Aula_03_Pos_Ciencia_De_Dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula 03 - Pos_Ciencia_De_Dados.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "dcgGn3_Zgleu",
        "HvePs-vyEe1g",
        "Qr6eWuGSnxRm",
        "moV99Nvq1GK-"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g2QNYvPogbmy"
      },
      "source": [
        "# **Turma de Pós-Graduação de Ciência de Dados**\n",
        "\n",
        "**Disciplina: Linguagem de Programação Python**\n",
        "\n",
        "**prof: Sérgio Assunção Monteiro, DSc**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwuXXgO-gflX"
      },
      "source": [
        "# **Aula 03**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcgGn3_Zgleu"
      },
      "source": [
        "# **Solução do Exercício da Aula 02**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfzj-rNVEYnM"
      },
      "source": [
        "from numpy.random import seed\n",
        "from numpy.random import normal"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZXj9E7CA06Y"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CQ4MlB0BZQA"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01LUCeADESe3"
      },
      "source": [
        "def gerar_valores(dias):\n",
        "  seed(1)\n",
        "  dados = normal(loc=100, scale=10, size=dias)\n",
        "  return dados"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1R8Es1ykGHvD"
      },
      "source": [
        "def gerar_dados(posicao_grafico):\n",
        "  ndias = 365\n",
        "  dias = np.arange(365)\n",
        "  y = gerar_valores(ndias)\n",
        "  linha  = 2\n",
        "  coluna = 3\n",
        "  plt.subplot(linha, coluna, posicao_grafico)\n",
        "  plt.plot(dias,y)\n",
        "  return y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AUYJZ9ZeH8aG"
      },
      "source": [
        "def analise_exploratoria(dados, investimento):\n",
        "  media = dados.mean()\n",
        "  desvio_padrao=dados.std()\n",
        "  minimo = dados.min()\n",
        "  maximo = dados.max()\n",
        "  print(\"-----------------------------------------------\")\n",
        "  print(\"Análise do Investimento:{}\".format(investimento))\n",
        "  print(\"-----------------------------------------------\")\n",
        "  print(\"media:{}\".format(media))\n",
        "  print(\"desvio padrão:{}\".format(desvio_padrao))\n",
        "  print(\"mínimo:{}\".format(minimo))\n",
        "  print(\"máximo:{}\".format(maximo))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 970
        },
        "id": "UXEjdj5NGbIO",
        "outputId": "8b6ce8ab-2650-4339-d9bd-6b516d82fd0c"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "retorno_1 = gerar_dados(1)\n",
        "retorno_2 = gerar_dados(2)\n",
        "retorno_3 = gerar_dados(3)\n",
        "retorno_4 = gerar_dados(4)\n",
        "retorno_5 = gerar_dados(5)\n",
        "retorno_6 = gerar_dados(6)\n",
        "plt.show()\n",
        "analise_exploratoria(retorno_1,1)\n",
        "analise_exploratoria(retorno_2,2)\n",
        "analise_exploratoria(retorno_3,3)\n",
        "analise_exploratoria(retorno_4,4)\n",
        "analise_exploratoria(retorno_5,5)\n",
        "analise_exploratoria(retorno_6,6)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-----------------------------------------------\n",
            "Análise do Investimento:1\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n",
            "-----------------------------------------------\n",
            "Análise do Investimento:2\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n",
            "-----------------------------------------------\n",
            "Análise do Investimento:3\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n",
            "-----------------------------------------------\n",
            "Análise do Investimento:4\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n",
            "-----------------------------------------------\n",
            "Análise do Investimento:5\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n",
            "-----------------------------------------------\n",
            "Análise do Investimento:6\n",
            "-----------------------------------------------\n",
            "media:100.53136853116376\n",
            "desvio padrão:9.617838986112098\n",
            "mínimo:72.0691499985346\n",
            "máximo:130.3085711237203\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HvePs-vyEe1g"
      },
      "source": [
        "# **Exercício para próxima aula:**\n",
        "**Para cada gráfico do exercício anterior, acrescentar informações sobre:**\n",
        "\n",
        "  > A que investimento o gráfico se refere \n",
        "\n",
        "  > Acrescentar rótulos para os eixos horizontal e vertical\n",
        "\n",
        "  > Usar marcadores para destacar pontos nos gráficos\n",
        "  \n",
        "  > Usar cores diferentes para cada gráfico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPCNcQwbBars"
      },
      "source": [
        "def gerar_dados_2(investimento):\n",
        "  ndias = 365\n",
        "  dias = np.arange(365)\n",
        "  y = gerar_valores(ndias)\n",
        "  return y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "aMKbqf51CMR2",
        "outputId": "d94c0ecd-3b8c-4d15-a4ea-adfa42066f1d"
      },
      "source": [
        "lista_cores = ['red','blue','green','orange','darkgreen','black']\n",
        "lista_marcadores = ['x', '*','x','o','x', '*']\n",
        "fig, axs = plt.subplots(2,3, figsize=(15, 8), sharex=True, sharey=True)\n",
        "for i, ax in enumerate(axs.flat):\n",
        "    ax.plot(gerar_dados_2(i), marker=lista_marcadores[i], color=lista_cores[i])\n",
