqUqlMpgBpwsWLCiwzdXVFSVKlEDTpk1x9OhRHD16FIByr4X/+uuv+O2339CzZ0+sXLkSo0aNgqenJyZOnKiIOWze5dSpU4We1WrUqFGBW4+VwtTUFEZGRgCAMmXKIDk5GdWrV4etra36FlQlsrOzK7A8hCHcatuvXz/Ex8fj888/x5gxY9ChQwcsXLgQz58/L9ZzF/GyC16O/F+7dm2BNSCOHDmCHj16KG70uIeHB44fP24QA049PDze6ziVSqWoaadfV6JECVy8eBHu7u4oU6YM/v77b9SuXRtXrlxBo0aNCh0hrxS2trbYv38/fHx8NLafOHECzZo1w6NHj2RKpjutW7dG37598dVXX2HAgAE4c+YMhg4dijVr1uDhw4c4cuSI3BFJh27cuIETJ06gUqVKqFWrltxx3opnPvDyVtuyZcsW2O7o6Ig7d+7IkEi3lFam3sWQ3uvbODs7Iz09He7u7ihfvjwOHz6M2rVrIzExUS8XpCqKpk2bYsaMGVi3bh2MjY0BvLz1cMaMGWjSpInM6XRj+vTp6lI1bdo09O7dG4MGDULlypUVfUn1lbt37yIhIQEAULVq1QLTzCudu7s73N3d5Y7xr1g+8PLU3KFDhwr8lXzo0CG4uLjIlEq3YmJi8NNPP+HixYsAAC8vL4wcORKfffaZzMl04/nz56hWrRp27NiB6tWryx1HUi1atMC2bdvg4+ODfv36Yfjw4YiIiMDx48cVe/3/lVmzZqFp06aoWrWq+nv7wIEDyMrKUuxaH/Xr11f/u0yZMoiMjJQxjXSysrIwePBghIeHq+e2MDY2xpdffonQ0FDF3natrwtHGskdoDgYMGAAhg0bhhUrVuDGjRu4ceMGli9fjuHDhyty9sfff/8dfn5+KFGiBIYOHaoefNqyZUtFzOZaGFNTU4MY41KY3377DePGjQMADB48GMuXL0f16tUxZcoUxc598IqXlxfOnDmD7t274+7du3j06BF69+6NS5cuwdvbW+54pEUDBgzAkSNHsGPHDmRkZCAjIwM7duzA8ePH8f3338sdT2c2btxY6LimTz/9FBERETIkej8c8wFACIExY8ZgwYIF6tsQLSwsMHr0aMWs7fK66tWr47vvvsPw4cM1ts+ZMwdLlixRnw1RmunTp+Py5ctYunQpTEx40o+Uw8fH570XylPqrdUlS5bE7t27C1xOO3DgANq2bYvHjx/LlEy3LCwscO7cOVSqVElj+9WrV+Ht7V1s/+jiT2C8HGw4a9YsTJgwARcvXoSlpSUqV66smNlN33T9+vVC5y8JCAjA//3f/8mQSBrHjh1DdHQ09uzZg5o1a6JkyZIa+5V0+92ZM2fe+9jiPCjtY61YsQJWVlbo1q2bxvY///wTOTk5erso15teLSpnyEqVKlXopRVbW1vY29vLkEga+rpwJMvHa6ysrNCgQQNkZWVh165dqFq1qiLHB7i5uSE6OrpAU46KilL0rWmF3YqnVHXq1IFKpfrXAaUqlarYrv2gDTNmzMDixYsLbC9Tpgy+++47xZQPpU6gVRTjx49HcHAw1qxZA2dnZwAvbyYYOXKkYtboKkxwcDCGDBmCe/fuFbpwZHHFyy4AunfvjqZNm2LIkCF48uQJateujaSkJAghEB4errhfWGFhYRg2bBj69++vvr340KFDWLlyJebPn6/o66OG4saNG+99rD6MjP9QFhYWuHTpUoGZTJOSklC9enU8efJEnmCkdT4+Prh69Spyc3NRvnx5AEBycjLMzc0LTDKntEtPYWFhmDZtGm7fvg3g5fQRkydPLtYLR/LMB4DY2Fj1gLzNmzdDCIGMjAysWrUKP/74o+LKx6BBg+Ds7Iyff/4ZGzZsAPByHMj69evRsWNHmdORNii5UBRFmTJlcObMmQLlIz4+3iDmuTEkhnzpSR8XjuSZDwCWlpa4fPky3Nzc0Lt3b7i4uGDmzJlITk6Gl5dXsZ0bn4rGw8PjnYPylDrJ2IMHD9S/aFNSUrBkyRI8efIEAQEBir21+pXRo0dj/fr1WLFiBZo2bQrg5W3m/fv3R9euXfHTTz/JnJDIMPHMB16OgYiLi4ODgwMiIyMRHh4OAHj48CEsLCxkTqc9Dx8+xO+//44+ffrAxsZGY19mZiZWr15d6D6lGDZsmMbz58+f49SpU4iMjMTIkSPlCaVDZ8+eRYcOHZCSkoLKlSsjPDxcPerfyMgIc+fORUREhKL/Ypw6dSqSkpLQsmVL9R1O+fn56N27N6ZPny5zOtKFEydOqO/Yq1GjRoHZbZWgbt26iI6Ohr29/b/e6VRcLzGxfODlL6VevXrBysoK5cuXR7NmzQC8vBxTs2ZNecNp0cKFC3HmzBkEBgYW2Gdra6uefOnVJSilCQoKKnR7aGgojh8/LnEa3Rs1ahRq1qyJP/74A2vWrMEXX3yB9u3bY8mSJQCAwMBAzJw5U9Hlw8zMDOvXr8fUqVMRHx8PS0tL1KxZ0yAuSz158gSJiYnw8vKSO4ok7t69ix49emD//v3qBdUyMjLQvHlzhIeHK2oV744dO6rvxtTb/7+ChBBCHDt2TGzatEk8evRIvW3Hjh3i4MGDMqbSrtq1a4uoqKi37o+KihJ16tSRMFHxcO3aNWFtbS13DK0rVaqUiI+PF0II8ejRI6FSqcTx48fV+y9evChsbW1lSke68ujRI9GrVy9hZGQkjI2N1dsHDBggZsyYIWMy3erevbuoX7++uHDhgnrb+fPnRf