Apostila2_BootCampPython(pandas_scikitlearn).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Apostila2_BootCampPython(pandas/scikitlearn).ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNfYuOTSD+10F+FXNHaivJD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raulc27/python_testes/blob/master/Apostila2_BootCampPython(pandas_scikitlearn).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0pjzoiP4h6s"
      },
      "source": [
        "#importando as bibliotecas\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4BXuwUI88nhl"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCM_jXIl4z0s"
      },
      "source": [
        "# leitura dos dados\n",
        "df = pd.read_csv(\"https://pycourse.s3.amazonaws.com/temperature.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "YhfemD9Y5BLA",
        "outputId": "c9ebda75-2672-431e-add5-e6bf5ea3a206"
      },
      "source": [
        "df.head(3)"
      ],
      "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>date</th>\n",
              "      <th>temperatura</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-01</td>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  temperatura classification\n",
              "0  2020-01-01         29.1         quente\n",
              "1  2020-02-01         31.2   muito quente\n",
              "2  2020-03-01         28.5         quente"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-4dcRvu5Qqu"
      },
      "source": [
        "# transformando o tipo da coluna date para datetime\n",
        "df['date'] = pd.to_datetime(df['date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiVlgzcJ5g7w"
      },
      "source": [
        "# setando o índice\n",
        "df = df.set_index('date')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hgqIRcXy5lfe",
        "outputId": "9604d09d-14d7-42c2-9ae4-84fb4914237b"
      },
      "source": [
        "#visualizando o índice\n",
        "print(df.index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DatetimeIndex(['2020-01-01', '2020-02-01', '2020-03-01', '2020-04-01',\n",
            "               '2020-05-01', '2020-06-01'],\n",
            "              dtype='datetime64[ns]', name='date', freq=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yFGggnr85r8L",
        "outputId": "5462efee-1079-4164-c04e-b55e79ef3764"
      },
      "source": [
        "# estatísticas básicas de dados numéricos\n",
        "df.describe()"
      ],
      "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>temperatura</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.075782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>25.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>28.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>31.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       temperatura\n",
              "count     6.000000\n",
              "mean     26.800000\n",
              "std       4.075782\n",
              "min      20.000000\n",
              "25%      25.000000\n",
              "50%      28.250000\n",
              "75%      28.950000\n",
              "max      31.200000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nU-9sCN952pH",
        "outputId": "8def7f26-62a2-4866-cc28-0ced49747609"
      },
      "source": [
        "# indexação por índice selecionado todas as linhas\n",
        "# e a coluna 1\n",
        "# coluna 1: temperatura\n",
        "df.iloc[:, 1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2020-01-01          quente\n",
              "2020-02-01    muito quente\n",
              "2020-03-01          quente\n",
              "2020-04-01          quente\n",
              "2020-05-01     confortavel\n",
              "2020-06-01            frio\n",
              "Name: classification, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1cbimirM6Dws",
        "outputId": "69a7d6be-f7e6-4726-b28f-40f45b6e1548"
      },
      "source": [
        "# indexação por nome\n",
        "# selecionado todas as linhas e a coluna 1\n",
        "df.loc[:, 'temperatura']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2020-01-01    29.1\n",
              "2020-02-01    31.2\n",
              "2020-03-01    28.5\n",
              "2020-04-01    28.0\n",
              "2020-05-01    24.0\n",
              "2020-06-01    20.0\n",
              "Name: temperatura, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "fj1TNSCy6Mqi",
        "outputId": "8c6606c7-c237-4025-df25-370fe8abfcdd"
      },
      "source": [
        "# ordenando por uma coluna\n",
        "df.sort_values(by='temperatura')"
      ],
      "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>temperatura</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-06-01         20.0           frio\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-04-01         28.0         quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-01-01         29.1         quente\n",
              "2020-02-01         31.2   muito quente"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "5smFp9V86XIE",
        "outputId": "f82d4157-f69e-4635-dc2b-e2218ca62fc9"
      },
      "source": [
        "# ordenando pelo índice\n",
        "df.sort_index(ascending=False)"
      ],