        "    ax.set_title(f'Investimento {i+1}')\n",
        "\n",
        "plt.setp(axs[-1, :], xlabel='dias')\n",
        "plt.setp(axs[:, 0], ylabel='Retorno')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0, 0.5, 'Retorno'), Text(0, 0.5, 'Retorno')]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbPHhIta3cyJ"
      },
      "source": [
        "**Um pouco mais sobre vetores ...**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buDnY1JE3g7g",
        "outputId": "9d21290b-6eaa-44b6-ceda-2df3123899a3"
      },
      "source": [
        "import numpy as np\n",
        "nums = np.array([1,2,3,4,5])     \n",
        "print('nums = {}'.format(nums))  \n",
        "print('nums[0] = {}'.format(nums[0]))\n",
        "print('nums[2:4] = {}'.format(nums[2:4]))\n",
        "print('nums[2:] = {}'.format(nums[2:]))\n",
        "print('nums[:2] = {}'.format(nums[:2]))\n",
        "print('nums[:] = {}'.format(nums[:]))\n",
        "print('nums[:-1] = {}'.format(nums[:-1]))\n",
        "print('nums[:-2] = {}'.format(nums[:-2]))\n",
        "nums[2:4] = [8, 9]        # Assign a new sublist to a slice\n",
        "print('nums = {}'.format(nums))               # Prints \"[0, 1, 8, 9, 4]\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nums = [1 2 3 4 5]\n",
            "nums[0] = 1\n",
            "nums[2:4] = [3 4]\n",
            "nums[2:] = [3 4 5]\n",
            "nums[:2] = [1 2]\n",
            "nums[:] = [1 2 3 4 5]\n",
            "nums[:-1] = [1 2 3 4]\n",
            "nums[:-2] = [1 2 3]\n",
            "nums = [1 2 8 9 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr6eWuGSnxRm"
      },
      "source": [
        "# **Um pouco mais sobre funções ...**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy970SjEOGcO"
      },
      "source": [
        " ![complex - T01 - 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)\n",
        "\n",
        "\n",
        "**Implementar uma função para resolver o problema**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNirOPTlOKUL"
      },
      "source": [
        "def funcao_somatorio_k_k2(n):\n",
        "  soma = 0\n",
        "  for i in range(n):\n",
        "    k = i +1\n",
        "    soma = soma + 3*k + 2*k**2\n",
        "  return soma\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afFsMFBlOuzQ",
        "outputId": "344acda2-8686-4cd8-d18b-1c0806131a01"
      },
      "source": [
        "resultado = funcao_somatorio_k_k2(10)\n",
        "print('resultado: {}'.format(resultado))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resultado: 935\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tW5XEafGbXLP"
      },
      "source": [
        "**Dada função F definida como:**\n",
        "\n",
        "> F(1) = 1\n",
        "\n",
        "> F(N) = F(N/2) + 1,  para N ≥ 2.\n",
        "\n",
        "\n",
        "**Suponha N= 1, 2, 4, 8, 16, 32, 64, ...**\n",
        "\n",
        "**Implementar uma função para resolver o problema.** \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CC_lskxJb535"
      },
      "source": [
        "def f(n):\n",
        "  if(n==1):\n",
        "    return 1\n",
        "  else:\n",
        "    return f(n/2)+1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usQh4w17cQnf",
        "outputId": "7df71b7c-6a9f-4f09-e7b0-a6b0b69ea4f9"
      },
      "source": [
        "resultado = f(64)\n",
        "print('resultado: {}'.format(resultado))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resultado: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B6OkpMdmpklD"
      },
      "source": [
        "**Obtenha f(n) para cada elemento da lista = [2, 16, 64, 256].**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkO5wRzQpx4b",
        "outputId": "06ee9394-c140-459b-d867-454fa7ad7129"
      },
      "source": [
        "lista = [2, 16, 64, 256]\n",
        "for a in lista:\n",
        "  print('resultado: {}'.format(f(a)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resultado: 2\n",
            "resultado: 5\n",
            "resultado: 7\n",
            "resultado: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkm_lV2rHYG4",
        "outputId": "c982b62d-c908-463f-8be5-9f8ac2f8498c"
      },
      "source": [
        "lista = [2, 16, 64, 256]\n",
        "n = len(lista)\n",
        "for i in range(n):\n",
        "  print('resultado: {}'.format(f(lista[i])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resultado: 2\n",
            "resultado: 5\n",
            "resultado: 7\n",
            "resultado: 9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oabS4Z4eqIAO"
      },
      "source": [
        "**Obter uma lista para dos resultados de f(n) para a lista = [2, 16, 64, 256].**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqscNF9CqWYq"
      },
      "source": [
        "resultado = []\n",
        "lista = [2, 16, 64, 256]\n",
        "for elem in lista:\n",
        "  resultado.append(f(elem))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-RXeK0jqskB",
        "outputId": "6e096453-3110-4fa8-f0e2-7aa67ae767f2"
      },
      "source": [
        "print('resultado: {}'.format(resultado))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "resultado: [2, 5, 7, 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ivPKZ17ig-GN"
      },
      "source": [
        "## **Package Pandas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sbm4SgbkrRUe"
      },
      "source": [
        "**Exemplo 01**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MofNrBE0hRaE"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "0xFlvfpTrkxp",
        "outputId": "1a9db09e-2155-4baf-abc8-750a8733dc3e"
      },
      "source": [
        "dados = {'coluna 01': [1, 2], 'coluna 02': [3, 4]}\n",
