grafico-semana.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "221c7197dc8b4a1681dfe72e8004af14",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Tipo de Atividade:', options=('Todos', 'Deslocamento', 'Lazer'), v…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(activity_type, ano, mes, genero, faixa_etaria)>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "from ipywidgets import interact, widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Carregar todos os arquivos CSV de uma pasta específica em um único DataFrame\n",
    "folder_path = 'csvs'  # Substitua pelo caminho da sua pasta\n",
    "csv_files = glob.glob(folder_path + '/*.csv')\n",
    "\n",
    "# Definir as colunas a serem importadas e seus tipos de dados\n",
    "usecols = [\n",
    "    'hour',\n",
    "    'total_trip_count',\n",
    "    'forward_commute_trip_count', 'reverse_commute_trip_count',\n",
    "    'forward_leisure_trip_count', 'reverse_leisure_trip_count',\n",
    "    'forward_male_people_count', 'reverse_male_people_count',\n",
    "    'forward_female_people_count', 'reverse_female_people_count',\n",
    "    'forward_unspecified_people_count', 'reverse_unspecified_people_count',\n",
    "    'forward_18_34_people_count', 'reverse_18_34_people_count',\n",
    "    'forward_35_54_people_count', 'reverse_35_54_people_count',\n",
    "    'forward_55_64_people_count', 'reverse_55_64_people_count',\n",
    "    'forward_65_plus_people_count', 'reverse_65_plus_people_count'\n",
    "]\n",
    "\n",
    "dtype_dict = {\n",
    "    'total_trip_count': 'Int16',\n",
    "    'forward_commute_trip_count': 'Int16', 'reverse_commute_trip_count': 'Int16',\n",
    "    'forward_leisure_trip_count': 'Int16', 'reverse_leisure_trip_count': 'Int16',\n",
    "    'forward_male_people_count': 'Int16', 'reverse_male_people_count': 'Int16',\n",
    "    'forward_female_people_count': 'Int16', 'reverse_female_people_count': 'Int16',\n",
    "    'forward_unspecified_people_count': 'Int16', 'reverse_unspecified_people_count': 'Int16',\n",
    "    'forward_18_34_people_count': 'Int16', 'reverse_18_34_people_count': 'Int16',\n",
    "    'forward_35_54_people_count': 'Int16', 'reverse_35_54_people_count': 'Int16',\n",
    "    'forward_55_64_people_count': 'Int16', 'reverse_55_64_people_count': 'Int16',\n",
    "    'forward_65_plus_people_count': 'Int16', 'reverse_65_plus_people_count': 'Int16'\n",
    "}\n",
    "\n",
    "# Carregar todos os CSVs em uma lista e concatenar em um único DataFrame\n",
    "df_list = [pd.read_csv(file, usecols=usecols, dtype=dtype_dict, parse_dates=['hour']) for file in csv_files]\n",
    "df_all = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "# Extrair ano, mês, dia da semana e hora do dia\n",
    "df_all['year'] = df_all['hour'].dt.year\n",
    "df_all['month'] = df_all['hour'].dt.month\n",
    "df_all['day_of_week'] = df_all['hour'].dt.dayofweek  # 0 = Segunda-feira, 6 = Domingo\n",
    "df_all['hour_of_day'] = df_all['hour'].dt.hour\n",
    "\n",
    "# Calcular os totais para gêneros e faixas etárias fora da função\n",
    "df_all['male_people_count'] = df_all['forward_male_people_count'].fillna(0) + df_all['reverse_male_people_count'].fillna(0)\n",
    "df_all['female_people_count'] = df_all['forward_female_people_count'].fillna(0) + df_all['reverse_female_people_count'].fillna(0)\n",
    "df_all['unspecified_people_count'] = df_all['forward_unspecified_people_count'].fillna(0) + df_all['reverse_unspecified_people_count'].fillna(0)\n",
    "\n",
    "df_all['18_34_people_count'] = df_all['forward_18_34_people_count'].fillna(0) + df_all['reverse_18_34_people_count'].fillna(0)\n",
    "df_all['35_54_people_count'] = df_all['forward_35_54_people_count'].fillna(0) + df_all['reverse_35_54_people_count'].fillna(0)\n",
    "df_all['55_64_people_count'] = df_all['forward_55_64_people_count'].fillna(0) + df_all['reverse_55_64_people_count'].fillna(0)\n",
    "df_all['65_plus_people_count'] = df_all['forward_65_plus_people_count'].fillna(0) + df_all['reverse_65_plus_people_count'].fillna(0)\n",
    "\n",
    "df_all['commute_trip_count'] = df_all['forward_commute_trip_count'].fillna(0) + df_all['reverse_commute_trip_count'].fillna(0)\n",
