From f0b1a1fb2183311db5e64b35470bd899083048fd Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 2 Nov 2023 20:06:48 -0300
Subject: [PATCH 1/7] Projeto Guiado III - Nargylla Cloviel (Semana 13)
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Análise de dados e banco de dados
---
 exercicios/para-casa/Banco_de_dados.ipynb    |  115 ++
 exercicios/para-casa/ProjetoGuiado-S13.ipynb | 1099 ++++++++++++++++++
 exercicios/para-casa/Students.db             |  Bin 0 -> 24576 bytes
 3 files changed, 1214 insertions(+)
 create mode 100644 exercicios/para-casa/Banco_de_dados.ipynb
 create mode 100644 exercicios/para-casa/ProjetoGuiado-S13.ipynb
 create mode 100644 exercicios/para-casa/Students.db

diff --git a/exercicios/para-casa/Banco_de_dados.ipynb b/exercicios/para-casa/Banco_de_dados.ipynb
new file mode 100644
index 0000000..8ee892b
--- /dev/null
+++ b/exercicios/para-casa/Banco_de_dados.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open(\"C:\\\\Users\\\\nargy\\\\Documents\\\\Estudos\\\\Semana-13\\\\on26-python-s13-projeto-guiado-II\\\\material\\\\Student Mental health.csv\")\n",
+    "conteudo = csv.reader(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x1b131dad340>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "connection = sqlite3.connect(\"Students.db\")\n",
+    "cursor = connection.cursor()\n",
+    "cursor.execute('''CREATE TABLE IF NOT EXISTS Students (\n",
+    "                Id INTEGER PRIMARY KEY,\n",
+    "                Timestamp TEXT,\n",
+    "                \"Choose your gender\" TEXT,\n",
+    "                Age INTEGER NOT NULL,\n",
+    "                \"What is your course?\" TEXT,\n",
+    "                \"Your current year of Study\" TEXT,\n",
+    "                \"What is your CGPA?\" FLOAT,\n",
+    "                \"Do you have Depression?\" TEXT,\n",
+    "                \"Do you have Anxiety?\" TEXT,\n",
+    "                \"Do you have Panic attack?\" TEXT,\n",
+    "                \"Did you seek any specialist for a treatment?\")\n",
+    "               ''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inserir_conteudo = \"INSERT INTO Students (Timestamp, \\\"Choose your gender\\\", Age, \\\"What is your course?\\\", \\\"Your current year of Study\\\", \\\"What is your CGPA?\\\", \\\"Do you have Depression?\\\", \\\"Do you have Anxiety?\\\", \\\"Do you have Panic attack?\\\", \\\"Did you seek any specialist for a treatment?\\\") VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\n",
+    "for row in conteudo:\n",
+    "    cursor.execute(inserir_conteudo, (row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9]))\n",
+    "\n",
+    "selecionar_tudo = \"SELECT * FROM Students\"\n",
+    "entradas = cursor.fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def excluir_registro(id):\n",
+    "    cursor.execute(\"DELETE FROM Students WHERE Id = ?\", (id,))\n",
+    "\n",
+    "excluir_registro(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/ProjetoGuiado-S13.ipynb b/exercicios/para-casa/ProjetoGuiado-S13.ipynb
new file mode 100644
index 0000000..57639d3
--- /dev/null
+++ b/exercicios/para-casa/ProjetoGuiado-S13.ipynb
@@ -0,0 +1,1099 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ANÁLISE DE DADOS - NARGYLLA CLOVIEL {S13 - PROJETO GUIADO III}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"C:\\\\Users\\\\nargy\\\\Documents\\\\Estudos\\\\Semana-13\\\\on26-python-s13-projeto-guiado-II\\\\material\\\\Student Mental health.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "BREVE HISTÓRIA DOS DADOS:\n",
+    "\n",
+    "Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "#tipo da df\n",
+    "print(type(df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 101 entries, 0 to 100\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column                                        Non-Null Count  Dtype  \n",
+      "---  ------                                        --------------  -----  \n",
+      " 0   Timestamp                                     101 non-null    object \n",
+      " 1   Choose your gender                            101 non-null    object \n",
+      " 2   Age                                           100 non-null    float64\n",
+      " 3   What is your course?                          101 non-null    object \n",
+      " 4   Your current year of Study                    101 non-null    object \n",
+      " 5   What is your CGPA?                            101 non-null    object \n",
+      " 6   Do you have Depression?                       101 non-null    object \n",
+      " 7   Do you have Anxiety?                          101 non-null    object \n",
+      " 8   Do you have Panic attack?                     101 non-null    object \n",
+      " 9   Did you seek any specialist for a treatment?  101 non-null    object \n",
+      "dtypes: float64(1), object(9)\n",
+      "memory usage: 57.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#memória utilizada\n",
+    "df.info(memory_usage=\"deep\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 101 entries, 0 to 100\n",
+      "Data columns (total 10 columns):\n",
+      " #   Column                                        Non-Null Count  Dtype  \n",
+      "---  ------                                        --------------  -----  \n",
+      " 0   Timestamp                                     101 non-null    object \n",
+      " 1   Choose your gender                            101 non-null    object \n",
+      " 2   Age                                           100 non-null    float64\n",
+      " 3   What is your course?                          101 non-null    object \n",
+      " 4   Your current year of Study                    101 non-null    object \n",
+      " 5   What is your CGPA?                            101 non-null    object \n",
+      " 6   Do you have Depression?                       101 non-null    object \n",
+      " 7   Do you have Anxiety?                          101 non-null    object \n",
+      " 8   Do you have Panic attack?                     101 non-null    object \n",
+      " 9   Did you seek any specialist for a treatment?  101 non-null    object \n",
+      "dtypes: float64(1), object(9)\n",
+      "memory usage: 8.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "#valores nulos\n",
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(101, 10)"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Número de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-size: x-large;\">- Analisando os dados</span>\n",
+    "\n",
+    "Os dados vão ser analisados e filtrados buscando entender, sobretudo, como se encontra a saúde mental das mulheres nessa Universidade em comparação a saúde mental dos homens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "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>Timestamp</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>What is your course?</th>\n",
+       "      <th>Your current year of Study</th>\n",
+       "      <th>What is your CGPA?</th>\n",
+       "      <th>Do you have Depression?</th>\n",
+       "      <th>Do you have Anxiety?</th>\n",
+       "      <th>Do you have Panic attack?</th>\n",
+       "      <th>Did you seek any specialist for a treatment?</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Choose your gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>26</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Timestamp  Age  What is your course?  \\\n",
+       "Choose your gender                                         \n",
+       "Female                     75   75                    75   \n",
+       "Male                       26   25                    26   \n",
+       "\n",
+       "                    Your current year of Study  What is your CGPA?  \\\n",
+       "Choose your gender                                                   \n",
+       "Female                                      75                  75   \n",
+       "Male                                        26                  26   \n",
+       "\n",
+       "                    Do you have Depression?  Do you have Anxiety?  \\\n",
+       "Choose your gender                                                  \n",
+       "Female                                   75                    75   \n",
+       "Male                                     26                    26   \n",
+       "\n",
+       "                    Do you have Panic attack?  \\\n",
+       "Choose your gender                              \n",
+       "Female                                     75   \n",
+       "Male                                       26   \n",
+       "\n",
+       "                    Did you seek any specialist for a treatment?  \n",
+       "Choose your gender                                                \n",
+       "Female                                                        75  \n",
+       "Male                                                          26  "
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Qual a quantidade de homens e mulheres que responderam a pesquisa?\n",
+    "df.groupby(\"Choose your gender\").count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Choose your gender\n",
+      "Female    38\n",
+      "Male      17\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#qual gênero dos cursos de interesse mais responderam a pesquisa\n",
+    "curso_interesse = df[df[\"What is your course?\"].isin([\"Engineering\", \"Information Technology\", \"Computer Science\"])]\n",
+    "cont_gender = curso_interesse['Choose your gender'].value_counts()\n",
+    "print(cont_gender)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contagem = cont_gender\n",