        "df = pd.DataFrame(data=dados)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>coluna 01</th>\n",
              "      <th>coluna 02</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   coluna 01  coluna 02\n",
              "0          1          3\n",
              "1          2          4"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5iG38CS3r27h"
      },
      "source": [
        "**Exemplo 02**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "j7vqd4Lfr53p",
        "outputId": "f58327b5-7d16-4bf1-aa80-dbd2ab66a156"
      },
      "source": [
        "dados_2 = {'Produto': ['A', 'B'], 'Preço': [100, 500]}\n",
        "df_2 = pd.DataFrame(data = dados_2)\n",
        "df_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Produto</th>\n",
              "      <th>Preço</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Produto  Preço\n",
              "0       A    100\n",
              "1       B    500"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FzchAgbtqPH",
        "outputId": "88470c06-482f-49fa-ce56-5361a88046d8"
      },
      "source": [
        "n = len(df_2.Produto)\n",
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GDbRBcETL-3w",
        "outputId": "f482d50a-fa3e-4cbd-87cc-7b0a5314b36d"
      },
      "source": [
        "n = len(df_2.Preço)\n",
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e0K0ceJOvRTA"
      },
      "source": [
        "**Adicionar nova coluna**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cl6jeVFmtykH"
      },
      "source": [
        "df_2['Estoque'] = [200, 707]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "FfsSs2NSvlXz",
        "outputId": "627068d7-9522-4930-c765-f3d0b0d47b45"
      },
      "source": [
        "df_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Produto</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Estoque</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>500</td>\n",
              "      <td>707</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Produto  Preço  Estoque\n",
              "0       A    100      200\n",
              "1       B    500      707"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PocMoc6vwYCJ"
      },
      "source": [
        "**Outra forma de adicionar uma coluna**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoUMjCQvwcqV"
      },
      "source": [
        "df_2.insert(loc=3, column=\"Categoria\", value =['Suco', 'Cereal'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "S85lFBRQxGwV",
        "outputId": "d290222e-f909-4252-a6ec-8784d0171a13"
      },
      "source": [
        "df_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Produto</th>\n",
              "      <th>Preço</th>\n",
              "      <th>Estoque</th>\n",
              "      <th>Categoria</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>100</td>\n",
              "      <td>200</td>\n",
              "      <td>Suco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>500</td>\n",
              "      <td>707</td>\n",
              "      <td>Cereal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Produto  Preço  Estoque Categoria\n",
              "0       A    100      200      Suco\n",
              "1       B    500      707    Cereal"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "moV99Nvq1GK-"
      },
      "source": [
        "# **Visualização de Dados**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sagJuXA2Qys"
      },
      "source": [
        "**Gráfico de Barras**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "K5iDNYYv2T6c",
        "outputId": "7e7fdb12-4178-46e0-a3e7-a358b965f8a0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
        "y = np.array([3, 8, 1, 10])\n",
        "\n",
        "plt.bar(x,y)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Jiqux7PR-hvv",
        "outputId": "d84335d7-17b4-4935-f6f8-652086662e81"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
        "y = np.array([3, 8, 1, 10])\n",
        "\n",
        "plt.barh(x, y)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Ap_-LUi_-n6O",
        "outputId": "b58b2998-afe7-4039-d6de-e4d43b05904e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.array([\"A\", \"B\", \"C\", \"D\"])\n",
        "y = np.array([3, 8, 1, 10])\n",
        "\n",
        "plt.bar(x, y, color = \"red\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWNt-aRN2MGs"
      },
      "source": [
        "**Histograma**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "BdFd1XlX-_Gn",
        "outputId": "0b214b67-6e10-46e4-b68e-1ad328321979"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.random.normal(170, 10, 250)\n",
        "\n",
        "plt.hist(x)\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "7D4h6cMs2Phi",
        "outputId": "21ff63bb-4520-41b4-b82e-10d2e8350168"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "np.random.seed(1)\n",
        "dados = np.random.normal(loc=0, scale=1, size=200)\n",
        "\n",
        "count, bins, ignored = plt.hist(dados, 30)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOPi_a6t21Us"
      },
      "source": [
        "# **Exercícios:** Implementar uma solução que receba inicialmente um DataFrame com o nome de quatro ativos. Em seguida, você precisa adicionar uma nova coluna no DataFrame com os preços para cada ativo. Esses valores devem ser gerados seguindo a distribuição normal com média igual a 100 e desvio-padrão 7. Por fim, imprima um gráfico de barras que relacione cada ativo com seu preço."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solução: próxima aula**"
      ],
      "metadata": {
        "id": "1B0jE9mjpM8T"
      }
    }
  ]
}