    "df_all['leisure_trip_count'] = df_all['forward_leisure_trip_count'].fillna(0) + df_all['reverse_leisure_trip_count'].fillna(0)\n",
    "df_all['total_trip_count'] = df_all['commute_trip_count'] + df_all['leisure_trip_count']\n",
    "\n",
    "# Mapear números dos dias da semana para nomes em português\n",
    "day_names = {\n",
    "    0: 'Segunda-feira',\n",
    "    1: 'Terça-feira',\n",
    "    2: 'Quarta-feira',\n",
    "    3: 'Quinta-feira',\n",
    "    4: 'Sexta-feira',\n",
    "    5: 'Sábado',\n",
    "    6: 'Domingo'\n",
    "}\n",
    "\n",
    "# Criar widgets conforme solicitado\n",
    "activity_type_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Deslocamento', 'Lazer'],\n",
    "    value='Todos',\n",
    "    description='Tipo de Atividade:'\n",
    ")\n",
    "\n",
    "ano_widget = widgets.SelectMultiple(\n",
    "    options=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    value=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    description='Ano:'\n",
    ")\n",
    "\n",
    "mes_widget = widgets.SelectMultiple(\n",
    "    options=[('Janeiro', 1), ('Fevereiro', 2), ('Março', 3), ('Abril', 4), ('Maio', 5), ('Junho', 6),\n",
    "             ('Julho', 7), ('Agosto', 8), ('Setembro', 9), ('Outubro', 10), ('Novembro', 11), ('Dezembro', 12)],\n",
    "    value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "    description='Mês:'\n",
    ")\n",
    "\n",
    "genero_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Homem', 'Mulher', 'Não especificado'],\n",
    "    value='Todos',\n",
    "    description='Gênero:'\n",
    ")\n",
    "\n",
    "faixa_etaria_widget = widgets.Dropdown(\n",
    "    options=['Todos', '18-34', '35-54', '55-64', '65+'],\n",
    "    value='Todos',\n",
    "    description='Faixa Etária:'\n",
    ")\n",
    "\n",
    "# Mapas para mapeamento das opções dos widgets para valores no DataFrame\n",
    "map_genero = {\n",
    "    'Todos': 'total',\n",
    "    'Homem': 'male',\n",
    "    'Mulher': 'female',\n",
    "    'Não especificado': 'unspecified'\n",
    "}\n",
    "\n",
    "map_atividade = {\n",
    "    'Todos': 'total',\n",
    "    'Deslocamento': 'commute',\n",
    "    'Lazer': 'leisure'\n",
    "}\n",
    "\n",
    "map_faixa_etaria = {\n",
    "    'Todos': 'total',\n",
    "    '18-34': '18_34',\n",
    "    '35-54': '35_54',\n",
    "    '55-64': '55_64',\n",
    "    '65+': '65_plus'\n",
    "}\n",
    "\n",
    "# Definir a função para atualizar o gráfico\n",
    "def update_plot(activity_type, ano, mes, genero, faixa_etaria):\n",
    "    # Mapear as opções selecionadas para os valores correspondentes\n",
    "    activity_type_mapped = map_atividade[activity_type]\n",
    "    genero_mapped = map_genero[genero]\n",
    "    faixa_etaria_mapped = map_faixa_etaria[faixa_etaria]\n",
    "\n",
    "    # Filtrar dados pelos anos e meses selecionados\n",
    "    df_filtered = df_all[(df_all['year'].isin(ano)) & (df_all['month'].isin(mes))]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    # Selecionar a coluna de contagem de viagens apropriada\n",
    "    if activity_type_mapped == 'total':\n",
    "        trip_count_column = 'total_trip_count'\n",
    "    else:\n",
    "        trip_count_column = activity_type_mapped + '_trip_count'  # 'commute_trip_count' ou 'leisure_trip_count'\n",
    "\n",
    "    # Tratar a seleção de gênero\n",
    "    if genero_mapped != 'total':\n",
    "        df_filtered['people_count'] = df_filtered[genero_mapped + '_people_count'].fillna(0)\n",
    "    else:\n",
    "        df_filtered['people_count'] = df_filtered[['male_people_count', 'female_people_count', 'unspecified_people_count']].fillna(0).sum(axis=1)\n",
    "\n",
    "    # Tratar a seleção de faixa etária\n",
    "    if faixa_etaria_mapped != 'total':\n",
    "        df_filtered['age_group_count'] = df_filtered[faixa_etaria_mapped + '_people_count'].fillna(0)\n",
    "    else:\n",
    "        df_filtered['age_group_count'] = df_filtered[['18_34_people_count', '35_54_people_count', '55_64_people_count', '65_plus_people_count']].fillna(0).sum(axis=1)\n",
    "\n",
    "    # Filtrar dados onde as contagens de pessoas são maiores que zero\n",
