{
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   "outputs": [],
   "source": [
    "# Carregando\n",
    "#começa tratando os dados e limpa a base\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "\n",
    "dados = pd.read_csv('BaciaRioDoce_filtro_setembro_abril.csv', sep=';', encoding='utf-8', decimal=',')\n",
    "#dados.head(5)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Erro ao converter a coluna 'DATA': name 'dados_df' is not defined\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12342 entries, 0 to 12341\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   CODIGO  12342 non-null  int64         \n",
      " 1   DATA    12342 non-null  datetime64[ns]\n",
      " 2   VALOR   12342 non-null  object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 289.4+ KB\n",
      "None\n",
      "   CODIGO       DATA VALOR\n",
      "0   10004 2020-09-01     0\n",
      "1   10004 2020-09-02     0\n",
      "2   10004 2020-09-03     0\n",
      "3   10004 2020-09-04     0\n",
      "4   10004 2020-09-05     0\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    dados['DATA'] = pd.to_datetime(dados['DATA'], format='%Y-%m-%d', errors='coerce')\n",
    "    if dados_df['DATA'].isnull().any():\n",
    "        dados(\"Aviso: Algumas datas foram convertidas para NaT (Not a Time) devido a formatos inválidos.\")\n",
    "except Exception as e:\n",
    "    print(f\"Erro ao converter a coluna 'DATA': {e}\")\n",
    "\n",
    "# Verificar o DataFrame após a conversão\n",
    "print(dados.info())\n",
    "print(dados.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12342 entries, 0 to 12341\n",
      "Data columns (total 3 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   CODIGO  12342 non-null  int64         \n",
      " 1   DATA    12342 non-null  datetime64[ns]\n",
      " 2   VALOR   12342 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(1), int64(1)\n",
      "memory usage: 289.4 KB\n",
      "None\n",
      "   CODIGO       DATA  VALOR\n",
      "0   10004 2020-09-01    0.0\n",
      "1   10004 2020-09-02    0.0\n",
      "2   10004 2020-09-03    0.0\n",
      "3   10004 2020-09-04    0.0\n",
      "4   10004 2020-09-05    0.0\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    dados['VALOR'] = dados['VALOR'].astype(str)  # Garantir que todos os valores são strings\n",
    "    \n",
    "    # Substituir vírgulas por pontos\n",
    "    dados['VALOR'] = dados['VALOR'].str.replace(',', '.', regex=False)\n",
    "    \n",
    "    # Converter a coluna 'VALOR' para float\n",
    "    dados['VALOR'] = pd.to_numeric(dados['VALOR'], errors='coerce')\n",
    "    \n",
    "    # Tratar valores NaN substituindo por 0\n",
    "    dados['VALOR'] = dados['VALOR'].fillna(0)\n",
    "    \n",
    "except Exception as e:\n",
    "    print(f\"Erro ao converter a coluna 'VALOR': {e}\")\n",
    "\n",
    "# Verificar o DataFrame após a conversão\n",
    "print(dados.info())\n",
    "print(dados.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": []
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