[IGTI]_Bootcamp_Módulo_2.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGa1ElkAlEha"
      },
      "source": [
        "## **Numpy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aL0_UKQ-n8pg"
      },
      "source": [
        "# importando as bibliotecas\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "glXv_BQc-eKm"
      },
      "source": [
        "### **Criação de arrays**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJDkzf_GcIPj"
      },
      "source": [
        "np.array?"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6fyF9yoT9VQ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b3aed42d-a066-43ef-9d4c-4872043f00de"
      },
      "source": [
        "# criação de um array 1D: [1, 2, 3]\n",
        "l = [1, 2, 3]\n",
        "x = np.array(l)\n",
        "print(\"x:\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [1 2 3]\n",
            "shape: (3,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3s3W1ZHLdzs_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1bb85457-cf18-4ffd-9f32-e343b6ff86a7"
      },
      "source": [
        "type(x)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2_U-rjj9iN3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e6da9f81-df73-46e0-cf58-e920661ecd6d"
      },
      "source": [
        "# criação de um array 2D: listas aninhadas\n",
        "l = [[1, 2], [3, 4]]\n",
        "x = np.array(l)\n",
        "print(\"x:\\n\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[1 2]\n",
            " [3 4]]\n",
            "shape: (2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45OpNTFPeO5D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fc0afde7-7794-4e9c-857d-077de16851d6"
      },
      "source": [
        "type(x)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYd7frOFeWyh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f22422a1-8884-477f-c07e-9309865a33a8"
      },
      "source": [
        "help(np.zeros)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on built-in function zeros in module numpy:\n",
            "\n",
            "zeros(...)\n",
            "    zeros(shape, dtype=float, order='C', *, like=None)\n",
            "    \n",
            "    Return a new array of given shape and type, filled with zeros.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    shape : int or tuple of ints\n",
            "        Shape of the new array, e.g., ``(2, 3)`` or ``2``.\n",
            "    dtype : data-type, optional\n",
            "        The desired data-type for the array, e.g., `numpy.int8`.  Default is\n",
            "        `numpy.float64`.\n",
            "    order : {'C', 'F'}, optional, default: 'C'\n",
            "        Whether to store multi-dimensional data in row-major\n",
            "        (C-style) or column-major (Fortran-style) order in\n",
            "        memory.\n",
            "    like : array_like\n",
            "        Reference object to allow the creation of arrays which are not\n",
            "        NumPy arrays. If an array-like passed in as ``like`` supports\n",
            "        the ``__array_function__`` protocol, the result will be defined\n",
            "        by it. In this case, it ensures the creation of an array object\n",
            "        compatible with that passed in via this argument.\n",
            "    \n",
            "        .. versionadded:: 1.20.0\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    out : ndarray\n",
            "        Array of zeros with the given shape, dtype, and order.\n",
            "    \n",
            "    See Also\n",
            "    --------\n",
            "    zeros_like : Return an array of zeros with shape and type of input.\n",
            "    empty : Return a new uninitialized array.\n",
            "    ones : Return a new array setting values to one.\n",
            "    full : Return a new array of given shape filled with value.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> np.zeros(5)\n",
            "    array([ 0.,  0.,  0.,  0.,  0.])\n",
            "    \n",
            "    >>> np.zeros((5,), dtype=int)\n",
            "    array([0, 0, 0, 0, 0])\n",
            "    \n",
            "    >>> np.zeros((2, 1))\n",
            "    array([[ 0.],\n",
            "           [ 0.]])\n",
            "    \n",
            "    >>> s = (2,2)\n",
            "    >>> np.zeros(s)\n",
            "    array([[ 0.,  0.],\n",
            "           [ 0.,  0.]])\n",
            "    \n",
            "    >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype\n",
            "    array([(0, 0), (0, 0)],\n",
            "          dtype=[('x', '<i4'), ('y', '<i4')])\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XjnO6UaW9_rr",
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          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bb763868-5a25-4035-a382-0d7d39f53c74"
      },
      "source": [
        "# array contendo apenas 0's\n",
        "dim = (2, 2)  # (linhas, colunas)\n",
        "x = np.zeros(dim)\n",
        "print(\"x:\\n\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[0. 0.]\n",
            " [0. 0.]]\n",
            "shape: (2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPPcYGt4_fzU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "431d5cac-955f-4569-daa5-e5c9c6cb120a"
      },
      "source": [
        "# array contendo apenas 1's\n",
        "size = (2, 2)  # (linhas, colunas)\n",
        "x = np.ones(size)\n",