    "    df_filtered = df_filtered[(df_filtered['people_count'] > 0) & (df_filtered['age_group_count'] > 0)]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    # Agrupar os dados por 'day_of_week' e somar as contagens de viagens\n",
    "    grouped = df_filtered.groupby('day_of_week')[trip_count_column].sum().reset_index()\n",
    "\n",
    "    # Mapear números dos dias da semana para nomes em português\n",
    "    grouped['day_name'] = grouped['day_of_week'].map(day_names)\n",
    "\n",
    "    # Ordenar os dados por 'day_of_week'\n",
    "    grouped.sort_values('day_of_week', inplace=True)\n",
    "\n",
    "    # Plotar os dados\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(x='day_name', y=trip_count_column, data=grouped, color='skyblue')\n",
    "    plt.title(f\"Viagens por Dia da Semana ({min(ano)} a {max(ano)}) - Atividade: {activity_type} - Faixa Etária: {faixa_etaria}\")\n",
    "    plt.xlabel('Dia da Semana')\n",
    "    plt.ylabel('Número de Viagens')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Determinar o valor do filtro para nomear o arquivo\n",
    "    if faixa_etaria_mapped != 'total':\n",
    "        valor_filtro = faixa_etaria_mapped\n",
    "    elif genero_mapped != 'total':\n",
    "        valor_filtro = genero_mapped\n",
    "    else:\n",
    "        valor_filtro = 'total'\n",
    "\n",
    "    pasta = 'graficos-semana'\n",
    "    \n",
    "    if not os.path.exists(pasta):\n",
    "        os.makedirs(pasta)\n",
    "\n",
    "    nome_arquivo = os.path.join(pasta, f\"grafico-{valor_filtro}-semana.png\")\n",
    "    \n",
    "    # Salvar o gráfico como um arquivo PNG\n",
    "    plt.savefig(nome_arquivo)\n",
    "    plt.show()\n",
    "\n",
    "# Tornar a função interativa com os widgets\n",
    "interact(\n",
    "    update_plot,\n",
    "    activity_type=activity_type_widget,\n",
    "    ano=ano_widget,\n",
    "    mes=mes_widget,\n",
    "    genero=genero_widget,\n",
    "    faixa_etaria=faixa_etaria_widget\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafico de linha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ef3162827a94985942f5be1cb27bf05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Tipo de Atividade:', options=('Todos', 'Deslocamento', 'Lazer'), v…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(activity_type, ano, mes, genero, faixa_etaria)>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "from ipywidgets import interact, widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Carregar todos os arquivos CSV de uma pasta específica em um único DataFrame\n",
    "folder_path = 'csvs'  # Substitua pelo caminho da sua pasta\n",
    "csv_files = glob.glob(folder_path + '/*.csv')\n",
    "\n",
    "# Definir as colunas a serem importadas e seus tipos de dados\n",
    "usecols = [\n",
    "    'hour',\n",
    "    'total_trip_count',\n",
    "    'forward_commute_trip_count', 'reverse_commute_trip_count',\n",
    "    'forward_leisure_trip_count', 'reverse_leisure_trip_count',\n",
    "    'forward_male_people_count', 'reverse_male_people_count',\n",
    "    'forward_female_people_count', 'reverse_female_people_count',\n",
    "    'forward_unspecified_people_count', 'reverse_unspecified_people_count',\n",
    "    'forward_18_34_people_count', 'reverse_18_34_people_count',\n",
    "    'forward_35_54_people_count', 'reverse_35_54_people_count',\n",
    "    'forward_55_64_people_count', 'reverse_55_64_people_count',\n",
    "    'forward_65_plus_people_count', 'reverse_65_plus_people_count'\n",
    "]\n",
    "\n",
    "dtype_dict = {\n",
    "    'total_trip_count': 'Int16',\n",
    "    'forward_commute_trip_count': 'Int16', 'reverse_commute_trip_count': 'Int16',\n",
    "    'forward_leisure_trip_count': 'Int16', 'reverse_leisure_trip_count': 'Int16',\n",
    "    'forward_male_people_count': 'Int16', 'reverse_male_people_count': 'Int16',\n",
    "    'forward_female_people_count': 'Int16', 'reverse_female_people_count': 'Int16',\n",
    "    'forward_unspecified_people_count': 'Int16', 'reverse_unspecified_people_count': 'Int16',\n",
    "    'forward_18_34_people_count': 'Int16', 'reverse_18_34_people_count': 'Int16',\n",
    "    'forward_35_54_people_count': 'Int16', 'reverse_35_54_people_count': 'Int16',\n",
    "    'forward_55_64_people_count': 'Int16', 'reverse_55_64_people_count': 'Int16',\n",