369UWPHj1kTKY7L168EDExMeLhw4dyRykyjvl4zbNnz5CYmIiKFSsqchIqa2trnD9/Xj0S/E3Jycnw9vZGVlaWxMnkNXv2bPz6669ISkqSO4pWGRkZITU1Vb22hbW1NeLj49X3/qelpcHFxUXRt9q+uVL1m5S41smQIUNw7NgxzJ07F02aNEF+fj4AYMuWLQgJCcGpU6dkTqgbtra2iIqKQoMGDTS2Hz16FK1bt0ZGRoY8wXTMwsICFy9efO9FNIsL5f2G/QA5OTkIDAzEqlWrAACXL1+Gp6cnAgMD4erqijFjxsicUDuMjY1x+/btt5aP27dvq5fiVpIpU6bghx9+QJMmTTSujQohkJqainv37uHXX3+VMaHuvHkt+H1nwVSKhw8fajx//vw5zp07h4yMDPWcCEqxa9cu+Pv7Y+vWrYiIiMAnn3yi8fX29vbGtWvXZEyoW/n5+TA1NS2w3dTUVF3AlMjb2xvXr19n+dBHY8eORXx8PPbv34+2bduqt/v5+WHy5MmKKR8+Pj7YsmULGjVqVOj+zZs3K3JwVkhICAYOHIiOHTtq/DA2MjKCo6MjmjVrhmrVqsmYUHf69u2rvjb89OlTDBw4UD2za25urpzRJLF58+YC2/Lz8zFo0CBUrFhRhkTaJ4TA+PHjcfDgQfj7++PBgweFruSanZ2t6PLZokULBAUFYd26deoFQW/duoXhw4ejZcuWMqfTnR9//BEjRozA1KlTUa9evQIzNxfXGwh42QUv50RYv349GjVqpHFq+urVq6hbt65iLkNs3LgRPXr0wNy5czFo0CCNJcZ//fVX/PDDD1i7di26du0qc1LtevPyg6Ho16/fex23YsUKHScpfhISEtCsWTPcuXNH7igf7b///S/i4uKwbt06mJubo2nTpvjyyy8xePBgGBkZqf/q//7773Hjxg3FrnKbkpKCgIAAnD9/Xj1Tc0pKCry9vbFt2zaUK1dO5oS68frZ6jfP7BbnGYx55gPAvXv3Cv3F9PjxY0X9pdClSxeMGjUKQ4cOxbhx49TX/q9fv47s7GyMHDlSccXjFSV9Hd+XIZaK93Xt2jW8ePFC7hha4e/vjxEjRqi/x6dPn4527drh4sWLUKlUCAkJwd69e3HixAnExMTInFZ33NzccPLkSURFReHSpUsAXk6e6OfnJ3My3dq3b5/cET4Iz3wAaNq0Kbp164bAwEBYW1vjzJkz8PDwQGBgIK5cuaK4vxSOHj2KP/74A1evXoUQAlWqVMFXX32Fhg0byh1NJ4yMjGBra/uvBSQ9PV2iRCSV4OBgjedCCNy5cwd//fUX+vTpg4ULF8qUTLeuXbuGmTNnIj4+HtnZ2ahbty5Gjx6tqKkDSL+xfADqa6X/+c9/sHLlSnz//fe4cOEC/vnnH8TExKBevXpyR6SPYGRkhHnz5hW64uXrlLLIGP0/zZs313j+apxPixYt0L9/f8Xc1RYcHIypU6eiZMmSiI2NxaeffqqY9/Y+8vPzsXLlSmzatAlJSUlQqVTw8PBA165d8fXXXyv+zOeBAwewePFiXL9+HX/++SdcXV2xZs0aeHh4oEmTJnLHKxTLx/+Pfykol6GO+SDDYWpqips3b8LJyQnGxsa4c+eOwXy/CyHQoUMH7Ny5E7Vr10a1atUghMDFixdx9uxZBAQEYMuWLXLH1JmNGzfi66+/Rq9evbBmzRpcuHABnp6eWLhwIXbu3ImdO3fKHbFQLB+keIb2w5gKunv3LhISEgAAVatWVdz3QuXKldG9e3e0bt0azZs3x+bNm2Fvb1/osa/WuFGKFStWICgoCFu3bi1wpmvv3r3o1KkTFi5cWKxXeP0YPj4+GD58OHr37q1xw8SpU6fg7++P1NRUuSMWTuJJzYoVlUoljIyM3vl4fYZA0k8qlUqkpaXJHUM2MTEx4vnz5wW2P3/+XMTExMiQSDqZmZniP//5jzA2NhYqlUqoVCphYmIievXqJTIyMuSOpzWbN28WTk5O6p9pr97rmw8jIyO5o2pdq1at3jlz67Rp00Tr1q0lTCQtS0tLkZiYKIQQwsrKSly7dk0I8XLmZnNzcxmTvZvhXBQsRGFzALwSFxeHBQsWKHpyGkNh6F/D5s2bF3rmJzMzE82bNy+2t+Jpw4ABA3Dq1Cn89ddf8PX1BfDy/3ZQUBC+//579SKS+q5Tp07o1KkTsrOzYWNjg4SEBMWd3XmbM2fOYPbs2W/d7+/vjwULFkiYSFrOzs64evUqKlSooLH94MGD6jsaiyW5209xc+nSJdGpUydhbGwsevfuLZKSkuSORPRRVCqVuHv3boHtCQkJilzT5nUlSpQQBw4cKLA9NjZWlChRQoZEujF8+HCRnZ0thBBi//79hZ7pUipTU1Nx+/btt+6/deuWMDMzkzCRtKZPny68vLzE4cOHhbW1tThw4ID4/fffhaOjo1iwYIHc8d7KoM98vO727duYNGkSVq1ahTZt2uD06dPw9vaWO5ZOpKWlYcSIEYiOjsbdu3ch3hj2o+S/hA1J586dAbyc4+T1mU6Bl1/jM2fO4NNPP5UrniRKlSpV6F1Otra2bx0ToY9++eUXjB49GiVLlkSLFi0MaoxTXl7eO+/sMTY2VsycLoUZM2YM8vPz0bJlS+Tk5KBp06YwNzfHiBEjCl3BvLgw+PKRmZmJ6dOn45dffkGdOnUQHR2Nzz77TO5YOtW3b18kJydjwoQJKFu2rOJvQzNUr37pCiFgbW0NS0tL9T4zMzM0atQIAwYMkCueJMaPH4/g4GCsWbMGzs7OAIDU1FSMHDkSEyZMkDmd9lSoUAELFixA69atIYRAXFycwQw4FUIUKNevU/oyAiqVCuPGjcPIkSNx9epVZGdnw8vLC1ZWVnJHeyeDvttl9uzZmDVrFpydnTF9+nR07NhR7kiSsLa2xoEDB1CnTh25o5COCSHQv39//PLLL8X+h5Eu+Pj44OrVq8jNzVUvqJicnAxzc3NUrlxZ49iTJ0/KEVErtmzZgoEDB+Lu3btQqVQFzma+Upyn2/5QXEZAPxl0+TAyMoKlpSX8/PzU65wUZtOmTRKm0j0vLy/88ccfilxEjjTl5+fDwsIC58+fL/DL1hCEhIS897GTJk3SYRJpvM+A03+bbI/0y9OnT/HLL79g3759uHv3boEB9sW1VBv0ZZfevXsb5CWHefPmYcyYMVi8eHGBEdKkLEZGRqhcuTIePHhgkOVDCYWiKKysrLBv3z54eHgY1Aynhuybb77Bnj170LVrVzRs2FBvfqcZ9JkPQ2Vvb4+cnBy8ePECJUqUgKmpqcZ+rnGiLNu3b8fs2bMRFham2EHU/+b48eO4ePEigJdn/gxlyYSnT5/i2bNnGtuK6xLrH2LgwIEYP378e61Yu379erx48QK9evWSIJl0bG1tsXPnTjRu3FjuKEXCamyA5s2bJ3cEklDv3r2Rk5OD2rVrw8zMTGPgKaDssnnz5k307NkThw4dgp2dHQAgIyMDn376KcLDwxW5zHpOTg5GjRqFDRs24MGDBwX2K2nMh6OjI2rUqIHGjRujQ4cOqF+/PlxcXGBhYYGHDx/iwoULOHjwIMLDw+Hi4oLffvtN7sha5+rqCmtra7ljFBnPfBAp3KpVq965X8kL6rVt2xYZGRlYtWoVqlatCgBISEhAv379YGNjo7gVqwFg8ODB2LdvH6ZOnYqvv/4aoaGhuHXrFhYvXoyZM2cq7i//tLQ0LF26FOHh4bhw4YLGPmtra/j5+eHbb79F27ZtZUqoW7t27cKCBQuwaNEiuLu7yx3nvbF8GDiln5Ylw2ZpaYl//vmnwODqEydO4LPPPkNOTo5MyXSnfPnyWL16NZo1awYbGxucPHkSlSpVwpo1a7Bu3bpiu9CYNjx8+BDJycl48uQJSpcujYoVK+rNGIgPde/ePXTv3h2xsbF6dRmdl10M0OPHjzF69GiDOC1LL+Xl5WHLli3qcQ81atRAQEDAO+/yUgI3Nzc8f/68wPa8vDy4uLjIkEj30tPT1dNq29jYqH/5NGnSBIMGDZIzms7Z29sravK499GzZ0/cunUL06dPh5OTk96ULZYPAzRq1Cjs27cPYWFhhZ6WJWW5evUq2rVrh1u3bqkvPcyYMQNubm7466+/ULFiRZkT6s5///tfBAYGIjQ0FPXr1wfwcvBpUFAQfvrpJ5nT6YanpycSExNRvnx5VKtWDRs2bEDDhg2xfft29bgXUo5//vkHcXFxqF27ttxRioSXXQyQIZ+WNUTt2rWDEAJ//PEHHBwcAAAPHjzAf/7zHxgZGeGvv/6SOaHuvH5n16tbT1/9u2TJkhrHFtfT00U1d+5cGBsbY+jQoYiKikKHDh0ghMDz588xZ84cBAUFyR2RtKhu3br49ddf0ahRI7mjFAnLhwGysrLChQsXUL58eZQrVw6bNm1Cw4YNkZiYiJo1ayI7O1vuiKRFJUuWxOHDh1GzZk2N7fHx8WjcuLGiv97/Ntj2dUodeHvjxg2cOHEClSpVQq1ateSOQ1q2Z88ehISEYNq0aahZs2aBMR/FdQwfL7sYIJ6WNSzm5uZ49OhRge3Z2dkwMzOTIZF0lFooisLd3V2v7oKgonl1F0/Lli01tgshivV0+iwfBqhfv36Ij4/H559/jjFjxqBDhw5YuHCh+rQsKcsXX3yB7777DsuWLUPDhg0BAEeOHMHAgQMREBAgczrpKP3Orr1792LIkCE4fPhwgfeVmZmJTz/9FIsWLVL8wpn37t1DQkICAKBq1apwdHSUOZFu7du37637zp49K2GSIhJk8JKSksTGjRtFfHy83FFIBx4+fCgCAgKESqUSZmZmwszMTBgZGYlOnTqJjIwMuePpVHZ2thg8eLBwdHQURkZGBR5K0qFDBzFnzpy37p8/f77o1KmThImklZ2dLfr16ydMTEyESqUSKpVKmJiYiP79+4vHjx/LHU8yWVlZYvHixaJBgwbF+nvcSO7yQ/Jzd3dH586deT1Yoezs7LB161YkJCQgIiICERERSEhIwObNmxW/yNioUaOwd+9ehIWFwdzcHEuXLkVISAhcXFywevVqueNpVXx8/Dsn0mrdujVOnDghYSJpBQcHIyYmBtu2bUNGRgYyMjKwdetWxMTE4IcffpA7ns7FxsaiT58+KFu2LH766Se0aNEChw8fljvW28ndfkh6gYGBYv78+QW2//LLLyIoKEj6QEQ64ubmJvbt2yeEEMLa2lpcuXJFCCHE6tWrhb+/v4zJtM/c3Fz9/gpz5coVYWFhIWEiaZUqVUr9tX7d3r17RenSpaUPJIE7d+6IGTNmiEqVKokyZcqIIUOGCBMTE3H+/Hm5o/0rnvkwQBs3bix0EaJPP/0UERERMiQiXcrLy8OyZcvw1Vdfwc/PDy1atNB4KNm7JtyKjY2VM5rWubq64ty5c2/df+bMGZQtW1bCRNLKycmBk5NTge1lypRR5Ey2HTp0QNWqVXHmzBnMmzcPt2/fxi+//