diff --git a/Analia - projetoPT1/ProjetoPT1.ipynb b/Analia - projetoPT1/ProjetoPT1.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identidade de Gênero\n",
+    "data_genero = {\n",
+    "    'Identidade de Gênero': ['Homem transexual', 'Mulher transexual', 'Travesti', 'Total'],\n",
+    "    'Percentual (%)': [34.7, 36.7, 28.6, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_genero = pd.DataFrame(data_genero)\n",
+    "\n",
+    "# Faixa etária dos entrevistados\n",
+    "data_faixa_etaria = {\n",
+    "    'Faixa Etária': ['15 a 19 anos', '20 a 24 anos', '25 a 29 anos', '30 a 39 anos', \n",
+    "                     '40 a 49 anos', '50 a 59 anos', '60 a 69 anos', 'Total'],\n",
+    "    'Percentual (%)': [15.6, 27.9, 24.5, 18.4, 7.5, 5.4, 0.7, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_faixa_etaria = pd.DataFrame(data_faixa_etaria)\n",
+    "\n",
+    "# Orientação Sexual\n",
+    "data_orientacao_sexual = {\n",
+    "    'Orientação Sexual': ['Heterossexual', 'Bissexual', 'Homossexual', 'Pansexual', 'Demissexual', \n",
+    "                          'Polissexual', 'Não sabe', 'Não respondeu'],\n",
+    "    'Percentual (%)': [71.4, 11.6, 9.5, 3.4, 0.7, 0.7, 2.0, 0.7]\n",
+    "}\n",
+    "\n",
+    "df_orientacao_sexual = pd.DataFrame(data_orientacao_sexual)\n",
+    "\n",
+    "# Correção de Nome e Gênero no Registro Civil\n",
+    "data_correcao_registro = {\n",
+    "    'Situação': ['Não fez o pedido de correção em registro civil', 'Aguardando julgamento', \n",
+    "                 'Não conhece o processo de alteração em registro civil', 'Sim, de nome e gênero', \n",
+    "                 'Sim, de nome', 'Não, julgamento indeferido', 'Total'],\n",
+    "    'Percentual (%)': [67.3, 12.9, 8.8, 7.5, 2.0, 1.4, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_correcao_registro = pd.DataFrame(data_correcao_registro)\n",
+    "\n",
+    "# Idade que se percebeu pessoa Trans\n",
+    "data_idade_percepcao_trans = {\n",
+    "    'Faixa Etária': ['0 a 4 anos', '5 a 9 anos', '10 a 14 anos', '15 a 19 anos', \n",
+    "                     '20 a 24 anos', '25 a 29 anos', '30 a 34 anos', '35 a 39 anos', 'Mais de 40 anos', 'Total'],\n",
+    "    'Percentual (%)': [4.8, 15.1, 24.0, 34.2, 13.0, 5.5, 2.1, 0.7, 0.7, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_idade_percepcao_trans = pd.DataFrame(data_idade_percepcao_trans)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame - Identidade de Gênero:\n",
+      "  Identidade de Gênero  Percentual (%)\n",
+      "0     Homem transexual            34.7\n",
+      "1    Mulher transexual            36.7\n",
+      "2             Travesti            28.6\n",
+      "3                Total           100.0\n",
+      "\n",
+      "DataFrame - Faixa Etária dos Entrevistados:\n",
+      "   Faixa Etária  Percentual (%)\n",
+      "0  15 a 19 anos            15.6\n",
+      "1  20 a 24 anos            27.9\n",
+      "2  25 a 29 anos            24.5\n",
+      "3  30 a 39 anos            18.4\n",
+      "4  40 a 49 anos             7.5\n",
+      "5  50 a 59 anos             5.4\n",
+      "6  60 a 69 anos             0.7\n",
+      "7         Total           100.0\n",
+      "DataFrame - Orientação Sexual:\n",
+      "  Orientação Sexual  Percentual (%)\n",
+      "0     Heterossexual            71.4\n",
+      "1         Bissexual            11.6\n",
+      "2       Homossexual             9.5\n",
+      "3         Pansexual             3.4\n",
+      "4       Demissexual             0.7\n",
+      "5       Polissexual             0.7\n",
+      "6          Não sabe             2.0\n",
+      "7     Não respondeu             0.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exibindo os DataFrames\n",
+    "print(\"DataFrame - Identidade de Gênero:\")\n",
+    "print(df_genero)\n",
+    "\n",
+    "print(\"\\nDataFrame - Faixa Etária dos Entrevistados:\")\n",
+    "print(df_faixa_etaria)\n",
+    "\n",
+    "print(\"DataFrame - Orientação Sexual:\")\n",
+    "print(df_orientacao_sexual)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Correção de Nome e Gênero no Registro Civil:\n",
+      "                                            Situação  Percentual (%)\n",
+      "0     Não fez o pedido de correção em registro civil            67.3\n",