    "    'forward_65_plus_people_count': 'Int16', 'reverse_65_plus_people_count': 'Int16'\n",
    "}\n",
    "\n",
    "# Carregar todos os CSVs em uma lista e concatenar em um único DataFrame\n",
    "df_list = [pd.read_csv(file, usecols=usecols, dtype=dtype_dict, parse_dates=['hour']) for file in csv_files]\n",
    "df_all = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "# Extrair ano, mês, dia da semana e hora do dia\n",
    "df_all['year'] = df_all['hour'].dt.year\n",
    "df_all['month'] = df_all['hour'].dt.month\n",
    "df_all['day_of_week'] = df_all['hour'].dt.dayofweek  # 0 = Segunda-feira, 6 = Domingo\n",
    "df_all['hour_of_day'] = df_all['hour'].dt.hour\n",
    "\n",
    "# Calcular os totais para gêneros e faixas etárias fora da função\n",
    "df_all['male_people_count'] = df_all['forward_male_people_count'].fillna(0) + df_all['reverse_male_people_count'].fillna(0)\n",
    "df_all['female_people_count'] = df_all['forward_female_people_count'].fillna(0) + df_all['reverse_female_people_count'].fillna(0)\n",
    "df_all['unspecified_people_count'] = df_all['forward_unspecified_people_count'].fillna(0) + df_all['reverse_unspecified_people_count'].fillna(0)\n",
    "\n",
    "df_all['18_34_people_count'] = df_all['forward_18_34_people_count'].fillna(0) + df_all['reverse_18_34_people_count'].fillna(0)\n",
    "df_all['35_54_people_count'] = df_all['forward_35_54_people_count'].fillna(0) + df_all['reverse_35_54_people_count'].fillna(0)\n",
    "df_all['55_64_people_count'] = df_all['forward_55_64_people_count'].fillna(0) + df_all['reverse_55_64_people_count'].fillna(0)\n",
    "df_all['65_plus_people_count'] = df_all['forward_65_plus_people_count'].fillna(0) + df_all['reverse_65_plus_people_count'].fillna(0)\n",
    "\n",
    "df_all['commute_trip_count'] = df_all['forward_commute_trip_count'].fillna(0) + df_all['reverse_commute_trip_count'].fillna(0)\n",
    "df_all['leisure_trip_count'] = df_all['forward_leisure_trip_count'].fillna(0) + df_all['reverse_leisure_trip_count'].fillna(0)\n",
    "df_all['total_trip_count'] = df_all['commute_trip_count'] + df_all['leisure_trip_count']\n",
    "\n",
    "# Mapear números dos dias da semana para nomes em português\n",
    "day_names = {\n",
    "    0: 'Segunda-feira',\n",
    "    1: 'Terça-feira',\n",
    "    2: 'Quarta-feira',\n",
    "    3: 'Quinta-feira',\n",
    "    4: 'Sexta-feira',\n",
    "    5: 'Sábado',\n",
    "    6: 'Domingo'\n",
    "}\n",
    "\n",
    "# Criar widgets conforme solicitado\n",
    "activity_type_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Deslocamento', 'Lazer'],\n",
    "    value='Todos',\n",
    "    description='Tipo de Atividade:'\n",
    ")\n",
    "\n",
    "ano_widget = widgets.SelectMultiple(\n",
    "    options=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    value=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    description='Ano:'\n",
    ")\n",
    "\n",
    "mes_widget = widgets.SelectMultiple(\n",
    "    options=[('Janeiro', 1), ('Fevereiro', 2), ('Março', 3), ('Abril', 4), ('Maio', 5), ('Junho', 6),\n",
    "             ('Julho', 7), ('Agosto', 8), ('Setembro', 9), ('Outubro', 10), ('Novembro', 11), ('Dezembro', 12)],\n",
    "    value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "    description='Mês:'\n",
    ")\n",
    "\n",
    "genero_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Homem', 'Mulher', 'Não especificado'],\n",
    "    value='Todos',\n",
    "    description='Gênero:'\n",
    ")\n",
    "\n",
    "faixa_etaria_widget = widgets.Dropdown(\n",
    "    options=['Todos', '18-34', '35-54', '55-64', '65+'],\n",
    "    value='Todos',\n",
    "    description='Faixa Etária:'\n",
    ")\n",
    "\n",
    "# Mapas para mapeamento das opções dos widgets para valores no DataFrame\n",
    "map_genero = {\n",
    "    'Todos': 'total',\n",
    "    'Homem': 'male',\n",
    "    'Mulher': 'female',\n",
    "    'Não especificado': 'unspecified'\n",
    "}\n",
    "\n",
    "map_atividade = {\n",
    "    'Todos': 'total',\n",
    "    'Deslocamento': 'commute',\n",
    "    'Lazer': 'leisure'\n",
    "}\n",
    "\n",
    "map_faixa_etaria = {\n",
    "    'Todos': 'total',\n",