CJ3rPfG8mGAHjx4UOjpdhsbG9y/f1+GRKRLQUFBCAoKQl5eHry9vVG7dm2Nh5K9urMLgPrOLgCKvLOrXbt2mDBhAp4+fVpg35MnTzBp0iR88cUXMiSThq+vLyZNmqTx/p88eYKQkBD4+vrKmEw3du3ahW+++QYhISFo37693s1WzHk+DJC3tzcGDhyIIUOGaGz/5ZdfEBYWVmBxJtJvpUuXxurVq9GuXTu5o0jubRNuPXv2DHPnzlXUhFtpaWmoW7cujI2NMWTIEPVstpcuXUJoaCjy8vJw8uTJQs8OKMG5c+fQpk0b5Obmqkt1fHw8LCwssHv3btSoUUPmhNp1+PBhLFu2DOvXr0f16tXx9ddfo0ePHihbtizi4+Ph5eUld8R3YvkwQMuXL8eQIUMwcuRI9Wn36Oho/Pzzz5g3bx4GDBggc0LSJhcXF+zfvx9VqlSRO4rsXk24Vbly5QKTrinBjRs3MGjQIOzevRuvfrSrVCq0adMGoaGh8PDwkDmhbuXk5OCPP/7ApUuXAADVq1dHr169YGlpKXMy3Xn8+DHWr1+P5cuX4+jRo8jLy8OcOXPQv39/WFtbyx3vrVg+DFRYWBimTZuG27dvAwAqVKiAyZMno3fv3jInI237+eefcf36dSxcuFBvFp36WIY+58XDhw9x9epVCCFQuXJlg1tszVAlJCRg2bJlWLNmDTIyMtCqVSts27ZN7liFYvkwcPfu3YOlpSWsrKzkjkJa1LlzZ43ne/fuhYODA2rUqFFg+uVNmzZJGU0SAQEBaN68OYYPH17o/gULFmDfvn3YvHmzxMlIV1atWoXSpUujffv2AF7eZv3bb7/By8sL69atM6hZXvPy8rB9+3YsX76c5YOIpNOvX7/3PnbFihU6TCIPd3d3REZGonr16oXuv3TpElq3bo3k5GSJk5GuVK1aFWFhYWjRogXi4uLQsmVLzJs3Dzt27ICJiYkiS7Y+4/TqBqJu3bqIjo6Gvb09fHx83nn6/eTJkxImI11QYqEoirS0tAJneF5nYmKCe/fuSZiIdC0lJQWVKlUCAGzZsgVdu3bFd999h8aNG6NZs2byhqMCWD4MRMeOHWFubg4A6NSpk7xhSFYxMTF4/PgxfH19FTsW4NWcF69+Gb1J6XNeGCIrKys8ePAA5cuXx549exAcHAwAsLCwwJMnT2ROR29i+TAQkyZNAvDyWmDz5s1Rq1Ytxc1zQJpmzZqF7OxsTJ06FcDLVS79/f2xZ88eAC8nX4qOjlbcLYjA/5vzom3btrCwsNDYZwhzXhiiVq1a4dtvv4WPjw8uX76svrX8/PnzqFChgrzhqABOMmZgjI2N0bp1azx8+FDuKKRj69evh7e3t/p5REQEYmNjceDAAdy/fx/169dHSEiIjAl1Z/z48UhPT0eVKlUwe/ZsbN26FVu3bsWsWbNQtWpVpKenY9y4cXLHJC0KDQ2Fr68v7t27h40bN6JUqVIAgBMnTqBnz54yp6M3ccCpAapfvz5mzZqFli1byh2FdMje3h7//POPetBlv379kJeXp15Q7fDhw+jWrRtSUlLkjKkzhj7nBVFxxjMfBujHH3/EiBEjsGPHDty5cwdZWVkaD1KGFy9eqMf5AEBcXBw+/fRT9XMXFxdFT6fv7u6OnTt34v79+zhy5AgOHz6M+/fvY+fOnSweChQZGYmDBw+qn4eGhqJOnTr46quveKa3GGL5MEDt2rVDfHw8AgICUK5cOdjb28Pe3h52dnaKHYBoiCpWrKhePC05ORmXL19G06ZN1ftv3rypPjWtZPb29mjQoAEaNmzI728FGzlypPqPp7Nnz+KHH35Au3btkJiYqB58SsUHB5waoH379skdgSQwePBgDBkyBAcOHMDhw4fh6+ursd7D3r174ePjI2NCIu1JTExUf39v3LgRX3zxBaZPn46TJ08a5LpGxR3LhwH6/PPP5Y5AEhgwYACMjY2xfft2NG3aVH3H0yu3b99G//79ZUpHpF1mZmbIyckBAERFRamXinBwcODl5GKIA04N1IEDB7B48WJcv34df/75J1xdXbFmzRp4eHigSZMmcscjIiqSgIAAPHv2DI0bN8bUqVORmJgIV1dX7NmzB0OGDMHly5fljkiv4ZgPA7Rx40a0adMGlpaWOHnyJHJzcwG8XHBr+vTpMqcjbXj8+LFOjycqbhYuXAgTExNEREQgLCwMrq6uAIBdu3ahbdu2MqejN/HMhwHy8fHB8OHD0bt3b1hbWyM+Ph6enp44deoU/P39kZqaKndE+khly5ZFUFAQ+vTp89aZPIUQiIqKwpw5c9C0aVOMHTtW4pREZKg45sMAJSQkaNz18IqtrS0yMjKkD0Rat3//fvzf//0fJk+ejNq1a6N+/fpwcXGBhYUFHj58iAsXLiAuLg4mJiYYO3Ysvv/+e7kjE320a9euYcWKFbh27Rrmz5+PMmXKYNeuXShfvrwiZ/LVZ7zsYoCcnZ1x9erVAtsPHjwIT09PGRKRtlWtWhUbN27E5cuX0b17d9y6dQsRERFYsmQJ9u/fD1dXVyxZsgRJSUn43//9XxgbG8sdmeijxMTEoGbNmjhy5Ag2bdqE7OxsAEB8fHyBwdYkP152MUAzZszA77//juXLl6NVq1bYuXMnbty4geHDh2PChAkIDAyUOyIRUZH4+vqiW7duCA4O1ricfPToUXTu3Bk3b96UOyK9hpddDNCYMWOQn5+Pli1bIicnB02bNoW5uTlGjBjB4kFEeuns2bNYu3Ztge1lypRR9Ey++orlwwCpVCqMGzcOI0eOxNWrV5GdnQ0vLy9YWVnJHY2I6IPY2dnhzp07BabOP3XqlPrOFyo+WD4MmJmZmcaMl0RE+qpHjx4YPXo0/vzzT6hUKuTn5+PQoUMYMWKEesIxKj445sMAPX36FL/88gv27duHu3fvIj8/X2P/yZMnZUpGRPRhnj17hsGDB2PlypXIy8uDiYkJ8vLy8NVXX2HlypUcVF3MsHwYoF69emHPnj3o2rUrnJycoFKpNPZzZDgR6RMhBFJSUuDo6Ij79+/j7NmzyM7Oho+PDypXrix3PCoEy4cBsrW1xc6dO9G4cWO5o5BEMjIysGzZMly8eBEAUKNGDfTv3x+2trYyJyP6ePn5+bCwsMD58+dZNvQE5/kwQK6urrC2tpY7Bknk+PHjqFixIubOnYv09HSkp6djzpw5qFixIi+xkSIYGRmhcuXKePDggdxR6D3xzIcB2rVrFxYsWIBFixbB3d1d7jikY5999hkqVaqEJUuWwMTk5RjzFy9e4Ntvv8X169cRGxsrc0Kij7d9+3bMnj0bYWFh8Pb2ljsO/QuWDwN07949dO/eHbGxsShRogRMTU019qenp8uUjHTB0tISp06dQrVq1TS2X7hwAfXr11cvQ06kz+zt7ZGTk4MXL17AzMwMlpaWGvv5c6144a22Bqhnz564desWpk+fXuiAU1IWGxsbJCcnFygfKSkpvPxGijFv3jy5I1AR8MyHASpRogTi4uJQu3ZtuaOQBIYOHYrNmzfjp59+wqeffgoAOHToEEaOHIkuXbrwhzYRSY5nPgxQtWrV8OTJE7ljkER++uknqFQq9O7dGy9evAAAmJqaYtCgQZg5c6bM6Yi0Jy8vD5s3b1bf1eXl5YWOHTuqxzpR8cEzHwZoz549CAkJwbRp01CzZs0CYz5sbGxkSka6lJOTg2vXrgEAKlasiBIlSsiciEh7zp8/j4CAAKSmpqJq1aoAgMuXL8PR0RHbt2/nINRihuXDABkZvbzD+s2xHkIIqFQq5OXlyRGLJJKVlYW9e/eiatWqqF69utxxiLTC19cXjo6OWLVqFezt7QEADx8+RN++fXHv3j38888/Miek17F8GKCYmJh37v/8888lSkJS6N69O5o2bYohQ4bgyZMnqF27NpKSkiCEQHh4OLp06SJ3RKKPZmlpiePHj6NGjRoa28+dO4cGDRrwUnMxwwthBuhd5eLcuXMSJiEpxMbGYty4cQCAzZs3QwiBjIwMrFq1Cj/++CPLBylClSpVkJaWVqB83L17F5UqVZIpFb0NZzglPHr0CL/99hsaNmzIO2AUKDMzEw4ODgCAyMhIdOnSBSVKlED79u1x5coVmdMRaceMGTMwdOhQRERE4ObNm7h58yYiIiIwbNgwzJo1C1lZWeoHyY9nPgxYbGwsli1bho0bN8LFxQWdO3dGaGio3LFIy9zc3BAXFwcHBwdERkYiPDwcwMvr4RYWFjKnI9KOL774AsDLy4yvxrO9GlXQoUMH9XOOayseWD4MTGpqKlauXIlly5YhKysL3bt3R25uLrZs2QIvLy+545EODBs2DL169YKVlRXc3d3RrFkzAC/LZ82aNeUNR6Ql+/btkzsCFQEHnBqQDh06IDY2Fu3bt0evXr3Qtm1bGBsbw9TUFPHx8SwfCnb8+HGkpKSgVatWsLKyAgD89ddfsLOz4+rGRCQ5lg8DYmJigqFDh2LQoEEay06zfBiWvLw8nD17Fu7u7upbEon0XWRkJKysrNCkSRMAQGhoKJYsWQIvLy+Ehobye72Y4YBTA3Lw4EE8evQI9erVwyeffIKFCxfi/v37csciHRs2bBiWLVsG4GXx+Pzzz1G3bl24ublh//798oYj0pKRI0eqB5OePXsWwcHBaNeuHRITExEcHCxzOnoTy4cBadSoEZYsWYI7d+7g+++/R3h4OFxcXJCfn4+///4bjx49kjsi6UBERIT6Lqbt27cjMTERly5dwvDhw9W34BLpu8TERPXZ240bN6JDhw6YPn06QkNDsWvXLpnT0ZtYPgxQyZIl0b9/fxw8eBBnz57FDz/8gJkzZ6JMmTIICAiQOx5p2f379+Hs7AwA2LlzJ7p164YqVaqgf//+OHv2rMzpiLTDzMwMOTk5AICoqCi0bt0aAODg4MDba4shlg8DV7VqVcyePRs3b97EunXr5I5DOuDk5IQLFy4gLy8PkZGRaNWqFYCXa70YGxvLnI5IO5o0aYLg4GBMnToVR48eRfv27QG8XN+lXLlyMqejN7F8EADA2NgYnTp1wrZt2+SOQlrWr18/dO/eHd7e3lCpVPDz8wMAHDlyBNWqVZM5HZF2LFy4ECYmJoiIiEBYWBhcXV0BALt27ULbtm1lTkdv4t0uRAYgIiICKSkp6Natm/qvwFWrVsHOzg4dO3aUOR0RGRqWDyID8vTpU85qSoqyYcMGdOrUCWZmZgCAmzdvwsXFRb16d05ODhYuXIhRo0bJGZPewMsuRAqXl5eHqVOnwtXVFVZWVrh+/ToAYMKECepbcIn0Vc+ePZGRkaF+7uXlhaSkJPXzR48eYezYsdIHo3di+SBSuGnTpmHlypWYPXu2+q9DAPD29sbSpUtlTEb08d48ec+T+fqB5YNI4VavXo3ffvsNvXr10ri7pXbt2rh06ZKMyYjIULF8ECncrVu3UKlSpQLb8/Pz8fz5cxkSEZGh46q2RArn5eWFAwcOwN3dXWN7REQEfHx8ZEpFpD27d++Gra0tgJelOjo6GufOnQMAjfEgVHywfBAp3MSJE9GnTx/cunUL+fn52LRpExISErB69Wrs2LFD7nhEH61Pnz4az7///nuN5yqVSso49B54qy2RAThw4ACmTJmC+Ph4ZGdno27dupg4caJ6CmoiIimxfBAp2IsXLzB9+nT079+fU0wTUbHB8kGkcFZWVjh37hwqVKggdxQiIgC824VI8Vq2bImYmBi5YxARqXHAKZHC+fv7Y8yYMTh79izq1auHkiVLauwPCAiQKRkRGSpediFSuFdrXBRGpVIhLy9PwjRERLzsQqR4+fn5b32weJCSZGRkYOnSpRg7dizS09MBACdPnsStW7dkTkZv4pkPIiLSe2fOnIGfnx9sbW2RlJSEhIQEeHp6Yvz48UhOTsbq1avljkiv4ZkPIgMQHR2NL774AhUrVkTFihXxxRdfICoqSu5YRFoTHByMvn374sqVK7CwsFBvb9euHWJjY2VMRoVh+SBSuF9//RVt27aFtbU1goKCEBQUBBsbG7Rr1w6hoaFyxyPSimPHjhWY2RQAXF1dkZqaKkMiehfe7UKkcNOnT8fcuXMxZMgQ9bahQ4eicePGmD59OgYPHixjOiLtMDc3R1ZWVoHtly9fhqOjowyJ6F145oNI4TIyMtC2bdsC21u3bo3MzEwZEhFpX0BAAKZMmaJeqVmlUiE5ORmjR49Gly5dZE5Hb2L5IFK4gIAAbN68ucD2rVu34osvvpAhEZH2/fzzz8jOzkaZMmXw5MkTfP7556hUqRKsra0xbdo0uePRG3i3C5HC/fjjj/jpp5/QuHFj+Pr6AgAOHz6MQ4cO4YcffoCNjY362KFDh8oVk0grDh48iDNnzqgXUPTz85M7EhWC5YNI4Tw8PN7rOJVKhevXr+s4DRERywcREempBQsWvPexPKtXvLB8EBmYvLw8nD17Fu7u7rC3t5c7DtEH41k9/cXyQaRww4YNQ82aNfHNN98gLy8PTZs2RVxcHEqUKIEdO3agWbNmckckIgPDu12IFC4iIgK1a9cGAGzfvh1JSUm4dOkShg8fjnHjxsmcjkj7hBDg39XFG8sHkcLdv38fzs7OAICdO3eiW7duqFKlCvr374+zZ8/KnI5Ie5YtWwZvb29YWFjAwsIC3t7eWLp0qdyxqBCc4ZRI4ZycnHDhwgWULVsWkZGRCAsLAwDk5OTA2NhY5nRE2jFx4kTMmTMHgYGB6lvK4+LiMHz4cCQnJ2PKlCkyJ6TXccwHkcJNnjwZ8+bNQ9myZZGTk4PLly/D3Nwcy5cvx5IlSxAXFyd3RKKP5ujoiAULFqBnz54a29etW4fAwEDcv39fpmRUGJ75IFK4yZMnw9vbGykpKejWrRvMzc0BAMbGxhgzZozM6Yi04/nz56hfv36B7fXq1cOLFy9kSETvwjMfRESk9wIDA2Fqaoo5c+ZobB8xYgSePHnCFZyLGZ75IFKodu3aYd26dbC1tQUAzJw5EwMHDoSdnR0A4MGDB/jss89w4cIFGVMSfbjg4GD1v1UqFZYuXYo9e/agUaNGAIAjR44gOTkZvXv3lisivQXPfBAplLGxMe7cuYMyZcoAAGxsbHD69Gl4enoCANLS0uDi4oK8vDw5YxJ9sObNm7/XcSqVCnv37tVxGioKnvkgUqg3/67g3xmkNPv27ZM7An0gzvNBREREkuKZDyKFUqlUUKlUBbYRKVHz5s3f+f3Nyy7FC8sHkUIJIdC3b1/1rbVPnz7FwIEDUbJkSQBAbm6unPGItKpOnToaz58/f47Tp0/j3Llz6NOnjzyh6K044JRIofr16/dex61YsULHSYjkM3nyZGRnZ+Onn36SOwq9huWDiIgU6+rVq2jYsCHS09PljkKv4YBTIiJSrLi4OFhYWMgdg97AMR9ERKT3OnfurPFcCIE7d+7g+PHjmDBhgkyp6G1YPoiISO+9msn3FSMjI1StWhVTpkxB69atZUpFb8MxH0RERCQpnvkgIiLFOHHiBC5evAgAqFGjBnx8fGRORIVh+SAiIr139+5d9OjRA/v371cvnpiRkYHmzZsjPDwcjo6O8gYkDbzbhYiI9F5gYCAePXqE8+fPIz09Henp6Th37hyysrIwdOhQuePRGzjmg4iI9J6trS2ioqLQoEEDje1Hjx5F69atkZGRIU8wKhTPfBARkd7Lz8+Hqalpge2mpqbIz8+XIRG9C8sHERHpvRYtWiAoKAi3b99Wb7t16xaGDx+Oli1bypiMCsPLLkREpPdSUlIQEBCA8+fPw83NTb3N29sb27ZtQ7ly5WROSK9j+SAiIkUQQiAqKgqXLl0CAFSvXh1+fn4yp6LCsHwQERGRpDjPBxER6bX8/HysXLkSmzZtQlJSElQqFTw8PNC1a1d8/fXXUKlUckekN/DMBxER6S0hBDp06ICdO3eidu3aqFatGoQQuHjxIs6ePYuAgABs2bJF7pj0Bp75ICIivbVy5UrExsYiOjoazZs319i3d+9edOrUCatXr0bv3r1lSkiF4ZkPIiLSW61bt0aLFi0wZsyYQvdPnz4dMTEx2L17t8TJ6F04zwcREemtM2fOoG3btm/d7+/vj/j4eAkT0ftg+SAiIr2Vnp4OJyent+53cnLCw4cPJUxE74Plg4iI9FZeXh5MTN4+fNHY2BgvXryQMBG9Dw44JSIivSWEQN++fWFubl7o/tzcXIkT0ftg+SAiIr3Vp