+      "1                              Aguardando julgamento            12.9\n",
+      "2  Não conhece o processo de alteração em registr...             8.8\n",
+      "3                              Sim, de nome e gênero             7.5\n",
+      "4                                       Sim, de nome             2.0\n",
+      "5                         Não, julgamento indeferido             1.4\n",
+      "6                                              Total           100.0\n",
+      "\n",
+      "DataFrame - Idade que se Percebeu Pessoa Trans:\n",
+      "      Faixa Etária  Percentual (%)\n",
+      "0       0 a 4 anos             4.8\n",
+      "1       5 a 9 anos            15.1\n",
+      "2     10 a 14 anos            24.0\n",
+      "3     15 a 19 anos            34.2\n",
+      "4     20 a 24 anos            13.0\n",
+      "5     25 a 29 anos             5.5\n",
+      "6     30 a 34 anos             2.1\n",
+      "7     35 a 39 anos             0.7\n",
+      "8  Mais de 40 anos             0.7\n",
+      "9            Total           100.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Correção de Nome e Gênero no Registro Civil:\")\n",
+    "print(df_correcao_registro)\n",
+    "\n",
+    "print(\"\\nDataFrame - Idade que se Percebeu Pessoa Trans:\")\n",
+    "print(df_idade_percepcao_trans)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Faixa Etária que Interrompeu os Estudos\n",
+    "data_interrupcao_estudos = {\n",
+    "    'Faixa Etária': ['10 a 14 anos', '15 a 17 anos', '18 a 24 anos', 'Mais de 24 anos', 'Não sabe', 'Não respondeu', 'Total'],\n",
+    "    'Percentual (%)': [6, 34, 32, 25, 2, 1, 100]\n",
+    "}\n",
+    "\n",
+    "df_interrupcao_estudos = pd.DataFrame(data_interrupcao_estudos)\n",
+    "\n",
+    "# Abandono Escolar\n",
+    "data_abandono_escolar = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Não respondeu', 'Total'],\n",
+    "    'Frequência': [81, 64, 1, 146],\n",
+    "    'Percentual (%)': [55.5, 43.8, 0.7, None]\n",
+    "}\n",
+    "\n",
+    "df_abandono_escolar = pd.DataFrame(data_abandono_escolar)\n",
+    "\n",
+    "# Motivo do Abandono Escolar\n",
+    "data_motivo_abandono = {\n",
+    "    'Motivo': ['Outras', 'Suspensão ou advertência da escola', 'Não ter vaga na escola pública', \n",
+    "               'Influência dos amigos/indisciplina', 'Envolvimento com drogas', 'Escola era longe de casa', \n",
+    "               'Mudou de cidade ou estado', 'Conflitos no espaço escolar', 'Problemas familiares', \n",
+    "               'Problemas de saúde/acidente', 'Falta de apoio da família', 'Precisava trabalhar', \n",
+    "               'Falta de interesse em estudar', 'Sofreu preconceito/agressão na escola'],\n",
+    "    'Percentual (%)': [2.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 4.0, 4.0, 5.1, 5.1, 18.2, 19.2, 31.3]\n",
+    "}\n",
+    "\n",
+    "df_motivo_abandono = pd.DataFrame(data_motivo_abandono)\n",
+    "\n",
+    "# Preconceito ou Agressão na Escola\n",
+    "data_preconceito_escolar = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Total'],\n",
+    "    'Frequência': [92, 54, 146],\n",
+    "    'Percentual (%)': [63.0, 37.0, None]\n",
+    "}\n",
+    "\n",
+    "df_preconceito_escolar = pd.DataFrame(data_preconceito_escolar)\n",
+    "\n",
+    "# Melhorias nas Escolas\n",
+    "data_melhorias_escola = {\n",
+    "    'Melhoria': ['Outras', 'Gestão Participativa', 'Valorização dos Professores', \n",
+    "                 'Uso do banheiro e nome social', 'Formação sobre gênero e sexualidade', \n",
+    "                 'Ampliação de investimentos', 'Inclusão da discussão sobre direitos humanos', \n",
+    "                 'Melhoria do ensino e currículo', 'Mais respeito à orientação sexual e identidade de gênero'],\n",
+    "    'Frequência': [43, 5, 6, 16, 34, 35, 45, 51, 61],\n",
+    "    'Total de Respostas': [296] * 9\n",
+    "}\n",
+    "\n",
+    "df_melhorias_escola = pd.DataFrame(data_melhorias_escola)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Faixa Etária que Interrompeu os Estudos:\n",
+      "      Faixa Etária  Percentual (%)\n",
+      "0     10 a 14 anos               6\n",
+      "1     15 a 17 anos              34\n",
+      "2     18 a 24 anos              32\n",
+      "3  Mais de 24 anos              25\n",