    "    '18-34': '18_34',\n",
    "    '35-54': '35_54',\n",
    "    '55-64': '55_64',\n",
    "    '65+': '65_plus'\n",
    "}\n",
    "\n",
    "# Definir a função para atualizar o gráfico\n",
    "def update_plot(activity_type, ano, mes, genero, faixa_etaria):\n",
    "    # Mapear as opções selecionadas para os valores correspondentes\n",
    "    activity_type_mapped = map_atividade[activity_type]\n",
    "    genero_mapped = map_genero[genero]\n",
    "    faixa_etaria_mapped = map_faixa_etaria[faixa_etaria]\n",
    "\n",
    "    # Filtrar dados pelos anos e meses selecionados\n",
    "    df_filtered = df_all[(df_all['year'].isin(ano)) & (df_all['month'].isin(mes))]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    # Selecionar a coluna de contagem de viagens apropriada\n",
    "    if activity_type_mapped == 'total':\n",
    "        trip_count_column = 'total_trip_count'\n",
    "    else:\n",
    "        trip_count_column = activity_type_mapped + '_trip_count'  # 'commute_trip_count' ou 'leisure_trip_count'\n",
    "\n",
    "    # Tratar a seleção de gênero\n",
    "    if genero_mapped != 'total':\n",
    "        df_filtered['people_count'] = df_filtered[genero_mapped + '_people_count'].fillna(0)\n",
    "    else:\n",
    "        df_filtered['people_count'] = df_filtered[['male_people_count', 'female_people_count', 'unspecified_people_count']].fillna(0).sum(axis=1)\n",
    "\n",
    "    # Tratar a seleção de faixa etária\n",
    "    if faixa_etaria_mapped != 'total':\n",
    "        df_filtered['age_group_count'] = df_filtered[faixa_etaria_mapped + '_people_count'].fillna(0)\n",
    "    else:\n",
    "        df_filtered['age_group_count'] = df_filtered[['18_34_people_count', '35_54_people_count', '55_64_people_count', '65_plus_people_count']].fillna(0).sum(axis=1)\n",
    "\n",
    "    # Filtrar dados onde as contagens de pessoas são maiores que zero\n",
    "    df_filtered = df_filtered[(df_filtered['people_count'] > 0) & (df_filtered['age_group_count'] > 0)]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    if faixa_etaria_mapped == 'total':\n",
    "        # Reorganizar os dados para ter uma coluna de faixa etária\n",
    "        age_group_columns = ['18_34_people_count', '35_54_people_count', '55_64_people_count', '65_plus_people_count']\n",
    "        df_melted = df_filtered.melt(\n",
    "            id_vars=['day_of_week', trip_count_column],\n",
    "            value_vars=age_group_columns,\n",
    "            var_name='age_group',\n",
    "            value_name='age_group_count_melted'\n",
    "        )\n",
    "        # Mapear os valores da coluna 'age_group' para rótulos legíveis\n",
    "        df_melted['age_group'] = df_melted['age_group'].map({\n",
    "            '18_34_people_count': '18-34',\n",
    "            '35_54_people_count': '35-54',\n",
    "            '55_64_people_count': '55-64',\n",
    "            '65_plus_people_count': '65+'\n",
    "        })\n",
    "\n",
    "        df_melted = df_melted[df_melted['age_group_count_melted'] > 0]\n",
    "        if df_melted.empty:\n",
    "            print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "            return\n",
    "        # Agrupar os dados por 'day_of_week' e 'age_group'\n",
    "        grouped = df_melted.groupby(['day_of_week', 'age_group'])[trip_count_column].sum().reset_index()\n",
    "        # Mapear números dos dias da semana para nomes em português\n",
    "        grouped['day_name'] = grouped['day_of_week'].map(day_names)\n",
    "        sns.lineplot(x='day_name', y=trip_count_column, hue='age_group', data=grouped, marker='o')\n",
    "    else:\n",
    "        # Agrupar os dados por 'day_of_week' e somar as contagens de viagens\n",
    "        grouped = df_filtered.groupby('day_of_week')[trip_count_column].sum().reset_index()\n",
    "        grouped['day_name'] = grouped['day_of_week'].map(day_names)\n",
    "        sns.barplot(x='day_name', y=trip_count_column, data=grouped, color='skyblue')\n",
    "\n",
    "    plt.title(f\"Viagens por Dia da Semana ({min(ano)} a {max(ano)}) - Atividade: {activity_type} - Faixa Etária: {faixa_etaria}\")\n",
    "    plt.xlabel('Dia da Semana')\n",