+    "plt.pie(contagem, labels=contagem.index, autopct='%1.1f%%', startangle=90, colors=['#00CCFF', '#FF99FF'])\n",
+    "plt.title(\"Gênero dos pesquisados\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Choose your gender\n",
+      "Female    21.0\n",
+      "Male      20.0\n",
+      "Name: Age, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#A média de idade \n",
+    "media_idade_genero = curso_interesse.groupby('Choose your gender')['Age'].mean()\n",
+    "print(round(media_idade_genero))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"font-size:large;\">*Dados das mulheres*</span>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_female = df[(df[\"Choose your gender\"] == \"Female\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Engineering                               20\n",
+      "Computer Science                          11\n",
+      "Information Technology                     7\n",
+      "Islamic Education                          3\n",
+      "Psychology                                 3\n",
+      "English and Literature                     3\n",
+      "Laws                                       3\n",
+      "KENMS                                      2\n",
+      "Communication                              1\n",
+      "Biomedical science                         1\n",
+      "Human Sciences                             1\n",
+      "Biotechnology                              1\n",
+      "Islamic Jurisprudence                      1\n",
+      "Nursing                                    1\n",
+      "Islamic Education                          1\n",
+      "Malcom                                     1\n",
+      "TESL                                       1\n",
+      "Islamic Jurisprudence                      1\n",
+      "Pharmacy                                   1\n",
+      "Biomedical Science                         1\n",
+      "Econs                                      1\n",
+      "CTS                                        1\n",
+      "ALA                                        1\n",
+      "Islamic Revealed Knowledge & Heritage      1\n",
+      "KIRKHS                                     1\n",
+      "Business Administration                    1\n",
+      "Banking Studies                            1\n",
+      "Marine Science                             1\n",
+      "Accounting                                 1\n",
+      "Human Resources                            1\n",
+      "Nursing                                    1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#filtrando a quantidade de mulheres nos cursos\n",
+    "curso_freq = df_female[\"What is your course?\"].value_counts()\n",
+    "print(curso_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Engineering               20\n",
+      "Computer Science          11\n",
+      "Information Technology     7\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "curso_freq_interesse = df_female[\"What is your course?\"].value_counts()\n",
+    "N = 3\n",
+    "top_3_cursos = curso_freq.head(3)\n",
+    "print(top_3_cursos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Engineering               27.0\n",
+      "Computer Science          15.0\n",
+      "Information Technology     9.0\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Colocando em porcentagem para que a comparação seja efetiva\n",
+    "total_mulheres = 75 #total que respondeu a pesquisa\n",
+    "curso_freq_interesse = df_female[\"What is your course?\"].value_counts()\n",
+    "curso_freq_percent = (curso_freq_interesse / total_mulheres) * 100\n",
+    "top_3_cursos = curso_freq_percent.head(3)\n",
+    "print(round(top_3_cursos))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contagem_course = top_3_cursos\n",
+    "contagem_course.plot(kind= \"barh\", edgecolor=\"black\", color=\"#ff0084\")\n",
+    "plt.xlabel(\"\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xticks(np.arange(0, 30, 3))\n",
+    "plt.title(\"Quantidade de mulheres nos cursos de interesse em %\")\n",
+    "plt.grid(axis= 'x', linestyle = '--', alpha = 0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#A saúde mental das mulheres nesses cursos\n",
+    "\n",
+    "palavras_filtro = [\"Engineering\", \"Computer Science\", \"Information Technology\"]\n",
+    "\n",
+    "df_filter_course = df_female[df_female['What is your course?'].isin(palavras_filtro)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Número de alunas dos top 3 cursos com depressão:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 36.8%\n",
+      "Não: 63.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_mulheres_curso = 38\n",
+    "depression = df_filter_course[\"Do you have Depression?\"].value_counts()\n",
+    "\n",
+    "mulheres_depressao = depression.get(\"Yes\", 0)\n",
+    "mulheres_sem_depressao = depression.get(\"No\", 0)\n",
+    "percent_sim = (mulheres_depressao / total_mulheres_curso) * 100\n",
+    "percent_nao = (mulheres_sem_depressao / total_mulheres_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_nao:.1f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Número de alunas dos top 3 cursos com ansiedade:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 36.8%\n",
+      "Não: 63.2%\n"
+     ]
+    }
+   ],
+   "source": [
+    "anxiety = df_filter_course[\"Do you have Anxiety?\"].value_counts()\n",
+    "\n",
+    "mulheres_ansiedade = anxiety.get(\"Yes\", 0)\n",
+    "mulheres_sem_ansiedade = anxiety.get(\"No\", 0)\n",
+    "percent_anxie_sim = (mulheres_ansiedade / total_mulheres_curso) * 100\n",
+    "percent_anxie_nao = (mulheres_sem_ansiedade/ total_mulheres_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_anxie_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_anxie_nao:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Número de alunas dos top 3 cursos com ataque de pânico:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 34.2%\n",
+      "Não: 65.8%\n"
+     ]
+    }
+   ],
+   "source": [
+    "panic = df_filter_course[\"Do you have Panic attack?\"].value_counts()\n",
+    "\n",
+    "mulheres_panico = panic.get(\"Yes\", 0)\n",
+    "mulheres_sem_panico = panic.get(\"No\", 0)\n",
+    "percent_panic_sim = (mulheres_panico/ total_mulheres_curso) * 100\n",
+    "percent_panic_nao = (mulheres_sem_panico/ total_mulheres_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_panic_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_panic_nao:.1f}%\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Número de alunas dos top 3 cursos que fazem tratamento:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 7.89%\n",
+      "Não: 92.11%\n"
+     ]
+    }
+   ],
+   "source": [
+    "treatment = df_filter_course[\"Did you seek any specialist for a treatment?\"].value_counts()\n",
+    "\n",
+    "mulheres_trat = treatment.get(\"Yes\", 0)\n",
+    "mulheres_sem_trat = treatment.get(\"No\", 0)\n",
+    "percent_treat_sim = (mulheres_trat/ total_mulheres_curso) * 100\n",
+    "percent_treat_nao = (mulheres_sem_trat/ total_mulheres_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_treat_sim:.2f}%\")\n",
+    "print(f\"Não: {percent_treat_nao:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "treatment_contagem = percent_treat_sim\n",
+    "treatment_contagem_nao = percent_treat_nao\n",
+    "\n",
+    "anxiety_contagem = percent_anxie_sim\n",
+    "anxiety_contagem_nao = percent_anxie_nao\n",
+    "\n",
+    "depression_contagem = percent_sim\n",
+    "depression_contagem_nao = percent_nao\n",
+    "\n",
+    "panic_attack_contagem = percent_panic_sim\n",
+    "panic_attack_contagem_nao = percent_panic_nao"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
+    "Dados homens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Computer Science          7\n",
+      "Engineering               6\n",
+      "Information Technology    4\n",
+      "KIRKHS                    2\n",
+      "Islamic Education         1\n",
+      "Mathemathics              1\n",
+      "TAASL                     1\n",
+      "Communication             1\n",
+      "Biomedical science        1\n",
+      "Radiography               1\n",
+      "Biomedical Science        1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_male = df[(df[\"Choose your gender\"] == \"Male\")]\n",
+    "#filtrando a quantidade de homens nos cursos\n",
+    "curso_freq_male = df_male[\"What is your course?\"].value_counts()\n",
+    "print(curso_freq_male)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Computer Science          7\n",
+      "Engineering               6\n",
+      "Information Technology    4\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "curso_freq_inter_male = df_male[\"What is your course?\"].value_counts()\n",
+    "N = 3\n",
+    "top_3_cursos = curso_freq_male.head(3)\n",
+    "print(top_3_cursos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your course?\n",
+      "Computer Science          27.0\n",
+      "Engineering               23.0\n",
+      "Information Technology    15.0\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "total_homens = 26\n",
+    "curso_freq_inter_male = df_male[\"What is your course?\"].value_counts()\n",
+    "curso_freq_male_percent = (curso_freq_inter_male/ total_homens) * 100\n",
+    "top_3_cursos_male = curso_freq_male_percent.head(3)\n",