0+ffz2Gd7oUP7zbhYiIiCTFMR9EREQkKZYPIiIikhTLBxEREUmK5YOIiIgkxfJBREREkmL5IKJ/9dtvv8HNzQ1GRkaYN2/eW7cREb0Plg8ihUtNTUVgYCA8PT1hbm4ONzc3dOjQAdHR0e/18VlZWRgyZAhGjx6NW7du4bvvvit028dq1qwZVCrVWx/NmjX76NcgouKB83wQKVhSUhIaN24MOzs7TJkyBTVr1sTz58+xe/du/Pbbb7h06dK/fo5z586hZs2auH79Ojw8PN667WOlp6fj2bNnAICUlBQ0bNgQUVFRqFGjBgDAzMwMDg4OWnktIpIXz3wQKdj//u//QqVS4ejRo+jSpQuqVKmCGjVqIDg4GIcPHwYAJCcno2PHjrCysoKNjQ26d++OtLQ0AMDKlStRs2ZNAICnpydUKlWh25KSkgAAW7duRd26dWFhYQFPT0+EhIRorKuRkZGBb7/9Fo6OjrCxsUGLFi3UK446ODjA2dkZzs7OcHR0BACUKlUKzs7O+OqrrzBx4kSN93bv3j2YmZmpz+BUqFABU6dORc+ePVGyZEm4uroiNDRU42Pe9fpEJB2WDyKFSk9PR2RkJAYPHoySJUsW2G9nZ4f8/Hx07NgR6enpiImJwd9//43r16/jyy+/BAB8+eWXiIqKAgAcPXoUd+7cQbdu3Qpsc3Nzw4EDB9C7d28EBQXhwoULWLx4MVauXIlp06apX7Nbt264e/cudu3ahRMnTqBu3bpo2bIl0tPT3/levv32W6xdu1ZjnY7ff/8drq6uaNGihXrbf//7X9SuXRunTp3CmDFjEBQUhL///vujX5+ItEwQkSIdOXJEABCbNm166zF79uwRxsbGIjk5Wb3t/PnzAoA4evSoEEKIU6dOCQAiMTFRfUxh21q2bCmmT5+u8fnXrFkjypYtK4QQ4sCBA8LGxkY8ffpU45iKFSuKxYsXa2xLTEwUAMSpU6eEEEI8efJE2Nvbi/Xr16uPqVWrlpg8ebL6ubu7u2jbtq3G5/nyyy+Fv79/kV+fiHSLC8sRKZR4j+FcFy9ehJubG9zc3NTbvLy8YGdnh4sXL6JBgwbv/Xrx8fE4dOiQxpmOvLw8PH36FDk5OYiPj0d2djZKlSql8XFPnjzBtWvX3vm5LSws8PXXX2P58uXo3r07Tp48iXPnzmHbtm0ax/n6+hZ4/upOnI95fSLSLpYPIoWqXLkyVCrVew0q1Ybs7GyEhISgc+fOBfZZWFggOzsbZcuWxf79+wvst7Oz+9fP/+2336JOnTq4efMmVqxYgRYtWsDd3b1I+T7m9YlIe1g+iBTKwcEBbdq0QWhoKIYOHVpg3EdGRgaqV6+OlJQUpKSkqM9+XLhwARkZGfDy8irS69WtWxcJCQmoVKnSW/enpqbCxMQEFSpUKPL7qVmzJurXr48lS5Zg7dq1WLhwYYFjXg2iff159erVtfL6RKQ9HHBKpGChoaHIy8tDw4YNsXHjRly5cgUXL17EggUL4OvrCz8/P9SsWRO9evXCyZMncfToUfTu3Ruff/456tevX6TXmjhxIlavXo2QkBCcP38eFy9eRHh4OMaPHw8A8PPzg6+vLzp16oQ9e/YgKSkJ//zzD8aNG4fjx4+/12t8++23mDlzJoQQ+J//+Z8C+w8dOoTZs2fj8uXLCA0NxZ9//omgoCCtvT4RaQfLB5GCeXp64uTJk2jevDl++OEHeHt7o1WrVoiOjkZYWBhUKhW2bt0Ke3t7NG3aFH5+fvD09MT69euL/Fpt2rTBjh07sGfPHjRo0ACNGjXC3Llz1ZdGVCoVdu7ciaZNm6Jfv36oUqUKevTogRs3bsDJyem9XqNnz54wMTFBz549YWFhUWD/Dz/8gOPHj8PHxwc//vgj5syZgzZt2mjt9YlIOzjJGBHpjaSkJFSsWBHHjh1D3bp1NfZVqFABw4YNw7Bhw+QJR0TvjWM+iKjYe/78OR48eIDx48ejUaNGBYoHEekXXnYhomLv0KFDKFu2LI4dO4ZFixbJHYeIPhIvuxAREZGkeOaDiIiIJMXyQURERJJi+SAiIiJJsXwQERGRpFg+iIiISFIsH0RERCQplg8iIiKSFMsHERERSer/A4hfhVri9NocAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Criando o gráfico segundo os dados anteriores\n",
+    "tipo_cafe.plot(kind='bar');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CoffeeCupsPerDay\n",
+       "2    32\n",
+       "3    23\n",
+       "1    16\n",
+       "4    15\n",
+       "5     3\n",
+       "6     3\n",
+       "7     3\n",
+       "8     2\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Usando a mesma função anterior \"value_counts\" para verificar a quantidade de copos consumidos por dia:\n",
+    "quant_copos = df['CoffeeCupsPerDay'].value_counts()\n",