    "    plt.ylabel('Número de Viagens')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Salvar o gráfico\n",
    "    if faixa_etaria_mapped != 'total':\n",
    "        valor_filtro = faixa_etaria_mapped\n",
    "    elif genero_mapped != 'total':\n",
    "        valor_filtro = genero_mapped\n",
    "    else:\n",
    "        valor_filtro = 'total'\n",
    "\n",
    "    pasta = 'graficos-semana'\n",
    "\n",
    "    if not os.path.exists(pasta):\n",
    "        os.makedirs(pasta)\n",
    "\n",
    "    nome_arquivo = os.path.join(pasta, f\"grafico-{valor_filtro}-semana.png\")\n",
    "    plt.savefig(nome_arquivo)\n",
    "    plt.show()\n",
    "\n",
    "# Tornar a função interativa com os widgets\n",
    "interact(\n",
    "    update_plot,\n",
    "    activity_type=activity_type_widget,\n",
    "    ano=ano_widget,\n",
    "    mes=mes_widget,\n",
    "    genero=genero_widget,\n",
    "    faixa_etaria=faixa_etaria_widget\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grafico barras Generos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09e2a2e4c49145129e8596aa40313711",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='Tipo de Atividade:', options=('Todos', 'Deslocamento', 'Lazer'), v…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.update_plot(activity_type, ano, mes, genero, faixa_etaria)>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import os\n",
    "from ipywidgets import interact, widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Carregar todos os arquivos CSV de uma pasta específica em um único DataFrame\n",
    "folder_path = 'csvs'  # Substitua pelo caminho da sua pasta\n",
    "csv_files = glob.glob(folder_path + '/*.csv')\n",
    "\n",
    "# Definir as colunas a serem importadas e seus tipos de dados\n",
    "usecols = [\n",
    "    'hour',\n",
    "    'total_trip_count',\n",
    "    'forward_commute_trip_count', 'reverse_commute_trip_count',\n",
    "    'forward_leisure_trip_count', 'reverse_leisure_trip_count',\n",
    "    'forward_male_people_count', 'reverse_male_people_count',\n",
    "    'forward_female_people_count', 'reverse_female_people_count',\n",
    "    'forward_unspecified_people_count', 'reverse_unspecified_people_count',\n",
    "    'forward_18_34_people_count', 'reverse_18_34_people_count',\n",
    "    'forward_35_54_people_count', 'reverse_35_54_people_count',\n",
    "    'forward_55_64_people_count', 'reverse_55_64_people_count',\n",
    "    'forward_65_plus_people_count', 'reverse_65_plus_people_count'\n",
    "]\n",
    "\n",
    "dtype_dict = {\n",
    "    'total_trip_count': 'Int16',\n",
    "    'forward_commute_trip_count': 'Int16', 'reverse_commute_trip_count': 'Int16',\n",
    "    'forward_leisure_trip_count': 'Int16', 'reverse_leisure_trip_count': 'Int16',\n",
    "    'forward_male_people_count': 'Int16', 'reverse_male_people_count': 'Int16',\n",
    "    'forward_female_people_count': 'Int16', 'reverse_female_people_count': 'Int16',\n",
    "    'forward_unspecified_people_count': 'Int16', 'reverse_unspecified_people_count': 'Int16',\n",
    "    'forward_18_34_people_count': 'Int16', 'reverse_18_34_people_count': 'Int16',\n",
    "    'forward_35_54_people_count': 'Int16', 'reverse_35_54_people_count': 'Int16',\n",
    "    'forward_55_64_people_count': 'Int16', 'reverse_55_64_people_count': 'Int16',\n",
    "    'forward_65_plus_people_count': 'Int16', 'reverse_65_plus_people_count': 'Int16'\n",
    "}\n",
    "\n",
    "# Carregar todos os CSVs em uma lista e concatenar em um único DataFrame\n",
    "df_list = [pd.read_csv(\n",
    "    file,\n",
    "    usecols=usecols,\n",
    "    dtype=dtype_dict,\n",
    "    parse_dates=['hour']\n",
    ") for file in csv_files]\n",
    "\n",
    "df_all = pd.concat(df_list, ignore_index=True)\n",
    "\n",
    "# Extrair o ano, mês, dia da semana e hora do dia\n",
    "df_all['year'] = df_all['hour'].dt.year\n",
    "df_all['month'] = df_all['hour'].dt.month\n",
    "df_all['day_of_week'] = df_all['hour'].dt.dayofweek  # Segunda=0, Domingo=6\n",
    "df_all['hour_of_day'] = df_all['hour'].dt.hour\n",
    "\n",
    "# Calcular os totais para gêneros e faixas etárias\n",
    "df_all['male_people_count'] = df_all['forward_male_people_count'].fillna(0) + df_all['reverse_male_people_count'].fillna(0)\n",