+    "print(round(top_3_cursos_male))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+/bZt2+jWrRurV69m06ZNwNEfT/Hx8cTExDBv3rzDHlI0e/ZsduzYQf/+/UPGGx0dzdlnn33U55Bj7b3iiitCntcKnwMGDBgQssjq7LPPZu/evcHWw+nfvz+bNm3iyy+/ZNOmTYwaNYr8/Hxuv/12HnzwQZKSkrjvvvuoV68eLVq0OOyhWOGiQwhEjkNJJ6a5ubl4nndcL1MuWLCAe+65hy+//LLIcYnZ2dkhT2S1atUq8vXly5c/pmM5D7ZhwwYaNGhQ5Bd8RkZGkW03btzI//3f/zFz5swit1c40S785dWwYcMiX5+RkRHyy3L16tVkZ2eTnp5e7NgyMzOPOO5TcTuF4uLiihz/fOj9vmHDBqpVq1bkj53CwzcO/cV+6Pey8Ptcs2bNYi8vvK3CX8SDBg067Hizs7ODx7wVd1uFn9u+fTspKSlceeWVvPjii1x33XXccccddOrUiT/84Q9cfvnlREUdff/H0R6Xv/32G/n5+cU+rpo0aUIgEOCnn36iWbNm3H///fTq1YtGjRpx2mmn0b17d6655hpatGhxxDGsXbuWPn36HHWsx6J69epFFgt16NCBPn36cN999/H444/TsWNHevfuzVVXXRVc8FTSx8Lx3u8bNmwgKioq5I8GKPpz+9tvv7Fjxw6ef/55nn/++WKvqySP/+Ic6TF1NKtXr+bbb78t8jN1uDEdujh2zZo1OOe4++67ufvuuw97HdWrVz/q4yk2Npbx48czatQoKleuzDnnnMMll1zCwIEDqVKlSnC8ABdeeGGxt5WSklKqvcf73HAk5cuX55xzzgn+e9y4caSnpzNkyBBeeuklnnvuOV555RXWr1/PlVdeycqVKyPmsAJNYEWOQ2pqKtWqVePbb7894nbffvstNWrUCP6yO9wen0MX/axdu5ZOnTrRuHFjHnvsMWrWrElMTAwffPABjz/+eJG9MtHR0cVerztkYUhpKygooEuXLmRlZXH77bfTuHFjEhMT2bRpE4MHDz6uvXaBQID09HReeeWVYj9/uCf7cNzO4e73E3G46zza97jwvn7kkUc4/fTTi9320DdeONp1xsfH89lnnzF37lzef/99PvroI15//XUuvPBCZs2addT+0nxctm/fnrVr1/LOO+8wa9YsXnzxRR5//HGee+45rrvuumO+voOV9Oey0MGvOhx8HdOnT+err77i3Xff5eOPP2bo0KE8+uijfPXVV8f0phcner8fTeFjZcCAAYf9g+dofxgczol8zwOBAF26dOG2224r9vONGjUK+feh34fCrtGjRwdX0h+qcPJVksfTzTffTM+ePXn77bf5+OOPufvuuxk3bhyffvopZ5xxRvD2Xn755eCk9mBHO/XUsfYe73NDSa1fv55HH32UWbNmERUVxb/+9S+GDRsWnKBPnTqV1157jb/+9a/HdL0niyawIsepZ8+eTJo0iS+++ILzzz+/yOc///xz1q9fzy233BK8rHz58uzYsaPItofuhXv33XfZs2cPM2fODPmru6QvaxfnWF4urV27Nt999x3OuZCv++GHH0K2W758OT/++CNTp05l4MCBwcsPXRleu3ZtgGJfrjv0OuvXr88nn3zCeeedV+xE4WjjPhW3c6xj+uSTT8jNzQ3Z8/b999+HjPlEFe51S0lJoXPnzqVynQBRUVF06tSJTp068dhjjzF27Fjuuusu5s6de8K3U6lSJRISEop8b+DA/RMVFRWyd6lChQoMGTKEIUOGsHPnTtq3b8+99957xAls/fr1+e677444jsK9hIf+bJbkZe9DnXPOOZxzzjk8+OCDvPrqq1x99dW89tprXHfddcf0WDie+7127doEAgHWrl0bstf10Pu38AwFBQUFpfpYKanDPRfVr1+fnTt3HveYCg9DKVu2bImuoySPp/r16zNq1ChGjRrF6tWrOf3003n00Uf55z//GfyZS09PP64xn2hvaRs9ejSXXnpp8PfZL7/8QrVq1YKfr1at2lEPSziVdAysyHEaPXo0CQkJDBs2jG3btoV8Lisriz/96U+kpKQwYsSI4OX169cnOzs7ZM/t5s2bixxbVPgX9cF/QWdnZzN58uTjHm9iYiJQ9Jd0cS666CJ++eUXpk+fHrys8JRhRxunc46///3vIdtVrVqV008/nalTp4Ycvzt79uwi71jWt29fCgoKeOCBB4qMa//+/Ucc/6m6nWNx0UUXUVBQwNNPPx1y+eOPP47nefTo0aNUbqd169bUr1+fCRMmsHPnziKfP/hURiVV3Kl4CvfuFndKtWMVHR1N165deeedd0JOTbRlyxZeffVVzj///ODLsIf+jCUlJdGgQYOjjqNPnz4sW7as2OP3Ch+3hRORg485LCgoOOzL68XZvn17kT1eh95XJX0sHO/9Xvj1Tz75ZMjlh65mj46Opk+fPrz55pvFTu6P57FyLBITE4v9+erbty9ffvklH3/8cZHP7dix46jvBpWenk7Hjh2ZNGkSmzdvLvL5g7uO9njKz89n9+7dIdvUr1+f5OTk4DbdunUjJSWFsWPHFnvc89HuxxPtLU1z587lgw8+4OGHHw5eVrly5eAfVwCrVq0qdk9zuGgPrMhxatCgAdOmTaN///40b96ca6+9lrp167J+/Xr+8Y9/sH37dl577bWQ47T69evH7bffzmWXXcZNN90UPOVKo0aNQo7P7Nq1KzExMfTs2ZNhw4axc+dOXnjhBdLT04t9Yi6J008/nejoaMaPH092djaxsbHB88we6vrrr+fpp59m4MCBLFq0iKpVq/Lyyy8XOVl748aNqV+/PqNHj2bTpk2kpKTw5ptvFnvs1bhx47j44os5//zzGTp0KFlZWTz11FM0a9YsZMLVoUMHhg0bxrhx41i6dCldu3albNmyrF69mjfeeIO///3vIYvLwnU7JdWzZ08uuOAC7rrrLtavX0/Lli2ZNWsW77zzDjfffHOR4xWPV1RUFC+++CI9evSgWbNmDBkyhOrVq7Np0ybmzp1LSkoK77777jFd5/33389nn33GxRdfTO3atcnMzOTZZ5+lRo0axb7qcDz+9re/Bc95esMNN1CmTBkmTZrEnj17Qn6ZNm3alI4dO9K6dWsqVKjAN998w/Tp00P+QCzOrbfeyvTp07niiisYOnQorVu3Jisri5kzZ/Lcc8/RsmVLmjVrxjnnnMOYMWPIysqiQoUKvPbaa8c0gZg6dSrPPvssl112GfXr1yc3N5cXXniBlJQULrroIqDkj4Xjvd9PP/10+vfvz7PPPkt2djZt27Zlzpw5rFmzpsi2Dz30EHPnzuXss8/m+uuvp2nTpmRlZbF48WI++eSTo55H9ES0bt2aiRMn8re//Y0GDRqQnp7OhRdeyK233srMmTO55JJLgqcBzMvLY/ny5UyfPp3169cfdT3BM888w/nnn0/z5s25/vrrqVevHlu2bOHLL7/k559/ZtmyZcDRH08//vgjnTp1om/fvjRt2pQyZcrw1ltvsWXLFvr16wcceLVj4sSJXHPNNbRq1Yp+/fpRqVIlNm7cyPvvv895551X5I+Vg5VGb2koKCjg5ptv5tZbbw15xe/yyy/ntttuo1KlSmzYsIHly5cf9pCrsDjVpz0QsWb58uXuqquuclWqVHFRUVEOcHFxcW7FihXFbj9r1ix32mmnuZiYGJeRkeH++c9/FnsarZkzZ7oWLVq4uLg4V6dOHTd+/Hj30ksvFTndT+3atYs9Hcyhp8ZyzrkXXnjB1atXz0VHR4ecHqi4bTds2OAuvfRSl5CQ4NLS0txf/vKX4CmmDj6t0MqVK13nzp1dUlKSS0tLc9dff33wdEmTJ08Ouc4333zTNWnSxMXGxrqmTZu6GTNmHPbUYs8//7xr3bq1i4+Pd8nJya558+butttuc7/88kux9+upvp1Bgwa5xMTEIpcX973Mzc11I0eOdNWqVXNly5Z1DRs2dI888kjI6YacO3DqqeHDh4dcVngKnUNP51N4iqc33ngj5PIlS5a4P/zhD65ixYouNjbW1a5d2/Xt29fNmTOnyBgPPU3ToaeTmjNnjuvVq5erVq2ai4mJcdWqVXP9+/cvctqf4hTX4lzR0y8559zixYtdt27dXFJSkktISHAXXHCB+89//hOyzd/+9jfXpk0bV65cORcfH+8aN27sHnzwQbd3796jjmXbtm1uxIgRrnr16i4mJsbVqFHDDRo0KOQUUmvXrnWdO3d2sbGxrnLlyu7OO+90s2fPLvY0Ws2aNStyG4sXL3b9+/d3tWrVcrGxsS49Pd1dcskl7ptvvgnZriSPhRO533ft2uVuuukmV7FiRZeYmOh69uzpfvrppyKn0XLOuS1btrjhw4e7mjVrurJly7oqVaq4Tp06ueeff/6ot3O402gdehqx4k5F9uuvv7qLL77YJScnOyDkuSc3N9eNGTPGNWjQwMXExLi0tDTXtm1bN2HChOD3+nA/E4XWrl3rBg4c6KpUqeLKli3rqlev7i655BI3ffr04DZHezxt3brVDR8+3DVu3NglJia61NRUd/bZZ4ecWvDgxm7durnU1FQXFxfn6tev7wYPHlzke1+cE+k93HPA4b4Xh/PMM8+4GjVquLy8vJDL9+3b52655RaXlpbmateu7aZOnVqi6ztVPOdO8ioPkd+ZadOmMXjwYAYMGBDyblgiIiJSOnQIgUgpGzhwIJs3b+aOO+6gRo0ajB07NtxDEhERMUV7YEVERETEV3QWAhERERHxFU1gRURERMRXNIEVEREREV/RBFZEREREfEVnIRBzAoEAv/zyC8nJycf09qkiIiISPs45cnNzqVatGlFRR97HqgmsmPPLL7+EvH+6iIiI+MdPP/1EjRo1jriNJrBiTnJyMgAbNmygXLly4R1MKQkEAvz2229UqlTpqH+V+oW1Jms9oCY/sNYD9pqs9cDJa8rJyaFmzZrB3+NHogmsmFN42EBKSgopKSlhHk3pCAQC7N69m5SUFFNPgJaarPWAmvzAWg/Ya7LWAye/qSSH/9m4J0VERETkd0MTWDHL0gIuz/NISUlRUwSz1gNq8gNrPWCvyVoPREaTDiEQs6w9WSQkJIR7GKXKWpO1HlCTH1jrAXtN1nogMpq0B1bMCgQC4R5CqQkEAmzdulVNEcxaD6jJD6z1gL0maz0QGU2awIr4xP79+8M9hFJnrclaD6jJD6z1gL0maz0Q/iZNYEVERETEVzSBFRERERFf0QRWzLK2iKt8+fJqimDWekBNfmCtB+w1WeuByGjSWQjELGtPFrGxseEeRqmy1mStB9TkB9Z6wF6TtR6IjCbtgRWzrK343LJli5oimLUeUJMfWOsBe03WeiAymjSBFfEJ51y4h1DqrDVZ6wE1+YG1HrDXZK0Hwt+kCayIiIiI+IomsCIiIiLiK5rAilnWFnFVrFhRTRHMWg+oyQ+s9YC9Jms9EBlNmsCKWdaeLKKjo9UUwaz1gJr8wFoP2Guy1gOR0aQJrJhlbcVnZmammiKYtR5Qkx9Y6wF7TdZ6IDKaNIEVEREREV/RBFZEREREfEUTWBERERHxFc+F+0y0IqUsJyeH1NRUsrOzSUlJCfdwSk0gECAqytbfnNaarPWAmvzAWg/Ya7LWAyen6Vh+f9u6N0UOYulvM+ccBQUFaopg1npATX5grQfsNVnrgcho0gRWzLL2ZLFt2zY1RTBrPaAmP7DWA/aarPVAZDRpAisiIiIivqIJrIiIiIj4iiawIj5h6V1cCllrstYDavIDaz1gr8laD4S/SWchEHOsnoVARETEsmP5/V3mFI1J5JRbsmQJycnJ4R5GqXDOBU9ZEu6/ekuLtSZrPfD7a0pLS6NWrVphGtnxcc6xd+9eYmJiTH2PLDVZ64HIaNIEVsy64IILzKz6jIqKonXr1ixatMjM+2lba7LWA7+/poT4BFZ9v8pXk1jnHNu3byc9Pd3U5MhSk7UeiIwmTWDFrDvbTaJJWutwD6N0eI7o6rspqBUHzsYToLkmaz3wu2pat30Vd88dwNatW301gRX5vdIEVsyqXa4RjdNahXsYpcJ5AXanZBK3Jx3P2Vh7aa3JWg+oSUQil356RXzC22/v701rTdZ6QE1+UKaMrR6w12StB8LfZO8eFSlk5SVPwHNRxGWlhXsYpcpak7UeUJMfREVFkZZmpwfsNVnrgcho0h5YMczGAi4Ah2N/XD5OTRHLWg+oyQ+cc+Tn55tZsAr2mqz1QGQ0aQIrdtnZAQueY19KDnh2ngDNNVnrATX5gHOOnJwcc5MjS03WeiAymjSBFRERERFf0QRWRERERHxFE1gRP3AeUXtjTC1MM9dkrQfU5AOe55l6hyew12StByKjSWchELuM/IIC8PCI3VEh3MMoVdaarPWAmvzA8zwqVLDTA/aarPVAZDRpD6wYZuiAeRz7EnPNrJwGe03WekBNfuCcIzc319wCIUtN1nogMpo0gRW77OyABc+xPzHPzMppwF6TtR5Qkw8458jLyzM3ObLUZK0HIqNJE1gRERER8RVNYEVERETEVzSBFfED5xG9K97UwjRzTdZ6QE0+4Hke8fHx5la4W2qy1gOR0aSzEIhdRn5BwYGV0zG5qeEeRqmy1mStB9TkB57nkZpqpwfsNVnrgcho0h5YscvIIg04sHJ6b3K2mZXTYK/JWg+oyQ+cc2RnZ5tbIGSpyVoPREaTJrAifuA5CuJ3mZqUm2uy1gNq8gHnHLt27TI3ObLUZK0HIqNJE1gRERER8RVNYEVERETEVzSBFbvsvFoDzqNMXqKphWnmmqz1gJp8wPM8EhMTza1wt9RkrQcio0lnIRDDDD1Z4FE2LzncwyhV1pqs9YCa/MDzPJKT7fSAvSZrPRAZTdoDK3YZWaQBB1ZO7ymXZWblNNhrstYDavID5xxZWVnmFghZarLWA5HRpAmsiB94jkDMXlOTcnNN1npATT7gnGPv3r3mJkeWmqz1QGQ0aQIrIiIiIr5y0iewv/76K126dCExMZFy5cqd7JsrNVOmTPHVeA81ePBgevfubeZ2RERERAod0wT2eCYrjz/+OJs3b2bp0qX8+OOPx/S1p0qdOnV44oknQi678sorT+p4169fj+d5R/yYMmXKSbv93wU7r9aA8yibk2Jm5TRgr8laD6jJBzzPIyUlxdwKd0tN1nogMppO+lkI1q5dS+vWrWnYsOFxX8fevXuJiYkpxVEdXXx8PPHx8Sft+mvWrMnmzZuD/54wYQIfffQRn3zySfCycL/PsP8ZerLAo8zuhHAPo1RZa7LWA2ryA8/zSEiw0wP2mqz1QGQ0ndAhBB07duSmm27itttuo0KFClSpUoV77703+Pk6derw5ptvMm3aNDzPY/DgwQBs3LiRXr16kZSUREpKCn379mXLli3Br7v33ns5/fTTefHFF6lbty5xcXHAgTts0qRJXHLJJSQkJNCkSRO+/PJL1qxZQ8eOHUlMTKRt27asXbs2eF1r166lV69eVK5cmaSkJM4666yQSWLHjh3ZsGEDI0eODO75hOIPIZg4cSL169cnJiaGjIwMXn755ZDPe57Hiy++yGWXXUZCQgINGzZk5syZxd530dHRVKlSJfiRlJREmTJlgv9OT0/niSeeoG7dusTHx9OyZUumT58ech0rVqzgkksuISUlheTkZNq1axfSDgcmxlWrVqVixYoMHz6cffv2hXx/xo4dy9ChQ0lOTqZWrVo8//zzIV+/fPlyLrzwQuLj46lYsSJ//OMf2blzZ7FNAHv27OGmm24iPT2duLg4zj//fL7++uuQbWbOnEnDhg2Ji4vjggsuYOrUqXiex44dO8jLyyMlJaVI69tvv01iYiK5ubmHve0ijCzSAHBegN0VtuK8QLiHUmqsNVnrATX5QSAQYOvWrQQCNnrAXpO1HoiMphM+Bnbq1KkkJiby3//+l4cffpj777+f2bNnA/D111/TvXt3+vbty+bNm/n73/9OIBCgV69eZGVlMX/+fGbPns3//vc/rrzyypDrXbNmDW+++SYzZsxg6dKlwcsfeOABBg4cyNKlS2ncuDFXXXUVw4YNY8yYMXzzzTc45xgxYkRw+507d3LRRRcxZ84clixZQvfu3enZsycbN24EYMaMGdSoUYP777+fzZs3h+wVPdhbb73FX/7yF0aNGsV3333HsGHDGDJkCHPnzg3Z7r777qNv3758++23XHTRRVx99dVkZWUd8/06btw4pk2bxnPPPceKFSsYOXIkAwYMYP78+QBs2rSJ9u3bExsby6effsqiRYsYOnQo+/fvD17H3LlzWbt2LXPnzmXq1KlMmTKlyGEJjz76KGeeeSZLlizhhhtu4M9//jM//PADAHl5eXTr1o3y5cvz9ddf88Ybb/DJJ5+E3L+Huu2223jzzTeZOnUqixcvpkGDBnTr1i14H6xbt47LL7+c3r17s2zZMoYNG8Zdd90V/PrExET69evH5MmTQ6538uTJXH755cWed27Pnj3k5OSEfFjkyuw/+kY+Y63JWg+oyQ8Oft63wlqTtR4If9MJH0LQokUL7rnnHgAaNmzI008/zZw5c+jSpQuVKlUiNjaW+Ph4qlSpAsDs2bNZvnw569ato2bNmgBMmzaNZs2a8fXXX3PWWWcBBw4bmDZtGpUqVQq5vSFDhtC3b18Abr/9ds4991zuvvtuunXrBsBf/vIXhgwZEty+ZcuWtGzZMvjvBx54gLfeeouZM2cyYsQIKlSoQHR0NMnJycExFmfChAkMHjyYG264AYBbbrmFr776igkTJnDBBRcEtxs8eDD9+/cHYOzYsTz55JMsXLiQ7t27l/g+3bNnD2PHjuWTTz7h3HPPBaBevXp88cUXTJo0iQ4dOvDMM8+QmprKa6+9RtmyZQFo1KhRyPWUL1+ep59+mujoaBo3bszFF1/MnDlzuP7664PbXHTRRcGm22+/nccff5y5c+eSkZHBq6++yu7du5k2bRqJiYkAPP300/Ts2ZPx48dTuXLlkNvLy8tj4sSJTJkyhR49egDwwgsvMHv2bP7xj39w6623MmnSJDIyMnjkkUcAyMjI4LvvvuPBBx8MXs91111H27Zt2bx5M1WrViUzM5MPPvggZM/5wcaNG8d9991X4vtXRERE/O2E98C2aNEi5N+FE47DWbVqFTVr1gxOXgGaNm1KuXLlWLVqVfCy2rVrF5m8Hnp7hROo5s2bh1y2e/fu4F64nTt3Mnr0aJo0aUK5cuVISkpi1apVwT2wJbVq1SrOO++8kMvOO++8kDEfOr7ExERSUlKOeH8UZ82aNeTn59OlSxeSkpKCH9OmTQseIrB06VLatWsXnLwWp1mzZkRHRwf/Xdz35uDxep5HlSpVgtusWrWKli1bBievhc2BQCC4l/Zga9euZd++fSH3U9myZWnTpk3wfvrhhx+Cf6QUatOmTZF/N2vWjKlTpwLwz3/+k9q1a9O+fftiO8eMGUN2dnbw46effjrsfSIiIiL+d8J7YA+dQHmeVyrHRBw8aTrc7RUer1rcZYVjGD16NLNnz2bChAk0aNCA+Ph4Lr/8cvbu3XvCYzza+ArHc6z3R+Expu+//z7Vq1cP+VxsbCxAiRaYlWQsJ+v7d6Kuu+46nnnmGe644w4mT57MkCFDDrvaMTY2Nni/hLBzCCw4j5gd5c2snAbsNVnrATX5gOd5lC9f3twKd0tN1nogMppO+RsZNGnShJ9++ilkL9nKlSvZsWMHTZs2LfXbW7BgAYMHD+ayyy6jefPmVKlShfXr14dsExMTQ0FBwVHHvWDBgiLXfTLG3LRpU2JjY9m4cSMNGjQI+Sjcc92iRQs+//zzkEVZpa1JkyYsW7aMvLy84GULFiwgKiqKjIyMItsXLnA7+H7at28fX3/9dfB+ysjI4Jtvvgn5ukMXeQEMGDCADRs28OSTT7Jy5UoGDRp0HAWGnizwiN4bi6emiGWtB9TkB57nERsba25yZKnJWg9ERtMpn8B27tyZ5s2bc/XVV7N48WIWLlzIwIED6dChA2eeeWap317Dhg2DC8GWLVvGVVddVWQPY506dfjss8/YtGkTW7duLfZ6br31VqZMmcLEiRNZvXo1jz32GDNmzGD06NGlPubk5GRGjx7NyJEjmTp1KmvXrmXx4sU89dRTwZfVR4wYQU5ODv369eObb75h9erVvPzyy8W+tH+8rr76auLi4hg0aBDfffcdc+fO5cYbb+Saa64pcvwrHNhr/uc//5lbb72Vjz76iJUrV3L99deTn5/PtddeC8CwYcP4/vvvuf322/nxxx/597//HVxYdvAPQvny5fnDH/7ArbfeSteuXalRo8axBxg7C8GuSlvMrJwGe03WekBNfhAIBNiyZUtEvHJWWqw1WeuByGg65RNYz/N45513KF++PO3bt6dz587Uq1eP119//aTc3mOPPUb58uVp27YtPXv2pFu3brRq1Spkm/vvv5/169dTv379Yo+7Bejduzd///vfmTBhAs2aNWPSpElMnjyZjh07npRxP/DAA9x9992MGzeOJk2a0L17d95//33q1q0LQMWKFfn000/ZuXMnHTp0oHXr1rzwwgtHPCb2WCUkJPDxxx+TlZXFWWedxeWXX06nTp14+umnD/s1Dz30EH369OGaa66hVatWrFmzho8//pjy5csDULduXaZPn86MGTNo0aIFEydODJ6F4NDDAK699lr27t3L0KFDS63J1wxNyIOsNVnrATX5QDjfj/5ksdZkrQfC3+S5cI9AfvcefPBBnnvuuSKLr15++WVGjhzJL7/8ckxvZJGTk0NqairPXzqPVlU6lPZww8J5AXZXyiTut3Q8d8r/7jwprDVZ64HfV9P3WxczYEZrFi1aVGQnRyQLBAJkZmaSnp5OVJSN75G1Jms9cPKaCn9/Z2dnk5KScsRtT/o7cYkc6tlnn+Wss86iYsWKLFiwgEceeSTk3LL5+fls3ryZhx56iGHDhp3yd2ETERGRyGbjTwHxldWrV9OrVy+aNm3KAw88wKhRo0Lewe3hhx+mcePGVKlShTFjxhz/DVl6bcF5xG6raGblNGCvyVoPqMkHPM+jYsWK5hYIWWqy1gOR0aRDCMSc4CEEPefTqmrx5471I+cFzLyMW8hak7Ue+P00+fUQAjjwcq6Vl6YLWWuy1gMnp+lYDiGwdW+KHMzQQo3C4/asrJwGe03WekBNflB4LKK1Fe6Wmqz1QGQ0aQIrIiIiIr6iCayIiIiI+IomsCIiIiLiK5rAil1GVhkDeC7K1Lk4wV6TtR5Qkx9ERUWZOr8o2Guy1gOR0WTn3hQpwtAiLhwuqgCnpohlrQfU5AfOOQoKCsL+rkilyVqTtR6IjCZNYMUuOztgwXPsqbjN1JkVzDVZ6wE1+YBzjm3btpmbHFlqstYDkdGkCayIiIiI+IomsCIiIiLiK5rAiviFoUVpQdaarPWAmnzA0luUFrLWZK0Hwt9UJqy3LnIyGfol5bko4n+rHO5hlCprTdZ6QE1+EBUVReXKdnrAXpO1HoiMJu2BFcMMHTCPoyBmj5mV02CvyVoPqMkPnHPs2bPH3AIhS03WeiAymjSBFbvs7IAFz7G33HYzK6cBe03WekBNPuCcY/v27eYmR5aarPVAZDRpAisiIiIivqIJrIiIiIj4iiawIj7h7be35tJak7UeUJMflCljqwfsNVnrgfA32btHRQoZOwtBXFZauIdRqqw1WesBNflBVFQUaWl2esBek7UeiIwm7YEVwwwdMI9jf1y+mZXTYK/JWg+oyQ+cc+Tn55tbIGSpyVoPREaTJrBil50dsOA59qXkmFk5DdhrstYDavIB5xw5OTnmJkeWmqz1QGQ0aQIrIiIiIr6iY2DFrA07fiShTHK4h1E6PEd07G4Ktm6yc2yvtSZrPfC7alq3fVUYByUix0oTWDFr7OfDzLxkExUVRYMGDVizZg2BQCDcwykV1pqs9cDvrykhPiHsC1OOled5xMTEhP196UuTtSZrPRAZTZrAilnz5s0jKSkp3MMQEZ9IS0ujVq1a4R7GMfE8jwoVKoR7GKXKWpO1HoiMJk1gxayWLVuSmpoa7mGUCuccO3fuJCkpycxf8daarPWAmvzAWg/Ya7LWA5HRpEVcYpaVwwfgQEteXp6aIpi1HlCTH1jrAXtN1nogMpo0gRURERERX9EEVkRERER8RRNYMcvKsUZwoCU+Pl5NEcxaD6jJD6z1gL0maz0QGU2es3RQhgiQk5NDamoq2dnZpKSkhHs4IiIiUgLH8vtbe2DFLEt/mznnyM7OVlMEs9YDavIDaz1gr8laD0RGkyawYpa1J4tdu3apKYJZ6wE1+YG1HrDXZK0HIqNJE1gRERER8RVNYEVERETEVzSBFbOsrfhMTExUUwSz1gNq8gNrPWCvyVoPREaTzkIg5ugsBCIiIv6jsxCIYG8RV1ZWlpoimLUeUJMfWOsBe03WeiAymjSBFbOsPVns3btXTRHMWg+oyQ+s9YC9Jms9EBlNmsCKiIiIiK9oAisiIiIivqIJrJhlbcVnSkqKmiKYtR5Qkx9Y6wF7TdZ6IDKayoTtlkVOMmtPFgkJCeEeRqmy1mStB9TkB9Z6wF6TtR6IjCbtgRWzAoFAuIdQagKBAFu3blVTBLPWA2ryA2s9YK/JWg9ERpMmsCI+sX///nAPodRZa7LWA2ryA2s9YK/JWg+Ev0kTWBERERHxFU1gRURERMRXNIEVs6wt4ipfvryaIpi1HlCTH1jrAXtN1nogMpp0FgIxy9qTRWxsbLiHUaqsNVnrATX5gbUesNdkrQcio0l7YMUsays+t2zZoqYIZq0H1OQH1nrAXpO1HoiMJk1gRXzC0vtoF7LWZK0H1OQH1nrAXpO1Hgh/kyawIiIiIuIrmsCKiIiIiK9oAitmWVvEVbFiRTVFMGs9oCY/sNYD9pqs9UBkNGkCK2ZZe7KIjo5WUwSz1gNq8gNrPWCvyVoPREaTJrBilrUVn5mZmWqKYNZ6QE1+YK0H7DVZ64HIaNIEVkRERER8RRNYEREREfEVTWBFRERExFc8F+4z0YqUspycHFJTU8nOziYlJSXcwyk1gUCAqChbf3Naa7LWA2ryA2s9YK/JWg+cnKZj+f1t694UOYilv82ccxQUFKgpglnrATX5gbUesNdkrQcio0kTWDHL2pPFtm3b1BTBrPWAmvzAWg/Ya7LWA5HRpAmsiIiIiPiKJrAiIiIi4iuawIr4hKV3cSlkrclaD6jJD6z1gL0maz0Q/iadhUDMsXoWAhEREct0FgIR7C3i2rNnj5oimLUeUJMfWOsBe03WeiAymjSBFbOsPVls375dTRHMWg+oyQ+s9YC9Jms9EBlNmsCKiIiIiK9oAisiIiIivqIJrIhPlClTJtxDKHXWmqz1gJr8wFoP2Guy1gPhb9JZCMQcnYVARETEf3QWAhHsLeLKz89XUwSz1gNq8gNrPWCvyVoPREaTJrBilrUni5ycHDVFMGs9oCY/sNYD9pqs9UBkNGkCKyIiIiK+ogmsiIiIiPiKJrBiVrjfp7k0eZ5HTEyMmiKYtR5Qkx9Y6wF7TdZ6IDKadBYCMUdnIRAREfEfnYVABHuLuHJzc9UUwaz1gJr8wFoP2Guy1gOR0aQJrJhl7ckiLy9PTRHMWg+oyQ+s9YC9Jms9EBlNmsCKiIiIiK9oAisiIiIivqIJrJhlbcVnfHy8miKYtR5Qkx9Y6wF7TdZ6IDKadBYCMUdnIRAREfGfY/n9XeYUjUnklFuyZAnJycnhHkapcM6xb98+ypYta+aveGtN1npATX5grQcisyktLY1atWod19cWvu1qSkpKxPScqEho0gRWzLrgggvMrPqMioqidevWLFq0iEAgEO7hlAprTdZ6QE1+YK0HIrMpIT6BVd+vOq5JrHOOXbt2kZycbGoCG+4mTWDFrDvbTaJJWutwD6N0eI7o6rspqBUHzsYToLkmaz2gJj+w1gMR17Ru+yrunjuArVu3HvdeWCl9msCKWbXLNaJxWqtwD6NUOC/A7pRM4vak4zkbay+tNVnrATX5gbUesNkkpU+PDLHLxtEDBziPMnmJEbE3otRYa7LWA2ryA2s9YK7J8zwSExPNHD4AkdGkPbBimKEnCzzK5tlYkFbIWpO1HlCTH1jrAXtNnueZWVBcKBKatAdW7PLs7IJ1OPaUy8IZ2q1srclaD6jJD6z1gL0m5xxZWVlmFhVDZDRpAiviB54jELPX1KTcXJO1HlCTH1jrAXNNzjn27t1rbgIb7iZNYEVERETEVzSBFRERERFf0QRW7LLzag04j7I5KWZW5QL2mqz1gJr8wFoPmGvyPM/Uu3BBZDTpLARimKEnCzzK7E4I9zBKlbUmaz2gJj+w1gP2mjzPIyHBTg9ERpP2wIpdRhYAwP87sXeFrTgvMt5WsTRYa7LWA2ryA2s9YK8pEAiwdevWiHlb3NIQCU2awIr4hCuzP9xDKHXWmqz1gJr8wFoP2Gvav99WD4S/SRNYEREREfEVTWBFRERExFc0gRW77BwCC84jZkd5M6tyAXtN1npATX5grQfMNXmeR/ny5c2dhSDcTToLgRhm6MkCj+i9seEeRqmy1mStB9TkB9Z6wF6T53nExtrpgcho0h5YscvYWQh2VdpiZlUu2Guy1gNq8gNrPWCvKRAIsGXLFnNnIQh3kyawIn5haEIeZK3JWg+oyQ+s9YC5Juds9UD4mzSBFRERERFf0QRWRERERHxFE1ixy9IrNs4jdltFM6tyAXtN1npATX5grQfMNXmeR8WKFc2dhSDcTToLgRhm6MkCDwLRB/5rhLUmaz2gJj+w1gP2mjzPIzo62twENtxN2gMrdhlaBOC8ALsrZZpZlQv2mqz1gJr8wFoP2GsKBAJkZmaaOwtBuJs0gRURERERX9EEVkRERER8RRNYY6ZMmUK5cuXCPYwgz/N4++23wz0MERERMUQT2FNo8ODBeJ5X5KN79+6ldhtXXnklP/74Y6ld34navHkzPXr0CM+NG1nBCuC5KOJ+S8dzdn5krTVZ6wE1+YG1HrDXFBUVRXp6OlFRNnogMpp0FoJTrHv37kyePDnkstJ8P+H4+Hji4+NL7fqO1969e4mJiaFKlSphHIWhRVw4XFQBFHhmVuZaa7LWA2ryA2s9YK/JOUdBQUFwp5UFkdBk588Bn4iNjaVKlSohH+XLlwcOvNz+4osvctlll5GQkEDDhg2ZOXNmyNfPnDmThg0bEhcXxwUXXMDUqVPxPI8dO3YARQ8huPfeezn99NN5+eWXqVOnDqmpqfTr14/c3NzgNoFAgHHjxlG3bl3i4+Np2bIl06dPD7nd7777jh49epCUlETlypW55ppr2Lp1a/DzHTt2ZMSIEdx8882kpaXRrVu3YFPhIQTr16/H8zxmzJjBBRdcQEJCAi1btuTLL78Mua0XXniBmjVrkpCQwGWXXcZjjz12xMMi9uzZQ05OTsjHgRs/6rfDPzzHnorbTJ1ZwVyTtR5Qkx9Y6wFzTc45tm3bFva3Xi1NkdCkCWyEue++++jbty/ffvstF110EVdffTVZWVkArFu3jssvv5zevXuzbNkyhg0bxl133XXU61y7di1vv/027733Hu+99x7z58/noYceCn5+3LhxTJs2jeeee44VK1YwcuRIBgwYwPz58wHYsWMHF154IWeccQbffPMNH330EVu2bKFv374htzN16lRiYmJYsGABzz333GHHc9dddzF69GiWLl1Ko0aN6N+/P/v37wdgwYIF/OlPf+Ivf/kLS5cupUuXLjz44INH7Bs3bhypqanBj5o1ax71PhERERH/0gT2FHvvvfdISkoK+Rg7dmzw84MHD6Z///40aNCAsWPHsnPnThYuXAjApEmTyMjI4JFHHiEjI4N+/foxePDgo95mIBBgypQpnHbaabRr145rrrmGOXPmAAf2Xo4dO5aXXnqJbt26Ua9ePQYPHsyAAQOYNGkSAE8//TRnnHEGY8eOpXHjxpxxxhm89NJLzJ07N+R424YNG/Lwww+TkZFBRkbGYcczevRoLr74Yho1asR9993Hhg0bWLNmDQBPPfUUPXr0YPTo0TRq1IgbbrjhqMfQjhkzhuzs7ODHTz/9dNT7RERERPxLx8CeYhdccAETJ04MuaxChQrB/2/RokXw/xMTE0lJSSEzMxOAH374gbPOOivka9u0aXPU26xTpw7JycnBf1etWjV4nWvWrCE/P58uXbqEfM3evXs544wzAFi2bBlz584lKSmpyHWvXbuWRo0aAdC6deujjgVCG6tWrQpAZmYmjRs35ocffuCyyy4L2b5Nmza89957h72+2NjYUj2OOGIZWpQWZK3JWg+oyQ+s9YC5JivHvh4s3E2awJ5iiYmJNGjQ4LCfL1u2bMi/Pc874Xe6ONJ17ty5E4D333+f6tWrh2xXOCncuXMnPXv2ZPz48UWuu3ACCgfajnU8hT8AJ+XdPAw9AXouivjfKod7GKXKWpO1HlCTH1jrAXtNUVFRVK5spwcio0kTWB/JyMjggw8+CLns66+/PqHrbNq0KbGxsWzcuJEOHToUu02rVq148803qVOnDmXKnNyHTEZGRpGm4280dMA8jkDMXqL2xphYlQv2mqz1gJr8wFoP2GtyzgXPzBPuvZalJRKadAzsKbZnzx5+/fXXkI+DV/MfybBhw/j++++5/fbb+fHHH/n3v//NlClTgOPflZ+cnMzo0aMZOXIkU6dOZe3atSxevJinnnqKqVOnAjB8+HCysrLo378/X3/9NWvXruXjjz9myJAhFBQUHNftHs6NN97IBx98wGOPPcbq1auZNGkSH3744fH12XieOMBz7C233cyqXMBek7UeUJMfWOsBc03OObZv327uLAThbtIE9hT76KOPqFq1asjH+eefX6KvrVu3LtOnT2fGjBm0aNGCiRMnBs9CcCLHgD7wwAPcfffdjBs3jiZNmtC9e3fef/996tatC0C1atVYsGABBQUFdO3alebNm3PzzTdTrly5Uj+J8Xnnncdzzz3HY489RsuWLfnoo48YOXIkcXFxpXo7IiIi4l+es/Qnwe/Qgw8+yHPPPWd65f3111/P999/z+eff16i7XNyckhNTeX5S+fRqkrxh0X4jfMC7K6Uaerdaaw1WesBNfmBtR6IvKbvty5mwIzWLFq0iFatWh3z1wcCATIzM8P+zlWl6WQ1Ff7+zs7OJiUl5Yjb6hhYn3n22Wc566yzqFixIgsWLOCRRx5hxIgR4R5WqZowYQJdunQhMTGRDz/8kKlTp/Lss8+Ge1hh5+239+NqrclaD6jJD6z1gL2mk71+JBzC3WTvHjVu9erV/O1vfyMrK4tatWoxatQoxowZE+5hlaqFCxfy8MMPk5ubS7169XjyySe57rrrjv2KjJ2FIC4rLdzDKFXWmqz1gJr8wFoP2GuKiooiLc1OD0RGkyawPvP444/z+OOPh3sYJ9W///3vUromO0fHOBwFcbuI3h1vYlUu2Guy1gNq8gNrPWCvyTnHrl27iI+PN3UWgnA32TgYQ6Q4Np4nDvAc+1JyzKzKBew1WesBNfmBtR4w1+ScIycnx9xZCMLdpAmsiIiIiPiKJrAiIiIi4iuawIr4gfOI2htjamGauSZrPaAmP7DWA+aaPM8z9S5cEBlNWsQldhl58gPw8IjdUSHcwyhV1pqs9YCa/MBaD9hr8jyPChXs9EBkNGkPrBhm6IB5HPsSc3FqiljWekBNfmCtB+w1OefIzc01t4gr3E2awIpddnbAgufYn5hnZlUuYK/JWg+oyQ+s9YC5JucceXl55iaw4W7SBFZEREREfEUTWBERERHxFU1gRfzAeUTvije1MM1ck7UeUJMfWOsBc02e55l6Fy6IjCadhUDsMvLkBwdW5cbkpoZ7GKXKWpO1HlCTH1jrAXtNnueRmmqnByKjSXtgxS4jCwDgwKrcvcnZZlblgr0maz2gJj+w1gP2mpxzZGdnm1vEFe4mTWBF/MBzFMTvMjUpN9dkrQfU5AfWesBck3OOXbt2mZvAhrtJE1gRERER8RVNYEVERETEVzSBFbvsvFoDzqNMXqKphWnmmqz1gJr8wFoPmGvyPI/ExERzZyEId5POQiCGGXqywKNsXnK4h1GqrDVZ6wE1+YG1HrDX5Hkeycl2eiAymrQHVuwysgAADqzK3VMuy8yqXLDXZK0H1OQH1nrAXpNzjqysLHOLuMLdpAmsiB94jkDMXlOTcnNN1npATX5grQfMNTnn2Lt3r7kJbLibdAiBmLVhx48klDHyso3niI7dTcHWTWaOCzPXZK0H1OQH1nog4prWbV8V7iFIMTSBFbPGfj7MzF+8UVFRtG7dmkWLFhEIBMI9nFJhrclaD6jJD6z1QGQ2JcQnkJaWFu5hyEE0gRWz5s6dG/aDzEuLc46CggKio6PNrGS11mStB9TkB9Z6IDKb0tLSqFWr1nF9red5pKSkRExLaYiEJk1gxawzzjiDlJSUcA9DRER+xzzPIyEhIdzDKFWR0KRFXGJWpLz0VBoCgQBbt25VUwSz1gNq8gNrPWCvyVoPREaTJrAiPrF///5wD6HUWWuy1gNq8gNrPWCvyVoPhL9JE1gRERER8RVNYEVERETEVzSBFbOsrfgsX768miKYtR5Qkx9Y6wF7TdZ6IDKadBYCMcvak0VsbGy4h1GqrDVZ6wE1+YG1HrDXZK0HIqNJe2DFLGsrPrds2aKmCGatB9TkB9Z6wF6TtR6IjCZNYEV8wsq7ih3MWpO1HlCTH1jrAXtN1nog/E2awIqIiIiIr2gCKyIiIiK+ogmsmGVtEVfFihXVFMGs9YCa/MBaD9hrstYDkdGkCayYZe3JIjo6Wk0RzFoPqMkPrPWAvSZrPRAZTZrAilnWVnxmZmaqKYJZ6wE1+YG1HrDXZK0HIqNJE1gRERER8RVNYEVERETEVzSBFRERERFf8Vy4z0QrUspycnJITU0lOzublJSUcA+n1AQCAaKibP3Naa3JWg+oyQ+s9YC9Jms9cHKajuX3t617U+Qglv42c85RUFCgpghmrQfU5AfWesBek7UeiIwmTWDFLGtPFtu2bVNTBLPWA2ryA2s9YK/JWg9ERpMmsCIiIiLiK5rAioiIiIivaAIr4hOW3sWlkLUmaz2gJj+w1gP2mqz1QPibdBYCMcfqWQhEREQs01kIRLC3iGvPnj1qimDWekBNfmCtB+w1WeuByGjSBFbMsvZksX37djVFMGs9oCY/sNYD9pqs9UBkNGkCKyIiIiK+ogmsiIiIiPiKJrAiPlGmTJlwD6HUWWuy1gNq8gNrPWCvyVoPhL9JZyEQc3QWAhEREf/RWQhEsLeIKz8/X00RzFoPqMkPrPWAvSZrPRAZTZrAilnWnixycnLUFMGs9YCa/MBaD9hrstYDkdGkCayIiIiI+IomsCIiIiLiK5rAilnhfp/m0uR5HjExMWqKYNZ6QE1+YK0H7DVZ64HIaNJZCMQcnYVARETEf3QWAhHsLeLKzc1VUwSz1gNq8gNrPWCvyVoPREaTJrBilrUni7y8PDVFMGs9oCY/sNYD9pqs9UBkNGkCKyIiIiK+ogmsiIiIiPiKJrBilrUVn/Hx8WqKYNZ6QE1+YK0H7DVZ64HIaNJZCMQcnYVARETEf3QWAhHsLeLKzs5WUwSz1gNq8gNrPWCvyVoPREaTJrBilrUni127dqkpglnrATX5gbUesNdkrQcio0kTWBERERHxFU1gRURERMRXNIEVs6yt+ExMTFRTBLPWA2ryA2s9YK/JWg9ERpPOQiDm6CwEIiIi/qOzEIhgbxFXVlaWmiKYtR5Qkx9Y6wF7TdZ6IDKaNIEVs6w9Wezdu1dNEcxaD6jJD6z1gL0maz0QGU2awIqIiIiIr2gCKyIiIiK+ogmsmGVtxWdKSoqaIpi1HlCTH1jrAXtN1nogMprKhO2WRU4ya08WCQkJ4R5GqbLWZK0H1OQH1nrAXpO1HoiMJu2BFbMCgUC4h1BqAoEAW7duVVMEs9YDavIDaz1gr8laD0RGkyawIj6xf//+cA+h1FlrstYDavIDaz1gr8laD4S/SRNYEREREfEVTWBFRERExFc0gRWzrC3iKl++vJoimLUeUJMfWOsBe03WeiAymnQWAjFr2bJlJCUlhXsYIiIipqSlpVGrVq3wDsKJGJOdne0A53meA0x8REVFuVatWrmoqKiwj0VNv48eNfnjw1qPxSZrPYBLSkxy3377rSsoKDgpv7+zs7OPuq32wIpZd7abRJO01uEeRunwHNHVd1NQNw6ckZehrDVZ6wE1+YG1HrDXZKxn3fZV3DN/IDk5OWEdhyawYlbtco1onNYq3MMoFc4LsDslk7g96XjOxqHr1pqs9YCa/MBaD9hrstYTKXRPioiIiIivaAIrdrlwD6AUOY/YbRVNvPwUZK3JWg+oyQ+s9YC9Jms9HHgnrtjYWJ2FQOTksPNk4eFBIPrAf42w1mStB9TkB9Z6wF6TtZ5CnueFdQKrPbBil2dnF6zzAuyulInz7LyXtrUmaz2gJj+w1gP2mqz1AERFRbF7924CgfA1aQIrIiIiIr6iCayIiIiI+IomsCIiIiLiK5rAil2GVnx6Loq432ydQ9Bak7UeUJMfWOsBe03WeuDAWQji4uKIigpfk517U6QIQ4u4cLioApyaIpa1HlCTH1jrAXtN1noKOedwLnxNmsCKXXZ2wILn2FNxm6kzK5hrstYDavIDaz1gr8laDwfOQrBnzx5NYEVERERESkoTWBERERHxFU1gRfzC0KK0IGtN1npATX5grQfsNVnriQB6K1mxy9AThueiiP+tcriHUaqsNVnrATX5gbUesNdkrQcOnIUgPj5eZyEQOTnsHDDvcBTE7DG1itVak7UeUJMfWOsBe03WegoVFBRoEZfISWFnByx4jr3ltptaxWquyVoPqMkPrPWAvSZrPRw4C8HevXs1gRURERERKSlNYEVERETEVzSBFfEJb7+9NZfWmqz1gJr8wFoP2Guy1gPgeeE9Ts/ePSpSyNhZCOKy0sI9jFJlrclaD6jJD6z1gL0maz1w4CwEcXFxOguByMlh54B5h2N/XL6pVazWmqz1gJr8wFoP2Guy1gMH9r7u379fi7hETgo7O2DBc+xLyTG1itVck7UeUJMfWOsBe03Wejgwgd23b58msCIiIiIiJaUJrBxVnTp1eOKJJ8I9DBERERHAwAT2119/5cYbb6RevXrExsZSs2ZNevbsyZw5c8I9tBKZMmUK5cqVO6m38dZbb3HOOeeQmppKcnIyzZo14+abby7x13/99df88Y9/PHkDlKNzHlF7Y0wtTDPXZK0H1OQH1nrAXpO1nv8nKioqrGci8PVZCNavX895551HuXLleOSRR2jevDn79u3j448/Zvjw4Xz//ffhHuIpU1BQgOd5RVYEzpkzhyuvvJIHH3yQSy+9FM/zWLlyJbNnzy7xdVeqVKm0h3tqGHqy8PCI3VEh3MMoVdaarPWAmvzAWg/Ya7LWAwfOQhAbGxvWCayv98DecMMNeJ7HwoUL6dOnD40aNaJZs2bccsstfPXVV8HtNm7cSK9evUhKSiIlJYW+ffuyZcuW4OfvvfdeTj/9dF566SVq1apFUlISN9xwAwUFBTz88MNUqVKF9PR0HnzwwZDb9zyPiRMn0qNHD+Lj46lXrx7Tp08Pfn7evHl4nseOHTuCly1duhTP81i/fj3z5s1jyJAhZGdn43kenudx7733ArBnzx5Gjx5N9erVSUxM5Oyzz2bevHnB6ynccztz5kyaNm1KbGwsGzduLHIfvfvuu5x33nnceuutZGRk0KhRI3r37s0zzzxTZLuzzjqLuLg40tLSuOyyy4KfO/QQgh07dnDddddRqVIlUlJSuPDCC1m2bFmR+/Pll1+mTp06pKam0q9fP3Jzc4PbBAIBHn74YRo0aEBsbCy1atUKuX9/+ukn+vbtS7ly5ahQoQK9evVi/fr1RfqOzM4B8w7HvsRcU6tYrTVZ6wE1+YG1HrDXZK0HtIjrhGRlZfHRRx8xfPhwEhMTi3y+8GX5QCBAr169yMrKYv78+cyePZv//e9/XHnllSHbr127lg8//JCPPvqIf/3rX/zjH//g4osv5ueff2b+/PmMHz+ev/71r/z3v/8N+bq7776bPn36sGzZMq6++mr69evHqlWrStTQtm1bnnjiCVJSUti8eTObN29m9OjRAIwYMYIvv/yS1157jW+//ZYrrriC7t27s3r16uDX5+fnM378eF588UVWrFhBenp6kduoUqUKK1as4LvvvjvsON5//30uu+wyLrroIpYsWcKcOXNo06bNYbe/4ooryMzM5MMPP2TRokW0atWKTp06kZWVFXJ/vv3227z33nu89957zJ8/n4ceeij4+TFjxvDQQw9x9913s3LlSl599VUqV64MwL59++jWrRvJycl8/vnnLFiwgKSkJLp3787evXuLjGfPnj3k5OSEfADmzkKwPzHP1CpWc03WekBNfmCtB+w1WeshMk6j5dtDCNasWYNzjsaNGx9xuzlz5rB8+XLWrVtHzZo1AZg2bRrNmjXj66+/5qyzzgIOTHRfeuklkpOTadq0KRdccAE//PADH3zwAVFRUWRkZDB+/Hjmzp3L2WefHbz+K664guuuuw6ABx54gNmzZ/PUU0/x7LPPHrUhJiaG1NRUPM+jSpUqwcs3btzI5MmT2bhxI9WqVQNg9OjRfPTRR0yePJmxY8cCByZ6zz77LC1btjzsbdx44418/vnnNG/enNq1a3POOefQtWtXrr76amJjYwF48MEH6devH/fdd1/w6w53nV988QULFy4kMzMz+PUTJkzg7bffZvr06cFjZQOBAFOmTCE5ORmAa665hjlz5vDggw+Sm5vL3//+d55++mkGDRoEQP369Tn//PMBeP311wkEArz44ovBlycmT55MuXLlmDdvHl27dg0Z07hx40LGLiIiIrb5dg9sSWf9q1atombNmsHJK0DTpk0pV65cyJ7SOnXqBCdbAJUrV6Zp06Yhx5RWrlyZzMzMkOs/99xzi/y7pHtgD2f58uUUFBTQqFEjkpKSgh/z589n7dq1we1iYmJo0aLFEa8rMTGR999/nzVr1vDXv/6VpKQkRo0aRZs2bcjPzwcOHNbQqVOnEo1t2bJl7Ny5k4oVK4aMbd26dSFjO/T+rFq1avC+W7VqFXv27DnsbS5btow1a9aQnJwcvP4KFSqwe/fukNsoNGbMGLKzs4MfP/30U4laRERExJ98uwe2YcOGeJ5Xagu1ypYtG/Jvz/OKvSwQCJT4OgsnvwdPtvft23fUr9u5cyfR0dEsWrSI6OjokM8lJSUF/z8+Pr7EB1DXr1+f+vXrc91113HXXXfRqFEjXn/9dYYMGUJ8fHyJrqNwbFWrVg05HrfQwWdTONJ9d7Tb27lzJ61bt+aVV14p8rniFpTFxsYG9wab5Tyid8WbWphmrslaD6jJD6z1gL0maz3/T3R0tBZxHY8KFSrQrVs3nnnmGfLy8op8vnDhVJMmTfjpp59C9sqtXLmSHTt20LRp0xMex8GLxQr/3aRJE+D/n2xt3rw5+PmlS5eGbB8TE0NBQUHIZWeccQYFBQVkZmbSoEGDkI+DDzU4XnXq1CEhISF4v7Vo0aLEpx1r1aoVv/76K2XKlCkytrS0kr3Xc8OGDYmPjz/sbbZq1YrVq1eTnp5e5DZSU1NLFgmmniw8PGJyU/EMHdhrrclaD6jJD6z1gL0maz1w4DDBmJgYTWCP1zPPPENBQQFt2rThzTffZPXq1axatYonn3wy+NJ+586dad68OVdffTWLFy9m4cKFDBw4kA4dOnDmmWee8BjeeOMNXnrpJX788UfuueceFi5cyIgRIwBo0KABNWvW5N5772X16tW8//77PProoyFfX6dOHXbu3MmcOXPYunUr+fn5NGrUiKuvvpqBAwcyY8YM1q1bx8KFCxk3bhzvv//+MY3v3nvv5bbbbmPevHmsW7eOJUuWMHToUPbt20eXLl0AuOeee/jXv/7FPffcw6pVq1i+fDnjx48v9vo6d+7MueeeS+/evZk1axbr16/nP//5D3fddRfffPNNicYUFxfH7bffzm233ca0adNYu3YtX331Ff/4xz8AuPrqq0lLS6NXr158/vnnrFu3jnnz5nHTTTfx888/lzze0AHzDsfe5GxTq1itNVnrATX5gbUesNdkrQcOvMK8d+9enYXgeNWrV4/FixdzwQUXMGrUKE477TS6dOnCnDlzmDhxInDgpet33nmH8uXL0759ezp37ky9evV4/fXXS2UM9913H6+99hotWrRg2rRp/Otf/wru2S1btiz/+te/+P7772nRogXjx4/nb3/7W8jXt23blj/96U9ceeWVVKpUiYcffhg4sGhp4MCBjBo1ioyMDHr37s3XX39NrVq1jml8HTp04H//+x8DBw6kcePG9OjRg19//ZVZs2aRkZEBQMeOHXnjjTeYOXMmp59+OhdeeCELFy4s9vo8z+ODDz6gffv2DBkyhEaNGtGvXz82bNgQPItASdx9992MGjWK//u//6NJkyZceeWVwWNkExIS+Oyzz6hVqxZ/+MMfaNKkCddeey27d+8mJSXlmPrN8BwF8btMTcrNNVnrATX5gbUesNdkref/KSgoCOsE1nPhvHWf8zyPt956i969e4d7KHKQnJwcUlNTef7SebSq0iHcwykVzguwu1Imcb+l4zlf/90ZZK3JWg+oyQ+s9YC9Jms9329dzMC3z+Kzzz7j3HPPLfIGSiei8Pd3dnb2UXdY+f+eFBEREZHfFU1gxS5Lry04jzJ5iaYWpplrstYDavIDaz1gr8laDwfOrlSmTJmwLuLy7Wm0IoGOvoh0dp4sPDzK5iUffUMfsdZkrQfU5AfWesBek7UeODD/KVu2rM5CIHJSGDpg3uHYUy7L1CpWa03WekBNfmCtB+w1WeuBA2ch2LNnj85CICJH4TkCMXtNTcrNNVnrATX5gbUesNdkref/CQQCmsCKiIiIiJSUJrAiIiIi4iuawIpdll6tcR5lc1JMrWI112StB9TkB9Z6wF6TtR4iYxGXzkIghtl5svDwKLM7IdzDKFXWmqz1gJr8wFoP2Guy1gORcRot7YEVuwwdMO+8ALsrbMV5gXAPpdRYa7LWA2ryA2s9YK/JWg8cOAvB7t27CQTC16QJrIhPuDL7wz2EUmetyVoPqMkPrPWAvSZrPRD+c+FrAisiIiIivqIJrIiIiIj4iiawYpedQ2DBecTsKG9qFau5Jms9oCY/sNYD9pqs9XDgTQxiYmJ0FgKRk8POk4WHR/Te2HAPo1RZa7LWA2ryA2s9YK/JWk+h6OhonYVA5KQwdhaCXZW2mFrFaq3JWg+oyQ+s9YC9Jms9cOAsBLt27dJZCESkBAxNyIOsNVnrATX5gbUesNdkrScCaAIrIiIiIr6iCayIiIiI+IomsGKXpVdsnEfstoqmVrGaa7LWA2ryA2s9YK/JWg8HzkIQGxursxCInBx2niw8PAhEH/ivEdaarPWAmvzAWg/Ya7LWU8jzPJ2FQOSkMHTQvPMC7K6UaWoVq7Umaz2gJj+w1gP2mqz1wIGzEOzevTusZyHQHlgxa8OOH0kokxzuYZQOzxEdu5uCrZvsvAxlrclaD6jJD6z1gL0mYz3rtq8K9xAATWDFsLGfD8M5G3tho6KiaN26NYsWLQrrX7ylyVqTtR5Qkx9Y6wF7TdZ6AJISk0hJSQnrGDSBFbPmzp1LcrKNPbDOOXbv3k1cXFxYjzkqTdaarPWAmvzAWg/Ya7LWA1CxYkViY8P77mKawIpZZ5xxRtj/QixNgUCAqChbh61ba7LWA2ryA2s9YK/JWg+Ev8nWvSlyECuHD8CBloKCAjVFMGs9oCY/sNYD9pqs9UBkNGkCK2ZZe7LYtm2bmiKYtR5Qkx9Y6wF7TdZ6IDKaNIEVEREREV/RBFZEREREfEUTWBGfsLJ69WDWmqz1gJr8wFoP2Guy1gPhb/KcpYMyRICcnBxSU1PJzs42dRYCERERy47l97f2wIpZlv42c86xZ88eNUUwaz2gJj+w1gP2mqz1QGQ0aQIrZll7sti+fbuaIpi1HlCTH1jrAXtN1nogMpo0gRURERERX9EEVkRERER8RRNYEZ8oU8beOz9ba7LWA2ryA2s9YK/JWg+Ev0lnIRBzdBYCERER/9FZCESwt4grPz9fTRHMWg+oyQ+s9YC9Jms9EBlNmsCKWdaeLHJyctQUwaz1gJr8wFoP2Guy1gOR0aQJrIiIiIj4iiawIiIiIuIrmsCKWeF+n+bS5HkeMTExaopg1npATX5grQfsNVnrgcho0lkIxBydhUBERMR/dBYCEewt4srNzVVTBLPWA2ryA2s9YK/JWg9ERpMmsGKWtSeLvLw8NUUwaz2gJj+w1gP2mqz1QGQ0aQIrIiIiIr6iCayIiIiI+IomsGKWtRWf8fHxaopg1npATX5grQfsNVnrgcho0lkIxBydhUBERMR/dBYCEewt4srOzlZTBLPWA2ryA2s9YK/JWg9ERpMmsGKWtSeLXbt2qSmCWesBNfmBtR6w12StByKjSRNYEREREfGVMuEegEhpK/yLMCcnh6goG3+jBQIBcnNziYuLU1OEstYDavIDaz1gr8laD5y8ppycHKBkr6BqAivmbNu2DYDatWuHeSQiIiJyrHJzc0lNTT3iNprAijkVKlQAYOPGjUf9AfCLnJwcatasyU8//WTmzArWmqz1gJr8wFoP2Guy1gMnr6nwLWqrVat21G01gRVzCl/OSE1NNfNkUSglJUVNEc5aD6jJD6z1gL0maz1wcppKuuPJxsEYIiIiIvK7oQmsiIiIiPiKJrBiTmxsLPfccw+xsbHhHkqpUVPks9YDavIDaz1gr8laD0RGk95KVkRERER8RXtgRURERMRXNIEVEREREV/RBFZEREREfEUTWBERERHxFU1gRURERMRXNIEVc5555hnq1KlDXFwcZ599NgsXLgz3kI7bxIkTadGiRfDdTs4991w+/PDDcA/rhGzatIkBAwZQsWJF4uPjad68Od988024h3VCcnNzufnmm6lduzbx8fG0bduWr7/+OtzDKrHPPvuMnj17Uq1aNTzP4+233w5+bt++fdx+++00b96cxMREqlWrxsCBA/nll1/CN+CjOFIPwODBg/E8L+Sje/fu4RlsCR2taefOnYwYMYIaNWoQHx9P06ZNee6558Iz2BIYN24cZ511FsnJyaSnp9O7d29++OGHkG2ef/55OnbsSEpKCp7nsWPHjvAMtoRK0lTIOUePHj2K/V5GiqP1rF+/vsjPUeHHG2+8cdLHpwmsmPL6669zyy23cM8997B48WJatmxJt27dyMzMDPfQjkuNGjV46KGHWLRoEd988w0XXnghvXr1YsWKFeEe2nHZvn075513HmXLluXDDz9k5cqVPProo5QvXz7cQzsh1113HbNnz+bll19m+fLldO3alc6dO7Np06ZwD61E8vLyaNmyJc8880yRz+Xn57N48WLuvvtuFi9ezIwZM/jhhx+49NJLwzDSkjlST6Hu3buzefPm4Me//vWvUzjCY3e0pltuuYWPPvqIf/7zn6xatYqbb76ZESNGMHPmzFM80pKZP38+w4cP56uvvmL27Nns27ePrl27kpeXF9wmPz+f7t27c+edd4ZxpCVXkqZCTzzxBJ7nhWGUJXe0npo1a4b8DG3evJn77ruPpKQkevTocfIH6EQMadOmjRs+fHjw3wUFBa5atWpu3LhxYRxV6Spfvrx78cUXwz2M43L77be7888/P9zDKFX5+fkuOjravffeeyGXt2rVyt11111hGtXxA9xbb711xG0WLlzoALdhw4ZTM6gTUFzPoEGDXK9evcIyntJQXFOzZs3c/fffH3KZnx6DmZmZDnDz588v8rm5c+c6wG3fvv3UD+wEHK5pyZIlrnr16m7z5s0l+nmLFEf6HhU6/fTT3dChQ0/JeLQHVszYu3cvixYtonPnzsHLoqKi6Ny5M19++WUYR1Y6CgoKeO2118jLy+Pcc88N93COy8yZMznzzDO54oorSE9P54wzzuCFF14I97BOyP79+ykoKCAuLi7k8vj4eL744oswjerkys7OxvM8ypUrF+6hHLd58+aRnp5ORkYGf/7zn9m2bVu4h3RC2rZty8yZM9m0aRPOOebOncuPP/5I165dwz20EsnOzgagQoUKYR5J6SmuKT8/n6uuuopnnnmGKlWqhGtox+Vo36NFixaxdOlSrr322lMyHk1gxYytW7dSUFBA5cqVQy6vXLkyv/76a5hGdeKWL19OUlISsbGx/OlPf+Ktt96iadOm4R7Wcfnf//7HxIkTadiwIR9//DF//vOfuemmm5g6dWq4h3bckpOTOffcc3nggQf45ZdfKCgo4J///CdffvklmzdvDvfwSt3u3bu5/fbb6d+/PykpKeEeznHp3r0706ZNY86cOYwfP5758+fTo0cPCgoKwj204/bUU0/RtGlTatSoQUxMDN27d+eZZ56hffv24R7aUQUCAW6++WbOO+88TjvttHAPp1QcrmnkyJG0bduWXr16hXF0x64k36N//OMfNGnShLZt256SMZU5JbciIsctIyODpUuXkp2dzfTp0xk0aBDz58/35SQ2EAhw5plnMnbsWADOOOMMvvvuO5577jkGDRoU5tEdv5dffpmhQ4dSvXp1oqOjadWqFf3792fRokXhHlqp2rdvH3379sU5x8SJE8M9nOPWr1+/4P83b96cFi1aUL9+febNm0enTp3COLLj99RTT/HVV18xc+ZMateuzWeffcbw4cOpVq1ayKtSkWj48OF89913pl6xKK5p5syZfPrppyxZsiSMIzs+R/se7dq1i1dffZW77777lI1Je2DFjLS0NKKjo9myZUvI5Vu2bPHdSzUHi4mJoUGDBrRu3Zpx48bRsmVL/v73v4d7WMelatWqRSbeTZo0YePGjWEaUemoX78+8+fPZ+fOnfz0008sXLiQffv2Ua9evXAPrdQUTl43bNjA7Nmzfbv3tTj16tUjLS2NNWvWhHsox2XXrl3ceeedPPbYY/Ts2ZMWLVowYsQIrrzySiZMmBDu4R3RiBEjeO+995g7dy41atQI93BKxeGaPv30U9auXUu5cuUoU6YMZcoc2IfYp08fOnbsGKbRHl1JvkfTp08nPz+fgQMHnrJxaQIrZsTExNC6dWvmzJkTvCwQCDBnzhzfHjNanEAgwJ49e8I9jONy3nnnFTmtzI8//kjt2rXDNKLSlZiYSNWqVdm+fTsff/yx714mPJzCyevq1av55JNPqFixYriHVKp+/vlntm3bRtWqVcM9lOOyb98+9u3bR1RU6K/06OhoAoFAmEZ1ZM45RowYwVtvvcWnn35K3bp1wz2kE3a0pjvuuINvv/2WpUuXBj8AHn/8cSZPnhyGER/ZsXyP/vGPf3DppZdSqVKlUzY+HUIgptxyyy0MGjSIM888kzZt2vDEE0+Ql5fHkCFDwj204zJmzBh69OhBrVq1yM3N5dVXX2XevHl8/PHH4R7acSk8/mvs2LH07duXhQsX8vzzz/P888+He2gn5OOPP8Y5R0ZGBmvWrOHWW2+lcePGvnnc7dy5M2Tv47p161i6dCkVKlSgatWqXH755SxevJj33nuPgoKC4DHlFSpUICYmJlzDPqwj9VSoUIH77ruPPn36UKVKFdauXcttt91GgwYN6NatWxhHfWRHaqpVqxYdOnTg1ltvJT4+ntq1azN//nymTZvGY489FsZRH97w4cN59dVXeeedd0hOTg4+plJTU4mPjwfg119/5ddffw12L1++nOTkZGrVqhWRi72O1lSlSpViXw2sVatWRE7gS/I9AlizZg2fffYZH3zwwakd4Ck514HIKfTUU0+5WrVquZiYGNemTRv31VdfhXtIx23o0KGudu3aLiYmxlWqVMl16tTJzZo1K9zDOiHvvvuuO+2001xsbKxr3Lixe/7558M9pBP2+uuvu3r16rmYmBhXpUoVN3z4cLdjx45wD6vECk9TdOjHoEGD3Lp164r9HODmzp0b7qEX60g9+fn5rmvXrq5SpUqubNmyrnbt2u766693v/76a7iHfURHanLOuc2bN7vBgwe7atWqubi4OJeRkeEeffRRFwgEwjvwwzjcY2ry5MnBbe65556jbhNJStJU3NdE6mm0StozZswYV7NmTVdQUHBKx+f9v0GKiIiIiPiCjoEVEREREV/RBFZEREREfEUTWBERERHxFU1gRURERMRXNIEVEREREV/RBFZEREREfEUTWBERERHxFU1gRURERMRXNIEVEREREV/RBFZEREREfEUTWBERERHxlf8PYNj2Z9Dj7fwAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contagem_course_male = top_3_cursos_male\n",