+      "4         Não sabe               2\n",
+      "5    Não respondeu               1\n",
+      "6            Total             100\n",
+      "DataFrame - Abandono Escolar:\n",
+      "        Resposta  Frequência  Percentual (%)\n",
+      "0            Sim          81            55.5\n",
+      "1            Não          64            43.8\n",
+      "2  Não respondeu           1             0.7\n",
+      "3          Total         146             NaN\n",
+      "\n",
+      "DataFrame - Motivo do Abandono Escolar:\n",
+      "                                   Motivo  Percentual (%)\n",
+      "0                                  Outras             2.0\n",
+      "1      Suspensão ou advertência da escola             1.0\n",
+      "2          Não ter vaga na escola pública             1.0\n",
+      "3      Influência dos amigos/indisciplina             2.0\n",
+      "4                 Envolvimento com drogas             2.0\n",
+      "5                Escola era longe de casa             2.0\n",
+      "6               Mudou de cidade ou estado             3.0\n",
+      "7             Conflitos no espaço escolar             4.0\n",
+      "8                    Problemas familiares             4.0\n",
+      "9             Problemas de saúde/acidente             5.1\n",
+      "10              Falta de apoio da família             5.1\n",
+      "11                    Precisava trabalhar            18.2\n",
+      "12          Falta de interesse em estudar            19.2\n",
+      "13  Sofreu preconceito/agressão na escola            31.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Faixa Etária que Interrompeu os Estudos:\")\n",
+    "print(df_interrupcao_estudos)\n",
+    "\n",
+    "print(\"DataFrame - Abandono Escolar:\")\n",
+    "print(df_abandono_escolar)\n",
+    "\n",
+    "print(\"\\nDataFrame - Motivo do Abandono Escolar:\")\n",
+    "print(df_motivo_abandono)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Preconceito ou Agressão na Escola:\n",
+      "  Resposta  Frequência  Percentual (%)\n",
+      "0      Sim          92            63.0\n",
+      "1      Não          54            37.0\n",
+      "2    Total         146             NaN\n",
+      "\n",
+      "DataFrame - Melhorias nas Escolas:\n",
+      "                                            Melhoria  Frequência  \\\n",
+      "0                                             Outras          43   \n",
+      "1                               Gestão Participativa           5   \n",
+      "2                        Valorização dos Professores           6   \n",
+      "3                      Uso do banheiro e nome social          16   \n",
+      "4                Formação sobre gênero e sexualidade          34   \n",
+      "5                         Ampliação de investimentos          35   \n",
+      "6       Inclusão da discussão sobre direitos humanos          45   \n",
+      "7                     Melhoria do ensino e currículo          51   \n",
+      "8  Mais respeito à orientação sexual e identidade...          61   \n",
+      "\n",
+      "   Total de Respostas  \n",
+      "0                 296  \n",
+      "1                 296  \n",
+      "2                 296  \n",
+      "3                 296  \n",
+      "4                 296  \n",
+      "5                 296  \n",
+      "6                 296  \n",
+      "7                 296  \n",
+      "8                 296  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Preconceito ou Agressão na Escola:\")\n",
+    "print(df_preconceito_escolar)\n",
+    "\n",
+    "print(\"\\nDataFrame - Melhorias nas Escolas:\")\n",
+    "print(df_melhorias_escola)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preconceito na Família\n",
+    "data_preconceito_familia = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Não sabe', 'Total'],\n",
+    "    'Frequência': [93, 53, 1, 147],\n",
+    "    'Percentual (%)': [63.3, 36.1, 0.7, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_preconceito_familia = pd.DataFrame(data_preconceito_familia)\n",
+    "\n",
+    "# Violência na Família\n",
+    "data_violencia_familia = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Total'],\n",
+    "    'Frequência': [64, 83, 147],\n",