    "df_all['female_people_count'] = df_all['forward_female_people_count'].fillna(0) + df_all['reverse_female_people_count'].fillna(0)\n",
    "df_all['unspecified_people_count'] = df_all['forward_unspecified_people_count'].fillna(0) + df_all['reverse_unspecified_people_count'].fillna(0)\n",
    "\n",
    "df_all['18_34_people_count'] = df_all['forward_18_34_people_count'].fillna(0) + df_all['reverse_18_34_people_count'].fillna(0)\n",
    "df_all['35_54_people_count'] = df_all['forward_35_54_people_count'].fillna(0) + df_all['reverse_35_54_people_count'].fillna(0)\n",
    "df_all['55_64_people_count'] = df_all['forward_55_64_people_count'].fillna(0) + df_all['reverse_55_64_people_count'].fillna(0)\n",
    "df_all['65_plus_people_count'] = df_all['forward_65_plus_people_count'].fillna(0) + df_all['reverse_65_plus_people_count'].fillna(0)\n",
    "\n",
    "df_all['commute_trip_count'] = df_all['forward_commute_trip_count'].fillna(0) + df_all['reverse_commute_trip_count'].fillna(0)\n",
    "df_all['leisure_trip_count'] = df_all['forward_leisure_trip_count'].fillna(0) + df_all['reverse_leisure_trip_count'].fillna(0)\n",
    "df_all['total_trip_count'] = df_all['commute_trip_count'] + df_all['leisure_trip_count']\n",
    "\n",
    "# Mapear números dos dias da semana para nomes em português\n",
    "day_names = {\n",
    "    0: 'Segunda-feira',\n",
    "    1: 'Terça-feira',\n",
    "    2: 'Quarta-feira',\n",
    "    3: 'Quinta-feira',\n",
    "    4: 'Sexta-feira',\n",
    "    5: 'Sábado',\n",
    "    6: 'Domingo'\n",
    "}\n",
    "\n",
    "# Criar widgets conforme solicitado\n",
    "activity_type_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Deslocamento', 'Lazer'],\n",
    "    value='Todos',\n",
    "    description='Tipo de Atividade:'\n",
    ")\n",
    "\n",
    "ano_widget = widgets.SelectMultiple(\n",
    "    options=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    value=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    description='Ano:'\n",
    ")\n",
    "\n",
    "mes_widget = widgets.SelectMultiple(\n",
    "    options=[('Janeiro', 1), ('Fevereiro', 2), ('Março', 3), ('Abril', 4), ('Maio', 5), ('Junho', 6),\n",
    "             ('Julho', 7), ('Agosto', 8), ('Setembro', 9), ('Outubro', 10), ('Novembro', 11), ('Dezembro', 12)],\n",
    "    value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
    "    description='Mês:'\n",
    ")\n",
    "\n",
    "genero_widget = widgets.Dropdown(\n",
    "    options=['Todos', 'Homem', 'Mulher', 'Não especificado'],\n",
    "    value='Todos',\n",
    "    description='Gênero:'\n",
    ")\n",
    "\n",
    "faixa_etaria_widget = widgets.Dropdown(\n",
    "    options=['Todos', '18-34', '35-54', '55-64', '65+'],\n",
    "    value='Todos',\n",
    "    description='Faixa Etária:'\n",
    ")\n",
    "\n",
    "# Mapas para mapeamento das opções dos widgets para valores no DataFrame\n",
    "map_genero = {\n",
    "    'Todos': 'total',\n",
    "    'Homem': 'male',\n",
    "    'Mulher': 'female',\n",
    "    'Não especificado': 'unspecified'\n",
    "}\n",
    "\n",
    "map_atividade = {\n",
    "    'Todos': 'total',\n",
    "    'Deslocamento': 'commute',\n",
    "    'Lazer': 'leisure'\n",
    "}\n",
    "\n",
    "map_faixa_etaria = {\n",
    "    'Todos': 'total',\n",
    "    '18-34': '18_34',\n",
    "    '35-54': '35_54',\n",
    "    '55-64': '55_64',\n",
    "    '65+': '65_plus'\n",
    "}\n",
    "\n",
    "# Definir a função para atualizar o gráfico\n",
    "def update_plot(activity_type, ano, mes, genero, faixa_etaria):\n",
    "    # Mapear as opções selecionadas para os valores correspondentes\n",
    "    activity_type_mapped = map_atividade[activity_type]\n",
    "    genero_mapped = map_genero[genero]\n",
    "    faixa_etaria_mapped = map_faixa_etaria[faixa_etaria]\n",
    "\n",
    "    # Filtrar dados pelos anos e meses selecionados\n",
    "    df_filtered = df_all[(df_all['year'].isin(ano)) & (df_all['month'].isin(mes))]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    # Selecionar a coluna de contagem de viagens apropriada\n",
    "    if activity_type_mapped == 'total':\n",
    "        trip_count_column = 'total_trip_count'\n",
    "    else:\n",