+    "contagem_course_male.plot(kind= \"barh\", edgecolor=\"black\", color=\"#cc66ff\")\n",
+    "plt.xlabel(\"\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.xticks(np.arange(0, 30, 3))\n",
+    "plt.title(\"Quantidade de homens nos cursos de interesse em %\")\n",
+    "plt.grid(axis= 'x', linestyle = '--', alpha = 0.3)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palavras_filtro_male = [\"Engineering\", \"Computer Science\", \"Information Technology\"]\n",
+    "\n",
+    "df_filter_course_male = df_male[df_male['What is your course?'].isin(palavras_filtro)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 29.4%\n",
+      "Não: 70.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NÚMERO DE ALUNOS DOS TOP 3 CURSOS COM DEPRESSÃO:\n",
+    "\n",
+    "total_homens_curso = 17 \n",
+    "depression_male = df_filter_course_male[\"Do you have Depression?\"].value_counts()\n",
+    "\n",
+    "homens_depressao = depression_male.get(\"Yes\", 0)\n",
+    "homens_sem_depressao = depression_male.get(\"No\", 0)\n",
+    "percent_male_sim = (homens_depressao / total_homens_curso) * 100\n",
+    "percent_male_nao = (homens_sem_depressao/ total_homens_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_male_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_male_nao:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 47.1%\n",
+      "Não: 52.9%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NÚMERO DE ALUNOS DOS TOP 3 CURSOS COM ANSIEDADE:\n",
+    "\n",
+    "anxiety_male = df_filter_course_male[\"Do you have Anxiety?\"].value_counts()\n",
+    "\n",
+    "homens_ansiedade = anxiety_male.get(\"Yes\", 0)\n",
+    "homens_sem_ansiedade = anxiety_male.get(\"No\", 0)\n",
+    "percent_anxie_male_sim = (homens_ansiedade / total_homens_curso) * 100\n",
+    "percent_anxie_male_nao = (homens_sem_ansiedade/ total_homens_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_anxie_male_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_anxie_male_nao:.1f}%\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 29.4%\n",
+      "Não: 70.6%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NÚMERO DE ALUNOS DO TOP 3 CURSOS COM ATAQUE DE PÂNICO:\n",
+    "panic_male = df_filter_course_male[\"Do you have Panic attack?\"].value_counts()\n",
+    "\n",
+    "homens_panico = panic_male.get(\"Yes\", 0)\n",
+    "homens_sem_panico = panic_male.get(\"No\", 0)\n",
+    "percent_panic_male_sim = (homens_panico/ total_homens_curso) * 100\n",
+    "percent_panic_male_nao = (homens_sem_panico/ total_homens_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_panic_male_sim:.1f}%\")\n",
+    "print(f\"Não: {percent_panic_male_nao:.1f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sim: 5.88%\n",
+      "Não: 94.12%\n"
+     ]
+    }
+   ],
+   "source": [
+    "#NÚMERO DE ALUNOS DO TOP 3 CURSOS QUE FAZEM TRATAMENTO\n",
+    "treatment_male = df_filter_course_male[\"Did you seek any specialist for a treatment?\"].value_counts()\n",
+    "\n",
+    "homens_trat = treatment_male.get(\"Yes\", 0)\n",
+    "homens_sem_trat = treatment_male.get(\"No\", 0)\n",
+    "percent_treat_male_sim = (homens_trat/ total_homens_curso) * 100\n",
+    "percent_treat_male_nao = (homens_sem_trat/ total_homens_curso) * 100\n",
+    "\n",
+    "print(f\"Sim: {percent_treat_male_sim:.2f}%\")\n",
+    "print(f\"Não: {percent_treat_male_nao:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "treatment_contagem_male = percent_treat_male_sim\n",
+    "anxiety_contagem_male = percent_anxie_male_sim\n",
+    "depression_contagem_male = percent_male_sim\n",
+    "panic_attack_contagem_male = percent_panic_male_sim\n",
+    "\n",
+    "treatment_contagem_male_nao = percent_treat_male_nao\n",
+    "anxiety_contagem_male_nao = percent_anxie_male_nao\n",
+    "depression_contagem_male_nao = percent_male_nao\n",
+    "panic_attack_contagem_male_nao = percent_panic_male_nao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "categorias = ['Medical Treatment', 'Anxiety', 'Depression', 'Panic Attack']\n",
+    "mulheres = [treatment_contagem, anxiety_contagem, depression_contagem, panic_attack_contagem]\n",
+    "homens = [treatment_contagem_male, anxiety_contagem_male, depression_contagem_male, panic_attack_contagem_male]\n",
+    "largura_barra = 0.3\n",
+    "posicoes = np.arange(len(categorias))\n",
+    "\n",
+    "plt.bar(posicoes - largura_barra/2, mulheres, largura_barra, label='Mulheres', color='#66ffcc', edgecolor = \"black\")\n",
+    "plt.bar(posicoes + largura_barra/2, homens, largura_barra, label='Homens', color='#FF9999', edgecolor = 'black')\n",
+    "plt.xlabel('Categorias')\n",
+    "plt.ylabel('Quantidade de \"Yes\"')\n",
+    "plt.title('Comparação da quantidade de \"Sim\" por gênero em %')\n",
+    "plt.legend()\n",
+    "plt.xticks(posicoes, categorias)\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.8)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "categorias_nao = ['Medical Treatment', 'Anxiety', 'Depression', 'Panic Attack']\n",
+    "mulheres_nao = [treatment_contagem_nao, anxiety_contagem_nao, depression_contagem_nao, panic_attack_contagem_nao]\n",
+    "homens_nao = [treatment_contagem_male_nao, anxiety_contagem_male_nao, depression_contagem_male_nao, panic_attack_contagem_male_nao]\n",
+    "largura_barra = 0.3\n",
+    "posicoes = np.arange(len(categorias_nao))\n",
+    "\n",
+    "plt.bar(posicoes - largura_barra/2, mulheres_nao, largura_barra, label='Mulheres', color='#6600CC', edgecolor = \"black\")\n",
+    "plt.bar(posicoes + largura_barra/2, homens_nao, largura_barra, label='Homens', color='#FFFF00', edgecolor = 'black')\n",
+    "plt.xlabel('Categorias')\n",
+    "plt.ylabel('Quantidade de \"No\"')\n",
+    "plt.title('Comparação da quantidade de \"Não\" por gênero em %')\n",
+    "plt.legend()\n",
+    "plt.xticks(posicoes, categorias_nao)\n",
+    "plt.yticks(np.arange(0, 100, 4))\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**ANALISANDO O DESEMPENHO ACADEMICO DESSAS CATEGORIAS DENTRO DOS CURSOS**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your CGPA?\n",
+      "3.50 - 4.00    55.0\n",
+      "3.00 - 3.49    37.0\n",
+      "2.50 - 2.99     5.0\n",
+      "2.00 - 2.49     3.0\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Desempenho academico mulheres\n",
+    "df_CGPA_female = df_filter_course[\"What is your CGPA?\"].value_counts()\n",
+    "percent = (df_CGPA_female/total_mulheres_curso)*100\n",
+    "print(round(percent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "What is your CGPA?\n",
+      "3.50 - 4.00    55.0\n",
+      "3.00 - 3.49    37.0\n",
+      "2.50 - 2.99     5.0\n",
+      "2.00 - 2.49     3.0\n",
+      "Name: count, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Desempenho academico homens\n",
+    "df_CGPA_male = df_filter_course_male[\"What is your CGPA?\"].value_counts()\n",
+    "percent_male = (df_CGPA_male/total_homens_curso)*100\n",
+    "print(round(percent))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#VISUALIZAÇÃO GRÁFICA\n",
+    "#(eu sei que esse não é o método ideal, já que com dataset com informações maiores\n",
+    "# eu não conseguiria usar, mas eu não consegui ajustar de como melhor)\n",
+    "\n",
+    "cat_CGPA = ['\"3.50 - 4.00\"','\"3.00 - 3.49\"','\"2.00 - 2.49\"','\"0 - 1.99\"']\n",
+    "male_cgpa = [35.0, 53.0, 6.0, 6.0]\n",
+    "female_cgpa = [55.0, 37.0, 5.0, 3.0]\n",
+    "largura_barra_cgpa = 0.35\n",
+    "posicoes = np.arange(len(cat_CGPA))\n",
+    "\n",
+    "plt.bar(posicoes - largura_barra/2, male_cgpa, largura_barra, label='alunos', color='#ffccff', edgecolor = 'maroon')\n",
+    "plt.bar(posicoes + largura_barra/2, female_cgpa, largura_barra, label='alunas', color='#cc0066', edgecolor = 'maroon')\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('porcentagem')\n",
+    "plt.title('Desempenho acadêmico entre alunos e alunas')\n",
+    "plt.xticks(posicoes, cat_CGPA)\n",
+    "plt.legend()\n",
+    "plt.yticks(np.arange(0, 60, 4))\n",
+    "plt.grid(axis='y', linestyle='--', alpha=0.5)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "INSIGHTS\n",
+    "\n",
+    "Pode-se notar que, de acordo com os dados levantados na pesquisa, os alunos possuem mais problemas na saúde mental do que as alunas, contudo, as alunas ainda são as que mais buscam ajuda para realizar o tratamento. Ainda que não seja possível avaliar com certeza, há uma possibilidade desse fato impactar no desempenho academico de todos os estudantes, uma vez que a maioria das alunas possuem um CGPA com nota excelente (3.50-4.00) em comparação aos alunos."
+   ]
+  }
+ ],
+ "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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From 6b0e561cdb8c752396b551cf722429eef4f9dacf Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 2 Nov 2023 20:07:55 -0300
Subject: [PATCH 2/7] Dataset utilizado