+    "    'Percentual (%)': [43.5, 56.5, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_violencia_familia = pd.DataFrame(data_violencia_familia)\n",
+    "\n",
+    "# Tipos de Violência na Família\n",
+    "data_tipos_violencia = {\n",
+    "    'Tipo de Violência': ['Violência Psicológica/Verbal', 'Violência Física', 'Violência Sexual'],\n",
+    "    'Total de Respostas': [38, 36, 10],\n",
+    "    'Percentual (%)': [45.2, 42.9, 11.9]\n",
+    "}\n",
+    "\n",
+    "df_tipos_violencia = pd.DataFrame(data_tipos_violencia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Preconceito na Família:\n",
+      "   Resposta  Frequência  Percentual (%)\n",
+      "0       Sim          93            63.3\n",
+      "1       Não          53            36.1\n",
+      "2  Não sabe           1             0.7\n",
+      "3     Total         147           100.0\n",
+      "\n",
+      "DataFrame - Violência na Família:\n",
+      "  Resposta  Frequência  Percentual (%)\n",
+      "0      Sim          64            43.5\n",
+      "1      Não          83            56.5\n",
+      "2    Total         147           100.0\n",
+      "\n",
+      "DataFrame - Tipos de Violência na Família:\n",
+      "              Tipo de Violência  Total de Respostas  Percentual (%)\n",
+      "0  Violência Psicológica/Verbal                  38            45.2\n",
+      "1              Violência Física                  36            42.9\n",
+      "2              Violência Sexual                  10            11.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Preconceito na Família:\")\n",
+    "print(df_preconceito_familia)\n",
+    "\n",
+    "print(\"\\nDataFrame - Violência na Família:\")\n",
+    "print(df_violencia_familia)\n",
+    "\n",
+    "print(\"\\nDataFrame - Tipos de Violência na Família:\")\n",
+    "print(df_tipos_violencia)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Atividades ou Trabalho Remunerado\n",
+    "data_atividades_trabalho = {\n",
+    "    'Atividade Principal': ['Outros', 'Profissional da saúde', 'Pesquisador(a)', 'Marketing/Publicidade', \n",
+    "                            'Envolvimento em atividades ilícitas (Tráfico de drogas)', 'Servidor(a) Público(a)', \n",
+    "                            'Cuidador(a)', 'Agente de prevenção/educador(a) social', 'Profissional de administração', \n",
+    "                            'Artista', 'Professor(a)/instrutor(a)', 'Profissional de comércio e vendas', \n",
+    "                            'Profissional de Culinária/Gastronomia', 'Profissional de estética e beleza', \n",
+    "                            'Profissional do sexo/Acompanhante'],\n",
+    "    'Percentual (%)': [8.8, 2.0, 2.0, 2.0, 2.0, 2.9, 2.9, 2.9, 3.9, 3.9, 4.9, 6.9, 8.8, 17.6, 28.4]\n",
+    "}\n",
+    "\n",
+    "df_atividades_trabalho = pd.DataFrame(data_atividades_trabalho)\n",
+    "\n",
+    "# Preconceito no Trabalho\n",
+    "data_preconceito_trabalho = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Não sabe', 'Não respondeu', 'Total'],\n",
+    "    'Frequência': [57, 87, 1, 1, 146],\n",
+    "    'Percentual (%)': [39.0, 59.6, 0.7, 0.7, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_preconceito_trabalho = pd.DataFrame(data_preconceito_trabalho)\n",
+    "\n",
+    "# Idade de Início como Profissional do Sexo\n",
+    "data_idade_profissional_sexo = {\n",
+    "    'Idade': [12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 25, 28, 29, 32, 34],\n",
+    "    'Percentual (%)': [4.0, 4.0, 8.0, 18.0, 14.0, 14.0, 6.0, 8.0, 8.0, 2.0, 2.0, 4.0, 2.0, 2.0, 2.0, 2.0]\n",
+    "}\n",
+    "\n",
+    "df_idade_profissional_sexo = pd.DataFrame(data_idade_profissional_sexo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DataFrame - Atividades ou Trabalho Remunerado:\n",
+      "                                  Atividade Principal  Percentual (%)\n",
+      "0                                              Outros             8.8\n",
+      "1                               Profissional da saúde             2.0\n",
+      "2                                      Pesquisador(a)             2.0\n",
+      "3                               Marketing/Publicidade             2.0\n",
+      "4   Envolvimento em atividades ilícitas (Tráfico d...             2.0\n",
+      "5                              Servidor(a) Público(a)             2.9\n",