+    "quant_copos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "quant_copos.plot(kind='bar');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Algumas conclusões:\n",
+    "\n",
+    "- As pessoas que fazem parte desse conjunto de dados geralmente codam em média 8 horas por dia; \n",
+    "- Segundo o conjunto de dados, homens consomem mais café enquanto codam;\n",
+    "- O tipo de café mais consumido é o Nescafé;\n",
+    "- E em média são 2 copos por dia.\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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/bdtayna.ipynb b/exercicios/para-casa/bdtayna.ipynb
new file mode 100644
index 0000000..ffd45d7
--- /dev/null
+++ b/exercicios/para-casa/bdtayna.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###Banco de dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import sqlite3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open(\"CoffeeAndCode.csv\", encoding= 'UTF-8') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conteudo = csv.reader(file)\n",
+    "\n",
+    "connection = sqlite3.connect(\"CafeECodigo.db\")\n",
+    "\n",
+    "cursor = connection.cursor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x24d095af8c0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cursor.execute('''CREATE TABLE IF NOT EXISTS rank (\n",
+    "    CodingHours INT,\n",
+    "    CoffeeCupsPerDay INT,\n",
+    "    CoffeeTime VARCHAR, \n",
+    "    CodingWithoutCoffee VARCHAR,\n",
+    "    CoffeeType VARCHAR,\n",
+    "    CoffeeSolveBugs VARCHAR,\n",
+    "    Gender VARCHAR,\n",
+    "    Country VARCHAR,\n",
+    "    AgeRange VARCHAR\n",
+    "    \n",
+    ")''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x24d095af8c0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inserir_conteudo = \"INSERT INTO rank (CodingHours, CoffeeCupsPerDay, CoffeeTime, CodingWithoutCoffee, CoffeeType, CoffeeSolveBugs, Gender, Country, AgeRange) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)\"\n",
+    "conteudo = (10, 3, 'morning', 'yes', 'espresso', 'yes', 'Male', 'USA', '25-34')\n",
+    "cursor.execute(inserir_conteudo, conteudo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selecionar_tudo = \"SELECT * FROM rank\"\n",
+    "entradas = cursor.execute(selecionar_tudo).fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10, 3, 'morning', 'yes', 'espresso', 'yes', 'Male', 'USA', '25-34')\n",
+      "(10, 3, 'morning', 'yes', 'espresso', 'yes', 'Male', 'USA', '25-34')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for entrada in entradas:\n",
+    "  print(entrada)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection.commit()\n",
+    "connection.close()"
+   ]
+  }
+ ],
+ "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}