O dataset utilizado retirado da plataforma Kaggle.
---
 material/Student Mental health.csv | 102 +++++++++++++++++++++++++++++
 1 file changed, 102 insertions(+)
 create mode 100644 material/Student Mental health.csv

diff --git a/material/Student Mental health.csv b/material/Student Mental health.csv
new file mode 100644
index 0000000..397fb92
--- /dev/null
+++ b/material/Student Mental health.csv	
@@ -0,0 +1,102 @@
+Timestamp,Choose your gender,Age,What is your course?,Your current year of Study,What is your CGPA?,Do you have Depression?,Do you have Anxiety?,Do you have Panic attack?,Did you seek any specialist for a treatment?
+8/7/2020 12:02,Female,18,Engineering,year 1,3.00 - 3.49,Yes,No,Yes,No
+8/7/2020 12:04,Male,21,Islamic Education,year 2,3.00 - 3.49,No,Yes,No,No
+8/7/2020 12:05,Male,19,Information Technology,year 1,3.00 - 3.49,Yes,Yes,Yes,No
+8/7/2020 12:06,Female,22,Laws,year 3,3.00 - 3.49,Yes,Yes,No,No
+8/7/2020 12:13,Male,23,Mathemathics,year 4,3.00 - 3.49,No,No,No,No
+8/7/2020 12:31,Male,19,Engineering,year 2,3.50 - 4.00,No,No,Yes,No
+8/7/2020 12:32,Female,23,Islamic Education,year 2,3.50 - 4.00,Yes,No,Yes,No
+8/7/2020 12:33,Female,18,Computer Science,year 1,3.50 - 4.00,No,Yes,No,No
+8/7/2020 12:35,Female,19,Human Resources,year 2,2.50 - 2.99,No,No,No,No
+8/7/2020 12:39,Male,18,KIRKHS,year 1,3.50 - 4.00,No,Yes,Yes,No
+8/7/2020 12:39,Female,20,Psychology,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 12:39,Female,24,Engineering,year 3,3.50 - 4.00,Yes,No,No,No
+8/7/2020 12:40,Female,18,Computer Science,year 1,3.00 - 3.49,Yes,No,No,No
+8/7/2020 12:41,Male,19,Engineering,year 1,3.00 - 3.49,No,No,No,No
+8/7/2020 12:43,Female,18,KENMS,year 2,3.50 - 4.00,No,Yes,No,No
+8/7/2020 12:43,Male,24,Computer Science,year 3,3.50 - 4.00,No,No,No,No
+8/7/2020 12:46,Female,24,Accounting ,year 3,3.00 - 3.49,No,No,No,No
+8/7/2020 12:52,Female,24,KENMS,year 4,3.00 - 3.49,Yes,Yes,Yes,No
+8/7/2020 13:05,Female,20,Information Technology,year 2,3.50 - 4.00,No,Yes,No,No
+8/7/2020 13:07,Female,18,Marine Science,year 2,3.50 - 4.00,Yes,Yes,Yes,No
+8/7/2020 13:12,Female,19,Engineering,year 1,3.00 - 3.49,No,No,Yes,No
+8/7/2020 13:13,Female,18,Engineering,year 2,3.00 - 3.49,No,No,No,No
+8/7/2020 13:13,Female,24,Computer Science,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 13:15,Female,24,Engineering,year 1,3.00 - 3.49,No,No,No,No
+8/7/2020 13:17,Female,23,Computer Science,year 3,3.50 - 4.00,Yes,Yes,Yes,No
+8/7/2020 13:29,Female,18,Banking Studies,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 13:35,Female,19,Engineering,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 13:41,Male,18,Engineering,Year 2,3.00 - 3.49,Yes,Yes,No,No
+8/7/2020 13:58,Female,24,Information Technology,Year 3,3.50 - 4.00,Yes,Yes,Yes,Yes
+8/7/2020 14:05,Female,24,Computer Science,year 4,3.50 - 4.00,No,No,No,No
+8/7/2020 14:27,Female,23,Business Administration,Year 2,3.00 - 3.49,No,No,No,No
+8/7/2020 14:29,Male,18,Computer Science,year 2,3.00 - 3.49,No,No,No,No
+8/7/2020 14:29,Male,19,Computer Science,year 1,3.50 - 4.00,No,No,Yes,No
+8/7/2020 14:31,Male,18,Computer Science,Year 2,3.50 - 4.00,Yes,Yes,No,Yes
+8/7/2020 14:41,Female,19,Information Technology,year 1,3.00 - 3.49,Yes,Yes,Yes,No
+8/7/2020 14:43,Female,18,Engineering,year 1,2.00 - 2.49,No,No,No,No
+8/7/2020 14:43,Female,18,Laws,Year 3,3.00 - 3.49,Yes,Yes,No,No
+8/7/2020 14:45,Female,19,Information Technology,year 1,2.50 - 2.99,Yes,Yes,Yes,No
+8/7/2020 14:47,Female,18,KIRKHS,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 14:56,Female,24,Engineering,Year 2,2.50 - 2.99,Yes,No,Yes,Yes
+8/7/2020 14:57,Female,24,Information Technology,Year 3,3.00 - 3.49,No,Yes,No,No
+8/7/2020 14:57,Female,22,Engineering,year 4,3.50 - 4.00,No,No,No,No
+8/7/2020 14:58,Female,20,Islamic Revealed Knowledge & Heritage ,year 2,3.00 - 3.49,Yes,No,No,No
+8/7/2020 15:07,Male,,Information Technology,year 1,0 - 1.99,No,No,No,No
+8/7/2020 15:08,Male,23,TAASL,year 2,3.50 - 4.00,No,No,Yes,No
+8/7/2020 15:09,Male,18,Computer Science,year 1,3.50 - 4.00,No,Yes,Yes,No
+8/7/2020 15:12,Female,19,Engineering,year 1,3.50 - 4.00,No,Yes,No,No
+8/7/2020 15:14,Female,18,Engineering,year 4,3.50 - 4.00,No,No,No,No
+8/7/2020 15:14,Male,24,Computer Science,year 2,3.00 - 3.49,Yes,No,No,No
+8/7/2020 15:18,Female,24,Computer Science,year 3,3.50 - 4.00,No,No,Yes,No
+8/7/2020 15:27,Female,23,ALA,year 1,2.50 - 2.99,Yes,No,Yes,Yes
+8/7/2020 15:37,Female,18,Computer Science,year 2,3.50 - 4.00,No,Yes,No,No
+8/7/2020 15:47,Female,19,Biomedical Science,year 3,3.00 - 3.49,No,No,No,No
+8/7/2020 15:48,Female,20,Engineering,year 3,3.00 - 3.49,Yes,Yes,Yes,No
+8/7/2020 15:57,Female,19,Computer Science,year 1,3.50 - 4.00,Yes,No,Yes,Yes
+8/7/2020 15:58,Male,21,Computer Science,year 1,3.00 - 3.49,No,No,No,No
+8/7/2020 16:08,Male,23,KIRKHS,Year 3,3.50 - 4.00,No,No,No,No
+8/7/2020 16:21,Female,20,English and Literature,Year 3,3.00 - 3.49,Yes,Yes,No,No
+8/7/2020 16:22,Female,18,Computer Science,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 16:34,Female,23,English and Literature,year 1,3.00 - 3.49,No,No,No,No
+8/7/2020 16:34,Female,18,Information Technology,Year 3,3.00 - 3.49,No,No,Yes,No
+8/7/2020 16:53,Female,19,Computer Science,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 17:05,Female,18,CTS,Year 1,3.50 - 4.00,No,No,Yes,No
+8/7/2020 17:37,Female,24,Engineering,year 1,3.50 - 4.00,No,No,Yes,No
+8/7/2020 17:46,Female,24,Engineering,year 1,3.50 - 4.00,No,No,No,No
+8/7/2020 17:50,Female,23,Econs,year 1,3.50 - 4.00,Yes,Yes,No,No
+8/7/2020 18:10,Female,18,Engineering,Year 3,3.00 - 3.49,No,Yes,No,No
+8/7/2020 18:11,Male,19,Communication,Year 3,3.00 - 3.49,Yes,No,Yes,No
+8/7/2020 19:05,Female,18,Malcom,year 1,3.50 - 4.00,Yes,No,No,No
+8/7/2020 19:32,Female,24,Pharmacy,year 4,3.00 - 3.49,No,Yes,No,No
+8/7/2020 20:36,Female,24,Biomedical science,year 1,3.00 - 3.49,No,No,No,No
+8/7/2020 21:21,Female,18,Laws,year 3,3.50 - 4.00,No,No,Yes,No
+8/7/2020 22:35,Female,19,Information Technology,year 3,3.00 - 3.49,Yes,No,No,No
+9/7/2020 6:57,Male,18,Biomedical science,year 1,0 - 1.99,No,No,No,No
+9/7/2020 11:43,Male,24,Information Technology,year 3,3.50 - 4.00,No,Yes,No,No
+9/7/2020 11:57,Female,24,Engineering,year 1,3.50 - 4.00,No,Yes,Yes,No
+9/7/2020 13:15,Female,23,Engineering,year 1,3.00 - 3.49,Yes,No,No,No
+9/7/2020 18:24,Female,18,Human Sciences ,year 2,3.00 - 3.49,No,No,Yes,No
+13/07/2020 10:07:32,Female,19,Biotechnology,year 3,0 - 1.99,No,No,No,No
+13/07/2020 10:10:30,Female,18,Engineering,year 4,3.50 - 4.00,No,No,No,No
+13/07/2020 10:11:26,Female,24,Communication ,year 2,3.50 - 4.00,Yes,Yes,Yes,No
+13/07/2020 10:12:18,Female,24,Nursing,year 2,3.50 - 4.00,No,No,No,No
+13/07/2020 10:12:26,Female,19,Engineering,year 1,3.00 - 3.49,Yes,Yes,No,No
+13/07/2020 10:12:28,Female,19,Islamic Education ,year 2,3.00 - 3.49,No,No,No,No
+13/07/2020 10:14:46,Male,23,Radiography,year 1,3.00 - 3.49,No,No,No,No
+13/07/2020 10:33:47,Female,18,Psychology,year 1,3.50 - 4.00,Yes,Yes,No,Yes
+13/07/2020 10:34:08,Female,19,Islamic Jurisprudence ,year 3,3.00 - 3.49,No,No,No,No
+13/07/2020 11:46:13,Female,18,Psychology,year 1,3.50 - 4.00,Yes,Yes,Yes,No
+13/07/2020 11:49:02,Male,24,Information Technology,year 1,3.00 - 3.49,No,Yes,No,No
+13/07/2020 11:54:58,Male,24,Engineering,year 2,2.00 - 2.49,No,No,Yes,No
+13/07/2020 13:57:11,Female,23,TESL,year 3,3.50 - 4.00,No,No,Yes,No
+13/07/2020 14:38:12,Male,18,Engineering,year 2,3.00 - 3.49,No,Yes,No,No
+13/07/2020 14:48:05,Female,19,Engineering,year 2,3.00 - 3.49,Yes,No,No,No
+13/07/2020 16:15:13,Female,18,English and Literature,year 1,3.00 - 3.49,Yes,No,No,No
+13/07/2020 17:30:44,Female,24,Islamic Jurisprudence,year 3,0 - 1.99,No,No,Yes,No
+13/07/2020 19:08:32,Female,18,Islamic Education,year 1,3.50 - 4.00,No,No,No,No
+13/07/2020 19:56:49,Female,21,Computer Science,year 1,3.50 - 4.00,No,Yes,No,No
+13/07/2020 21:21:42,Male,18,Engineering,year 2,3.00 - 3.49,Yes,Yes,No,No
+13/07/2020 21:22:56,Female,19,Nursing ,year 3,3.50 - 4.00,Yes,No,Yes,No
+13/07/2020 21:23:57,Female,23,Islamic Education,year 4,3.50 - 4.00,No,No,No,No
+18/07/2020 20:16:21,Male,20,Biomedical Science,year 2,3.00 - 3.49,No,No,No,No