+      "6                                         Cuidador(a)             2.9\n",
+      "7              Agente de prevenção/educador(a) social             2.9\n",
+      "8                       Profissional de administração             3.9\n",
+      "9                                             Artista             3.9\n",
+      "10                          Professor(a)/instrutor(a)             4.9\n",
+      "11                  Profissional de comércio e vendas             6.9\n",
+      "12              Profissional de Culinária/Gastronomia             8.8\n",
+      "13                  Profissional de estética e beleza            17.6\n",
+      "14                  Profissional do sexo/Acompanhante            28.4\n",
+      "\n",
+      "DataFrame - Preconceito no Trabalho:\n",
+      "        Resposta  Frequência  Percentual (%)\n",
+      "0            Sim          57            39.0\n",
+      "1            Não          87            59.6\n",
+      "2       Não sabe           1             0.7\n",
+      "3  Não respondeu           1             0.7\n",
+      "4          Total         146           100.0\n",
+      "\n",
+      "DataFrame - Idade de Início como Profissional do Sexo:\n",
+      "    Idade  Percentual (%)\n",
+      "0      12             4.0\n",
+      "1      13             4.0\n",
+      "2      14             8.0\n",
+      "3      15            18.0\n",
+      "4      16            14.0\n",
+      "5      17            14.0\n",
+      "6      18             6.0\n",
+      "7      19             8.0\n",
+      "8      20             8.0\n",
+      "9      22             2.0\n",
+      "10     24             2.0\n",
+      "11     25             4.0\n",
+      "12     28             2.0\n",
+      "13     29             2.0\n",
+      "14     32             2.0\n",
+      "15     34             2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"DataFrame - Atividades ou Trabalho Remunerado:\")\n",
+    "print(df_atividades_trabalho)\n",
+    "\n",
+    "print(\"\\nDataFrame - Preconceito no Trabalho:\")\n",
+    "print(df_preconceito_trabalho)\n",
+    "\n",
+    "print(\"\\nDataFrame - Idade de Início como Profissional do Sexo:\")\n",
+    "print(df_idade_profissional_sexo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Respeito ao Nome Social e Identidade de Gênero nos Serviços de Saúde\n",
+    "data_respeito_saude = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Não sabe', 'Não respondeu', 'Total'],\n",
+    "    'Percentual (%)': [59.1, 34.1, 5.3, 1.5, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_respeito_saude = pd.DataFrame(data_respeito_saude)\n",
+    "\n",
+    "# Etapas onde não houve Respeito\n",
+    "data_etapas_nao_respeitadas = {\n",
+    "    'Etapa': ['Não respondeu', 'No plano de saúde', 'Triagem/Chamada', 'Consulta', 'Em todas as etapas', 'Acolhimento/cadastro'],\n",
+    "    'Percentual (%)': [3.2, 3.2, 9.7, 19.4, 24.2, 40.3]\n",
+    "}\n",
+    "\n",
+    "df_etapas_nao_respeitadas = pd.DataFrame(data_etapas_nao_respeitadas)\n",
+    "\n",
+    "# Idade do Primeiro Uso de Hormônios\n",
+    "data_primeiro_uso_hormonios = {\n",
+    "    'Idade': ['10 a 14 anos', '15 a 19 anos', '20 a 24 anos', '25 a 29 anos', '30 a 34 anos', 'Mais de 35 anos'],\n",
+    "    'Percentual (%)': [18.3, 47.8, 22.6, 7.8, 2.6, 0.9]\n",
+    "}\n",
+    "\n",
+    "df_primeiro_uso_hormonios = pd.DataFrame(data_primeiro_uso_hormonios)\n",
+    "\n",
+    "# Acompanhamento Médico para Hormonioterapia\n",
+    "data_acompanhamento_hormonioterapia = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Total'],\n",
+    "    'Frequência': [26, 89, 115],\n",
+    "    'Percentual (%)': [22.6, 77.4, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_acompanhamento_hormonioterapia = pd.DataFrame(data_acompanhamento_hormonioterapia)\n",
+    "\n",
+    "# Tentativa de Suicídio\n",
+    "data_tentativa_suicidio = {\n",
+    "    'Resposta': ['Sim', 'Não', 'Não respondeu', 'Total'],\n",
+    "    'Frequência': [44, 39, 1, 84],\n",
+    "    'Percentual (%)': [52.4, 46.4, 1.2, 100.0]\n",
+    "}\n",
+    "\n",
+    "df_tentativa_suicidio = pd.DataFrame(data_tentativa_suicidio)\n",
+    "\n",
+    "# Melhoria dos Serviços de Atendimento à Saúde\n",
