grafico-linha-ano.ipynb

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       "interactive(children=(Dropdown(description='Tipo de Atividade:', options=('Todos', 'Deslocamento', 'Lazer'), v…"
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       "<function __main__.update_plot(activity_type, ano, genero, faixa_etaria)>"
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   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "from ipywidgets import interact, widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Carregar múltiplos arquivos CSV de uma pasta específica\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",
    "# Ler todos os arquivos CSV de uma vez 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 e o mês\n",
    "df_all['year'] = df_all['hour'].dt.year\n",
    "df_all['month'] = df_all['hour'].dt.month\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",
    "# 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 = widgets.Dropdown(\n",
    "    options=[2019, 2020, 2021, 2022, 2023, 2024],\n",
    "    value=2019,\n",
    "    description='Ano:'\n",
    ")\n",
    "\n",
    "genero = widgets.Dropdown(\n",
    "    options=['Todos', 'Homem', 'Mulher', 'Não especificado'],\n",
    "    value='Todos',\n",
    "    description='Gênero:'\n",
    ")\n",
    "\n",
    "faixa_etaria = 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, genero, faixa_etaria):\n",
    "    # Mapear as opções selecionadas para os valores correspondentes\n",
    "    genero_mapped = map_genero[genero]\n",
    "    faixa_etaria_mapped = map_faixa_etaria[faixa_etaria]\n",
    "\n",
    "    # Filtrar dados pelo ano selecionado\n",
    "    df_filtered = df_all[df_all['year'] == ano].copy()\n",
    "\n",
    "    if df_filtered.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return \n",
    "\n",
    "    # Selecionar a coluna de contagem de gênero\n",
    "    if genero_mapped == 'total':\n",
    "        df_filtered['people_count'] = df_filtered[['male_people_count', 'female_people_count', 'unspecified_people_count']].sum(axis=1)\n",
    "    else:\n",
    "        df_filtered['people_count'] = df_filtered[genero_mapped + '_people_count'].fillna(0)\n",
    "\n",
    "    # Selecionar a coluna de contagem de faixa etária\n",
    "    if faixa_etaria_mapped == 'total':\n",
    "        df_filtered['age_group_count'] = df_filtered[['18_34_people_count', '35_54_people_count', '55_64_people_count', '65_plus_people_count']].sum(axis=1)\n",
    "    else:\n",
    "        df_filtered['age_group_count'] = df_filtered[faixa_etaria_mapped + '_people_count'].fillna(0)\n",
    "\n",
    "    # Filtrar dados relevantes\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 \n",
    "\n",
    "    # Agrupar os dados por 'month' e calcular as somas\n",
    "    grouped = df_filtered.groupby(['month']).agg({\n",
    "        'commute_trip_count': 'sum',\n",
    "        'leisure_trip_count': 'sum',\n",
    "        'total_trip_count': 'sum'\n",
    "    }).reset_index()\n",
    "\n",
    "    # Adicionar uma coluna de nome do mês para facilitar a leitura\n",
    "    month_names = {1: 'Jan', 2: 'Fev', 3: 'Mar', 4: 'Abr', 5: 'Mai', 6: 'Jun',\n",
    "                   7: 'Jul', 8: 'Ago', 9: 'Set', 10: 'Out', 11: 'Nov', 12: 'Dez'}\n",
    "    grouped['month_name'] = grouped['month'].map(month_names)\n",
    "\n",
    "    grouped_melted = grouped.melt(id_vars=['month', 'month_name'], \n",
    "                                  value_vars=['total_trip_count', 'commute_trip_count', 'leisure_trip_count'], \n",
    "                                  var_name='activity_type', \n",
    "                                  value_name='trip_count')\n",
    "\n",
    "    # Mapear os nomes dos tipos de viagem corretamente\n",
    "    trip_type_names = {\n",
    "        'total_trip_count': 'Total',\n",
    "        'commute_trip_count': 'Deslocamento',\n",
    "        'leisure_trip_count': 'Lazer'\n",
    "    }\n",
    "    grouped_melted['trip_type'] = grouped_melted['activity_type'].map(trip_type_names)\n",
    "\n",
    "    # Aplicar o filtro baseado no tipo de atividade selecionado\n",
    "    if activity_type != 'Todos':\n",
    "        grouped_melted = grouped_melted[grouped_melted['trip_type'] == activity_type]\n",
    "\n",
    "    if grouped_melted.empty:\n",
    "        print(\"Não há dados disponíveis para os filtros selecionados.\")\n",
    "        return\n",
    "\n",
    "    grouped_melted.sort_values(['month'], inplace=True)\n",
    "\n",
    "    # Plotar os dados\n",
    "    plt.figure(figsize=(14, 7))\n",
    "\n",
    "    if activity_type == 'Todos':\n",
    "        # Plotar todos os tipos de atividade com hue='trip_type'\n",
    "        sns.lineplot(data=grouped_melted, x='month_name', y='trip_count', hue='trip_type', marker='o')\n",
    "        plt.legend(title='Tipo de Atividade')\n",
    "    else:\n",
    "        # Plotar apenas o tipo de atividade selecionado\n",
    "        sns.lineplot(data=grouped_melted, x='month_name', y='trip_count', marker='o')\n",
    "        plt.legend().set_visible(False)\n",
    "\n",
    "    plt.title(f\"Viagens Mensais em {ano}\")\n",
    "    plt.xlabel('Mês')\n",
    "    plt.ylabel('Número de Viagens')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Tornar a função interativa\n",
    "interact(\n",
    "    update_plot,\n",
    "    activity_type=activity_type_widget,\n",
    "    ano=ano,\n",
    "    genero=genero,\n",
    "    faixa_etaria=faixa_etaria\n",
    ")\n"
   ]
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