From 7666a54cfa354c5ec272d7939b7a8e1dc3efeb61 Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Wed, 13 Mar 2024 10:46:53 -0300
Subject: [PATCH 3/7] Update README.md

---
 README.md | 68 ++++++++++++-------------------------------------------
 1 file changed, 14 insertions(+), 54 deletions(-)

diff --git a/README.md b/README.md
index 3becf42..b816dca 100644
--- a/README.md
+++ b/README.md
@@ -2,67 +2,27 @@
   <img src="assets/reprograma-fundos-claros.png" alt="logo reprograma" width="500">
 </h1>
 
-# Tema da Aula
+# Projeto Guiado III - Saúde mental de estudantes universitários
 
 Turma Online 26 |Python | Semana 10 | 2023 | Professora Edilene Lopes
 
-### Instruções
-Antes de começar, vamos organizar nosso setup.
-* Fork esse repositório 
-* Clone o fork na sua máquina (Para isso basta abrir o seu terminal e digitar `git clone url-do-seu-repositorio-forkado`)
-* Entre na pasta do seu repositório (Para isso basta abrir o seu terminal e digitar `cd nome-do-seu-repositorio-forkado`)
-* [Add outras intrucoes caso necessario]
+### Breve História dos dados
+Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos.
 
-### Resumo
-O que veremos na aula de hoje?
-* [Apresentação de dados](#tema1)
+### Base de dados
+Os dados foram retirados da Plataforma Kaggle
 
+### Objetivo
+Praticar a análise de dados utilizando os recursos do Python e de suas bibliotecas.
 
+### Seguimento da Análise de Dados
+Os dados vão ser analisados e filtrados buscando entender, sobretudo, como se encontra a saúde mental das mulheres nessa Universidade em comparação a saúde mental dos homens.
 
-## Conteúdo
-### Tema1 
-1. [Tópico 1](#topico1)
+# Código e Análise de dados
+[Saúde Mental de Estudantes Universitários](https://github.com/NallaCloviel/on26-python-s13-projeto-guiado-III/blob/main/exercicios/para-casa/ProjetoGuiado-S13.ipynb)
 
-### Tema2 
-1. [Projeto Guiado](#topico3)
-   * [Subtópico 1](#subtopico1)
-   
-   
+## Insights da Análise
+Pode-se notar que, de acordo com os dados levantados na pesquisa, os alunos possuem mais problemas na saúde mental do que as alunas, contudo, as alunas ainda são as que mais buscam ajuda para realizar o tratamento. Ainda que não seja possível avaliar com certeza, há uma possibilidade desse fato impactar no desempenho academico de todos os estudantes, uma vez que a maioria das alunas possuem um CGPA com nota excelente (3.50-4.00) em comparação aos alunos.
 
 
-### Tema1 
-
-#### Topico1
-
-A apresentação de dados com Python e Pandas é a prática de importar, limpar, explorar e analisar dados de forma eficaz. A biblioteca Pandas permite a manipulação de dados, incluindo limpeza, resumo estatístico e agrupamento. Em conjunto com bibliotecas de visualização, como Matplotlib. Python e Pandas são ferramentas poderosas para transformar dados em informações compreensíveis e tomar decisões embasadas em informações.
-
- 
-
-### Tema2
-
-##### Subtopico1
-Realizar a análise de um conjunto de dados específico. Isso pode envolver a importação, limpeza e tratamento de dados. Assim como resumo estatístico.
-A aplicação de técnicas de manipulação de dados com Pandas. Você será desafiada a criar gráficos e visualizações a partir dos dados. Com o uso da biblioteca de visualização Matplotlib. 
-Bem como a interpretação dos gráficos e a formulação de conclusões com base nos dados analisados
-
-
-
-
-
-### Exercícios 
-* [Exercicio para sala](https://github.com/mflilian/repo-example/tree/main/exercicios/para-sala)
-* [Exercicio para casa](https://github.com/mflilian/repo-example/tree/main/exercicios/para-casa)
-
-### Material da aula 
-
-### Links Úteis
-- [Lorem Ipsum](https://www.lipsum.com/feed/html)
-- [Lorem Ipsum](https://www.lipsum.com/feed/html)
-- [Lorem Ipsum](https://www.lipsum.com/feed/html)
-- [Lorem Ipsum](https://www.lipsum.com/feed/html)
-
-
-<p align="center">
-Desenvolvido com :purple_heart:  
-</p>
-
+#### Desenvolvido por Nargylla Cloviel

From bceccca82810debfa1d42a102f9f1238e0967ece Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 21 Mar 2024 11:29:14 -0300
Subject: [PATCH 4/7] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index b816dca..7981a1f 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@ Turma Online 26 |Python | Semana 10 | 2023 | Professora Edilene Lopes
 ### Breve História dos dados
 Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos.
 
+[<img>](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)
+
 ### Base de dados
 Os dados foram retirados da Plataforma Kaggle
 

From 5879d7cbe510205b656a54aec839b4240bd25c7f Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 21 Mar 2024 11:29:36 -0300
Subject: [PATCH 5/7] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7981a1f..35f2832 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ Turma Online 26 |Python | Semana 10 | 2023 | Professora Edilene Lopes
 ### Breve História dos dados
 Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos.
 
-[<img>](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)
+[[<img>](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)
 
 ### Base de dados
 Os dados foram retirados da Plataforma Kaggle

From b48f0642ae4af5a0e2e183ded56eed8a34a06e1f Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 21 Mar 2024 11:34:14 -0300
Subject: [PATCH 6/7] Update README.md

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 35f2832..49cd6e7 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,9 @@ Turma Online 26 |Python | Semana 10 | 2023 | Professora Edilene Lopes
 ### Breve História dos dados
 Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos.
 
-[[<img>](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)](https://thumbs.jusbr.com/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016)
+<p align="center">
+ <img style="border-radius: 50%;" src = "https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.jusbrasil.com.br%2Fnoticias%2Fsaude-mental-de-universitarios-pernambucanos-preocupa-professores%2F504456338&psig=AOvVaw1YIhp3-nL9hEbUnZoK5ihM&ust=1711117636821000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCHkYnIhYUDFQAAAAAdAAAAABAE" width="700px;"/>
+</p>
 
 ### Base de dados
 Os dados foram retirados da Plataforma Kaggle

From 9df83e66b992ecba34c006709278adea7d8eb725 Mon Sep 17 00:00:00 2001
From: Nalla Cloviel <nargyllacloviel@gmail.com>
Date: Thu, 21 Mar 2024 11:35:01 -0300
Subject: [PATCH 7/7] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 49cd6e7..1d1d06a 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ Turma Online 26 |Python | Semana 10 | 2023 | Professora Edilene Lopes
 Dados coletados de alunos da Universidade Islâmica Internacional da Malásia através da plataforma Google Forms em 2020. A pesquisa contou com a participação de 101 alunos de diversas áreas. Tendo em vista que, nesse ano havia 26.000 alunos matrículados, a pesquisa é uma amostra de, aproximadamente, 0,39% de alunos.
 
 <p align="center">
- <img style="border-radius: 50%;" src = "https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.jusbrasil.com.br%2Fnoticias%2Fsaude-mental-de-universitarios-pernambucanos-preocupa-professores%2F504456338&psig=AOvVaw1YIhp3-nL9hEbUnZoK5ihM&ust=1711117636821000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCHkYnIhYUDFQAAAAAdAAAAABAE" width="700px;"/>
+ <img style="border-radius: 50%;" src = "https://thumbs.jusbr.com/filters:format(webp)/imgs.jusbr.com/publications/images/7d91f77fdfe06de77375d16037a23016" width="700px;"/>
 </p>
 
 ### Base de dados