+    "data_melhoria_atendimento_saude = {\n",
+    "    'Medidas Importantes': ['Outros', 'Orientação e informação à população trans', 'Disponibilizar medicação e liberar cirurgias', \n",
+    "                            'Maior agilidade e eficiência', 'Melhoria do atendimento', 'Ampliação dos profissionais de saúde', \n",
+    "                            'Capacitação dos profissionais de saúde para atendimento às pessoas trans', 'Respeito/serviço mais humanizado', \n",
+    "                            'Ampliação e melhoria em infra estrutura e serviços'],\n",
+    "    'Percentual (%)': [3.8, 3.1, 4.8, 10.0, 10.3, 12.4, 15.9, 17.6, 22.1]\n",
+    "}\n",
+    "\n",
+    "df_melhoria_atendimento_saude = pd.DataFrame(data_melhoria_atendimento_saude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Respeito ao Nome Social e Identidade de Gênero nos Serviços de Saúde:\n",
+      "        Resposta  Percentual (%)\n",
+      "0            Sim            59.1\n",
+      "1            Não            34.1\n",
+      "2       Não sabe             5.3\n",
+      "3  Não respondeu             1.5\n",
+      "4          Total           100.0\n",
+      "\n",
+      "DataFrame - Etapas onde não houve Respeito:\n",
+      "                  Etapa  Percentual (%)\n",
+      "0         Não respondeu             3.2\n",
+      "1     No plano de saúde             3.2\n",
+      "2       Triagem/Chamada             9.7\n",
+      "3              Consulta            19.4\n",
+      "4    Em todas as etapas            24.2\n",
+      "5  Acolhimento/cadastro            40.3\n",
+      "DataFrame - Idade do Primeiro Uso de Hormônios:\n",
+      "             Idade  Percentual (%)\n",
+      "0     10 a 14 anos            18.3\n",
+      "1     15 a 19 anos            47.8\n",
+      "2     20 a 24 anos            22.6\n",
+      "3     25 a 29 anos             7.8\n",
+      "4     30 a 34 anos             2.6\n",
+      "5  Mais de 35 anos             0.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Respeito ao Nome Social e Identidade de Gênero nos Serviços de Saúde:\")\n",
+    "print(df_respeito_saude)\n",
+    "\n",
+    "print(\"\\nDataFrame - Etapas onde não houve Respeito:\")\n",
+    "print(df_etapas_nao_respeitadas)\n",
+    "\n",
+    "print(\"DataFrame - Idade do Primeiro Uso de Hormônios:\")\n",
+    "print(df_primeiro_uso_hormonios)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "DataFrame - Acompanhamento Médico para Hormonioterapia:\n",
+      "  Resposta  Frequência  Percentual (%)\n",
+      "0      Sim          26            22.6\n",
+      "1      Não          89            77.4\n",
+      "2    Total         115           100.0\n",
+      "\n",
+      "DataFrame - Tentativa de Suicídio:\n",
+      "        Resposta  Frequência  Percentual (%)\n",
+      "0            Sim          44            52.4\n",
+      "1            Não          39            46.4\n",
+      "2  Não respondeu           1             1.2\n",
+      "3          Total          84           100.0\n",
+      "\n",
+      "DataFrame - Melhoria dos Serviços de Atendimento à Saúde:\n",
+      "                                 Medidas Importantes  Percentual (%)\n",
+      "0                                             Outros             3.8\n",
+      "1          Orientação e informação à população trans             3.1\n",
+      "2       Disponibilizar medicação e liberar cirurgias             4.8\n",
+      "3                       Maior agilidade e eficiência            10.0\n",
+      "4                            Melhoria do atendimento            10.3\n",
+      "5               Ampliação dos profissionais de saúde            12.4\n",
+      "6  Capacitação dos profissionais de saúde para at...            15.9\n",
+      "7                   Respeito/serviço mais humanizado            17.6\n",
+      "8  Ampliação e melhoria em infra estrutura e serv...            22.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nDataFrame - Acompanhamento Médico para Hormonioterapia:\")\n",
+    "print(df_acompanhamento_hormonioterapia)\n",
+    "\n",
+    "print(\"\\nDataFrame - Tentativa de Suicídio:\")\n",
+    "print(df_tentativa_suicidio)\n",
+    "\n",
+    "print(\"\\nDataFrame - Melhoria dos Serviços de Atendimento à Saúde:\")\n",
