Projeto Análise de dados.ipynb

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    "# Importar a base de dados\n",
    "import pandas as pd\n",
    "\n",
    "tabela = pd.read_csv(\"clientes.csv\", encoding=\"latin\", sep=\";\")\n",
    "\n",
    "# Deletar as colunas sem valores\n",
    "\n",
    "# axis = 0 -> Linha\n",
    "# axis = 1 --> Coluna\n",
    "tabela = tabela.drop(\"Unnamed: 8\", axis=1)\n",
    "\n",
    "# Visualizar a base de dados\n",
    "\n",
    "    # Entender as informações que você tem disponível\n",
    "    # Procurar os erros na base de dados\n",
    "display(tabela)"
   ]
  },
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   "cell_type": "code",
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   "source": [
    "# Tratamento de Dados\n",
    "\n",
    "# Corrigir informações que estão sendo reconhecidas de forma errada\n",
    "tabela[\"Salário Anual (R$)\"] = pd.to_numeric(tabela[\"Salário Anual (R$)\"], errors=\"coerce\")\n",
    "\n",
    "# Corrigir informações vazias\n",
    "tabela = tabela.dropna()\n",
    "print(tabela.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee91f01b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Análise Inicial -> Entender as notas dos clientes\n",
    "display(tabela.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da30b2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "# Criar o grafico\n",
    "\n",
    "# Análise Completa -> Entender como cada característica do cliente impacta na nota\n",
    "for coluna in tabela.columns:\n",
    "    grafico = px.histogram(tabela, x=coluna, y=\"Nota (1-100)\", histfunc=\"avg\", text_auto=True, nbins=10)\n",
    "    \n",
    "    # Exibição do gráfico final \n",
    "    grafico.show()"
   ]
  }
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