      "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>temperatura</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-06-01         20.0           frio\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-04-01         28.0         quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-01-01         29.1         quente"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0tnZdUOg6gmW",
        "outputId": "71549c4f-5658-4b05-8a07-dd867d849ec6"
      },
      "source": [
        "# indexação booleana\n",
        "# seleção de exemplos acima de 25 graus\n",
        "df[df['temperatura']>=25]"
      ],
      "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>temperatura</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-01-01         29.1         quente\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-04-01         28.0         quente"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "i-7qJJv96quS",
        "outputId": "b5c75776-3d84-4c02-889b-7f4796950b82"
      },
      "source": [
        "# indexação booleana considerando datetime\n",
        "# seleção de entradas até MArço de 2020\n",
        "df[df.index<='2020-03-01']"
      ],
      "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>temperatura</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-01-01         29.1         quente\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-03-01         28.5         quente"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "bF777zXZ69QS",
        "outputId": "b738b7fa-623c-4b61-bc9d-d00c793965e3"
      },
      "source": [
        "# plot de linhas\n",
        "df.plot(style='-o', figsize=(10,5), grid=True);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "aFGpZ34x7KmZ",
        "outputId": "574a3e65-851a-4d02-9eb1-a909a9ded107"
      },
      "source": [
        "# pie plot\n",
        "df['classification'].value_counts().plot.pie(autopct='%1.1f%%', shadow=True, figsize=(10,7));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFLkxygE7mCt"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-1fiqPP83uU"
      },
      "source": [
        "# SCIKIT LEARN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCzOlCfo88HM",
        "outputId": "1b6e58f5-fd4f-448e-d1e3-70f6c16f8fd5"
      },
      "source": [
        "# extraçao de x e y\n",
        "x,y=df[['temperatura']].values, df[['classification']].values\n",
        "print(\"x:\\n\", x)\n",
        "print(\"y:\\n\", y)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "x:\n",
            " [[29.1]\n",
            " [31.2]\n",
            " [28.5]\n",
            " [28. ]\n",
            " [24. ]\n",
            " [20. ]]\n",
            "y:\n",
            " [['quente']\n",
            " ['muito quente']\n",
            " ['quente']\n",
            " ['quente']\n",
            " ['confortavel']\n",
            " ['frio']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWI1IZgj9adq"
      },
      "source": [
        "# pré-processamento\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKUGuvLI9hYw",
        "outputId": "ec835e2a-3d73-4b17-94a2-006c1fdbd5ba"
      },
      "source": [
        "# conversão de y para valores numéricos\n",
        "le = LabelEncoder()\n",
        "y = le.fit_transform(y.ravel())\n",
        "print(\"y:\\n\",y)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "y:\n",
            " [3 2 3 3 0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDqFokZZ9wek"
      },
      "source": [
        "# modelo\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOXF-ONw92GQ",
        "outputId": "47dfc443-dfce-4992-e339-5c207efe14c1"
      },
      "source": [
        "# classificador\n",
        "clf = LogisticRegression()\n",
        "clf.fit(x,y)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OiHkOq2-KD0"
      },
      "source": [
        "# gerando 100 valores de temperatura\n",
        "# linearmente espaçados entre 0 e 45\n",
        "# predição em novos valores de temperatura\n",
        "x_test = np.linspace(start=0.,stop=45.,num=100).reshape(-1,1)\n",
        "\n",
        "# predição desses valores\n",
        "y_pred = clf.predict(x_test)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdXj4EWS-qdq"
      },
      "source": [
        "# conversão de y_pred para os valores originais\n",
        "y_pred = le.inverse_transform(y_pred)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWDwpoxf-7gR"
      },
      "source": [
        "# output\n",
        "output = { 'new_temp': x_test.ravel(), 'new_class': y_pred.ravel()}\n",
        "output = pd.DataFrame(output)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxrQmJl5_H-6",
        "outputId": "ddacd754-90fb-4833-d773-9aced5c844c6"
      },
      "source": [
        "# estatísticas\n",
        "output.info()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 2 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   new_temp   100 non-null    float64\n",
            " 1   new_class  100 non-null    object \n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 1.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "aZapuu8G_WpK",
        "outputId": "e3142cfe-8f40-41de-c3f4-9c99e796676b"
      },
      "source": [
        "# Distribuição do outoput produzido\n",
        "# conseguimos inferir a classificação novas temperaturas\n",
        "# a partir de um dataset com 6 exemplos\n",
        "output.boxplot(by='new_class', figsize=(10,5))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f95d1f38b10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}