+    "print(df_melhoria_atendimento_saude)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\ninff\\AppData\\Local\\Temp\\ipykernel_16892\\2781474352.py:7: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.barplot(x=identidade_genero, y=percentual, palette='muted')\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Dados\n",
+    "identidade_genero = ['Homem Trans', 'Mulher Trans', 'Travesti']\n",
+    "percentual = [35, 45, 20]\n",
+    "\n",
+    "# Gráfico de Barras\n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.barplot(x=identidade_genero, y=percentual, palette='muted')\n",
+    "plt.title('Distribuição por Identidade de Gênero', fontsize=16)\n",
+    "plt.xlabel('Identidade de Gênero')\n",
+    "plt.ylabel('Percentual (%)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Dados \n",
+    "orientacao_sexual = ['Heterossexual', 'Bissexual', 'Homossexual', 'Pansexual']\n",
+    "percentual_orientacao = [20, 30, 25, 25]\n",
+    "\n",
+    "# Gráfico de Pizza\n",
+    "plt.figure(figsize=(8,8))\n",
+    "plt.pie(percentual_orientacao, labels=orientacao_sexual, autopct='%1.1f%%', startangle=90, colors=sns.color_palette('pastel'))\n",
+    "plt.title('Distribuição de Orientação Sexual', fontsize=16)\n",
+    "plt.axis('equal')  # Garantir que o gráfico seja circular\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Dados\n",
+    "idades = [14, 16, 18, 20, 22, 24]\n",
+    "percentual_abandono = [10, 15, 20, 25, 30, 35]\n",
+    "\n",
+    "# Gráfico de Dispersão\n",
+    "plt.figure(figsize=(8,6))\n",
+    "sns.scatterplot(x=idades, y=percentual_abandono, s=100, color='b')\n",
+    "plt.title('Correlação: Idade de Percepção x Abandono Escolar', fontsize=16)\n",
+    "plt.xlabel('Idade de Percepção da Identidade Trans')\n",
+    "plt.ylabel('Percentual de Abandono Escolar (%)')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analia - projetoPT1/README.md b/Analia - projetoPT1/README.md
new file mode 100644
index 0000000..2549f6e
--- /dev/null
+++ b/Analia - projetoPT1/README.md	
@@ -0,0 +1,111 @@
+# 🍒 Projeto: Análise de Dados sobre a População Trans🍒
+
+#### Este é um programa em Python que realiza três tarefas principais:
+
+Este projeto visa analisar dados relacionados à população trans em diferentes aspectos, incluindo identidade de gênero, orientação sexual, educação, violência, mercado de trabalho e saúde. A análise foi realizada a partir de dados coletados de diversas fontes e tem como objetivo fornecer insights sobre os desafios e necessidades dessa população.
+
+
+
+* ## Bibliotecas
+
+  
+  
+  - **Python**: Linguagem de programação utilizada para a análise dos dados. 
+  - **Pandas**: Biblioteca utilizada para manipulação de dados e criação de DataFrames. - **
+  - **Matplotlib/Seaborn**: Ferramentas utilizadas para visualização de dados. - **
+  - **Jupyter Notebook**: Ambiente interativo utilizado para desenvolvimento e experimentação com os dados. - **
+  - **Markdown**: Formato utilizado para documentar o projeto.
+  
+  
+  
+  
+* ## Bases de Dados
+
+  * **Identidade de Gênero**: Percentual da população trans, incluindo homens transexuais, mulheres transexuais e travestis. 
+
+  * **Faixa Etária dos Entrevistados**: Distribuição etária dos entrevistados. 
+
+  *  **Orientação Sexual**: Percentuais de orientação sexual dos entrevistados (heterossexuais, bissexuais, homossexuais, pansexuais, etc.).
+
+  * **Correção de Nome e Gênero no Registro Civil**: Informações sobre a correção do nome e gênero no registro civil entre a população trans.
+
+  *  **Idade de Percepção de Identidade Trans**: Faixa etária em que os entrevistados se identificaram como trans.
+
+  * **Abandono Escolar e Motivos**: Dados sobre a interrupção dos estudos, causas do abandono escolar e preconceito/agressão no ambiente escolar.
+
+  *  **Preconceito e Violência na Família**: Incidência de preconceito e violência sofrida pela população trans no ambiente familiar.
+
+  * **Atividades Profissionais**: Informações sobre a principal atividade ou trabalho remunerado da população trans.
+
+  * **Preconceito no Trabalho**: Dados sobre o preconceito enfrentado pela população trans no ambiente de trabalho.
+
+  * **Saúde e Hormonioterapia**: Informações sobre a utilização de hormônios, acompanhamento médico e acesso a serviços de saúde.