    "        trip_count_column = activity_type_mapped + '_trip_count'  # 'commute_trip_count' ou 'leisure_trip_count'\n",
    "\n",
    "    # Tratar a seleção de faixa etária\n",
    "    if faixa_etaria_mapped != 'total':\n",
    "        df_filtered['age_group_count'] = df_filtered[faixa_etaria_mapped + '_people_count'].fillna(0)\n",
    "    else:\n",
    "        df_filtered['age_group_count'] = df_filtered[['18_34_people_count', '35_54_people_count', '55_64_people_count', '65_plus_people_count']].fillna(0).sum(axis=1)\n",
    "\n",
    "    # Filtrar dados onde as contagens de faixa etária são maiores que zero\n",
    "    df_filtered = df_filtered[df_filtered['age_group_count'] > 0]\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "\n",
    "    # Tratar a seleção de gênero\n",
    "    if genero_mapped != 'total':\n",
    "        df_filtered['people_count'] = df_filtered[genero_mapped + '_people_count'].fillna(0)\n",
    "        df_filtered = df_filtered[df_filtered['people_count'] > 0]\n",
    "        if df_filtered.empty:\n",
    "            print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "            return\n",
    "        valor_filtro = genero_mapped\n",
    "        # Agrupar os dados por 'day_of_week' e somar as contagens de viagens\n",
    "        grouped = df_filtered.groupby('day_of_week')[trip_count_column].sum().reset_index()\n",
    "        grouped['day_name'] = grouped['day_of_week'].map(day_names)\n",
    "\n",
    "        # Plotar os dados\n",
    "        sns.barplot(x='day_name', y=trip_count_column, data=grouped)\n",
    "        \n",
    "    else:\n",
    "        # Reorganizar os dados para ter uma coluna 'gender' e uma coluna 'people_count'\n",
    "        gender_columns = ['male_people_count', 'female_people_count', 'unspecified_people_count']\n",
    "        df_melted = df_filtered.melt(\n",
    "            id_vars=['day_of_week', trip_count_column],\n",
    "            value_vars=gender_columns,\n",
    "            var_name='gender',\n",
    "            value_name='people_count'\n",
    "        )\n",
    "        # Mapear os nomes das colunas para os gêneros correspondentes\n",
    "        df_melted['gender'] = df_melted['gender'].map({\n",
    "            'male_people_count': 'Homem',\n",
    "            'female_people_count': 'Mulher',\n",
    "            'unspecified_people_count': 'Não especificado'\n",
    "        })\n",
    "\n",
    "        # Filtrar dados onde as contagens de pessoas são maiores que zero\n",
    "        df_melted = df_melted[df_melted['people_count'] > 0]\n",
    "\n",
    "        if df_melted.empty:\n",
    "            print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "            return  # Sair da função se não houver dados após o filtro\n",
    "\n",
    "        # Agrupar os dados por 'day_of_week' e 'gender' e somar as contagens de viagens\n",
    "        grouped = df_melted.groupby(['day_of_week', 'gender'])[trip_count_column].sum().reset_index()\n",
    "        grouped['day_name'] = grouped['day_of_week'].map(day_names)\n",
    "        valor_filtro = 'todos_generos'\n",
    "\n",
    "        # Plotar os dados com a distinção de gênero\n",
    "        sns.barplot(x='day_name', y=trip_count_column, hue='gender', data=grouped)\n",
    "\n",
    "    plt.title(f\"Viagens por Dia da Semana ({min(ano)} a {max(ano)}) - Atividade: {activity_type}\")\n",
    "    plt.xlabel('Dia da Semana')\n",
    "    plt.ylabel('Número de Viagens')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.legend(title='Gênero')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Salvando o gráfico\n",
    "    pasta = 'graficos-semana'\n",
    "    if not os.path.exists(pasta):\n",
    "        os.makedirs(pasta)\n",
    "    nome_arquivo = os.path.join(pasta, f\"grafico-{valor_filtro}-semana.png\")\n",
    "    plt.savefig(nome_arquivo)\n",
    "    plt.show()\n",
    "\n",
    "# Tornar a função interativa com os widgets\n",
    "interact(\n",
    "    update_plot,\n",
    "    activity_type=activity_type_widget,\n",
    "    ano=ano_widget,\n",
    "    mes=mes_widget,\n",
    "    genero=genero_widget,\n",
    "    faixa_etaria=faixa_etaria_widget\n",
    ")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}