+
+    
+
+* ## Objetivo
+
+  * Analisar o perfil da população trans em diversas áreas, como educação, mercado de trabalho, violência e saúde. 
+
+  * Identificar os principais desafios enfrentados por essa população em relação ao preconceito e discriminação. 
+
+  * Propor sugestões de melhorias nos serviços de saúde, educação e apoio à população trans.
+
+
+
+* ## Visualizações
+
+  1.  **Gráfico de Barras: Distribuição por Identidade de Gênero**:
+
+     * Mostra como diferentes identidades de gênero (homem trans, mulher trans, travesti) estão distribuídas em termos de percentual.
+  2. **Gráfico de Pizza: Distribuição de Orientação Sexual**: 
+  *  Apresenta a distribuição das orientações sexuais da população trans.
+  3. **Gráfico de Dispersão: Correlação entre Idade de Percepção e Abandono Escolar**: 
+  * Mostra a relação entre a idade em que a pessoa percebeu sua identidade trans e a taxa de abandono escolar, para analisar possíveis correlações.
+  
+  
+
+* ## Conclusão
+
+  * Este projeto oferece uma visão abrangente sobre os desafios enfrentados pela população trans no Brasil, com foco em educação, mercado de trabalho, saúde e ambiente familiar. As análises realizadas buscam fornecer subsídios para a criação de políticas públicas mais inclusivas e o aprimoramento de serviços voltados para essa população fonte dos dados foi pela pop trans.
+  * Pop Trans pode referir-se ao Programa de Atenção Especializada à Saúde da População Trans (PAESPopTrans) ou a um webdocumentário.
+
+
+
+* ## Fontes
+
+  * Pesquisa Sobre Pessoas Trans na Região Metropolitana da Grande Vitória (SEDH/IJSN)
+
+
+
+### Como Executar o Código ✍️
+
+1. **Clone este repositório em sua máquina local:**
+
+```
+https://github.com/AnaliaAlves/on33-python-s16-dashboard.git
+```
+
+
+
+2. **Abra o Prompt de comando ou GitBash.**
+
+   
+
+3. **Navegue até o diretório do projeto:**
+
+```
+cd seu-repositorio  "o nome do seu repositorio que voce criou"
+```
+
+4. **Abrir VsCode**
+
+```
+code .
+```
+
+
+
+## Contribuição 🫶🏽
+
+Contribuições são bem-vindas! Se você tiver sugestões, correções de bugs ou novos recursos para adicionar, sinta-se à vontade para abrir uma issue ou enviar um pull request.
\ No newline at end of file
diff --git a/AnaliaS16/Exercicio S15.pptx b/AnaliaS16/Exercicio S15.pptx
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diff --git "a/Projeto Final/Popula\303\247ao Trans.pptx" "b/Projeto Final/Popula\303\247ao Trans.pptx"
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diff --git a/material/estados_brasileiros.csv b/material/estados_brasileiros.csv
new file mode 100644
index 0000000..b82d6ea
--- /dev/null
+++ b/material/estados_brasileiros.csv
@@ -0,0 +1,28 @@
+país,sigla,estado,região
+Brasil,AC,Acre,Norte
+Brasil,AL,Alagoas,Nordeste
+Brasil,AP,Amapá,Norte
+Brasil,AM,Amazonas,Norte
+Brasil,BA,Bahia,Nordeste
+Brasil,CE,Ceará,Nordeste
+Brasil,DF,Distrito Federal,Centro-Oeste
+Brasil,ES,Espírito Santo,Sudeste
+Brasil,GO,Goiás,Centro-Oeste
+Brasil,MA,Maranhão,Nordeste
+Brasil,MT,Mato Grosso,Centro-Oeste
+Brasil,MS,Mato Grosso do Sul,Centro-Oeste
+Brasil,MG,Minas Gerais,Sudeste
+Brasil,PA,Pará,Norte
+Brasil,PB,Paraíba,Nordeste
+Brasil,PR,Paraná,Sul
+Brasil,PE,Pernambuco,Nordeste
+Brasil,PI,Piauí,Nordeste
+Brasil,RJ,Rio de Janeiro,Sudeste
+Brasil,RN,Rio Grande do Norte,Nordeste
+Brasil,RS,Rio Grande do Sul,Sul
+Brasil,RO,Rondônia,Norte
+Brasil,RR,Roraima,Norte
+Brasil,SC,Santa Catarina,Sul
+Brasil,SP,São Paulo,Sudeste
+Brasil,SE,Sergipe,Nordeste
+Brasil,TO,Tocantins,Norte