        "print(\"x:\\n\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[1. 1.]\n",
            " [1. 1.]]\n",
            "shape: (2, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FD3QynDnfXyh"
      },
      "source": [
        "np.linspace?"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8eiwDYp_u_E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "84cab51e-c867-4e0f-ee3a-34e348f57533"
      },
      "source": [
        "# criação de valores dentro de um intervalo\n",
        "# valores uniformes entre 5 e 15\n",
        "x_min, x_max = 5, 15\n",
        "x = np.linspace(start=x_min, stop=x_max, num=6)\n",
        "print(\"x:\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [ 5.  7.  9. 11. 13. 15.]\n",
            "shape: (6,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfLgXJBSAOYp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cbc449a0-6c1c-46bb-c9ea-d1b9dafb4f0f"
      },
      "source": [
        "# criação da matriz identidade\n",
        "n = 4\n",
        "x = np.eye(n)\n",
        "print(\"x:\\n\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[1. 0. 0. 0.]\n",
            " [0. 1. 0. 0.]\n",
            " [0. 0. 1. 0.]\n",
            " [0. 0. 0. 1.]]\n",
            "shape: (4, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3UOujghVgN7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e1b6a4de-dd70-49f5-dd4c-a1b5741dc0c9"
      },
      "source": [
        "help(np.random.random)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on built-in function random:\n",
            "\n",
            "random(...) method of numpy.random.mtrand.RandomState instance\n",
            "    random(size=None)\n",
            "    \n",
            "    Return random floats in the half-open interval [0.0, 1.0). Alias for\n",
            "    `random_sample` to ease forward-porting to the new random API.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A618d3pvCPsC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "76351781-543f-42ce-9a4f-52d0b55445fe"
      },
      "source": [
        "# criação de valores aleatórios\n",
        "# np.random.seed(10)\n",
        "x = np.random.random(size=(2, 3))\n",
        "print(\"x:\\n\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[0.14421804 0.28626442 0.17581553]\n",
            " [0.99574375 0.52119558 0.42634111]]\n",
            "shape: (2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "769s_NzwDZII"
      },
      "source": [
        "### **Indexação de arrays**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSSxZMAKCaEb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6e4ed1e9-d14c-49f5-e776-ac488398d343"
      },
      "source": [
        "# os índices no Python vão de 0 a n-1,\n",
        "# onde n é o tamanho da dimensão\n",
        "x = np.linspace(start=10, stop=100, num=10)\n",
        "print(\"x:\", x)\n",
        "print(\"shape:\", x.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [ 10.  20.  30.  40.  50.  60.  70.  80.  90. 100.]\n",
            "shape: (10,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql5HQ3KhDzVm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "0bc420e2-f8e7-46bc-a276-24376117a2cf"
      },
      "source": [
        "# extração de elementos\n",
        "print(\"x:\", x)\n",
        "print(\"primeiro elemento:\", x[0])\n",
        "print(\"segundo elemento:\", x[1])\n",
        "print(\"último elemento:\", x[9])\n",
        "print(\"último elemento:\", x[-1])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [ 10.  20.  30.  40.  50.  60.  70.  80.  90. 100.]\n",
            "primeiro elemento: 10.0\n",
            "segundo elemento: 20.0\n",
            "último elemento: 100.0\n",
            "último elemento: 100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ds3lSMhqqjIz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "561e5525-f3e4-4b68-dd2a-3d22bdfd5e2c"
      },
      "source": [
        "x[5]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60.0"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kam3n9fJD4a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "39cf3e4b-8263-44ff-d481-1fb049063258"
      },
      "source": [
        "# slicing: extração de subarrays: \n",
        "print(\"x:\", x)\n",
        "print(\"dois primeiros elementos:\", x[0:2])  # 2 é exclusivo\n",
        "print(\"dois primeiros elementos:\", x[:2])\n",
        "print(\"dois últimos elementos:\", x[-2:])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: [ 10.  20.  30.  40.  50.  60.  70.  80.  90. 100.]\n",
            "dois primeiros elementos: [10. 20.]\n",
            "dois primeiros elementos: [10. 20.]\n",
            "dois últimos elementos: [ 90. 100.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14-JCNQBEg_4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "73e8b33f-b2b7-4068-e264-fe1e5e603441"
      },
      "source": [
        "# slicing em arrays 2D (matrizes)\n",
        "x = x.reshape(2, 5)  # reshape de x para 2 linhas e 5 colunas\n",
        "print(\"x:\\n\", x)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 10.  20.  30.  40.  50.]\n",
            " [ 60.  70.  80.  90. 100.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1_npwPKAGgu_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8bdd94c6-bbdd-4043-c153-01fff18a60d5"
      },
      "source": [
        "# extração de elementos\n",
        "print(\"primeira linha, segunda coluna:\", x[0, 1])\n",
        "print(\"segunda linha, penúltima coluna:\", x[1, -2])\n",
        "print(\"última linha, última coluna:\", x[1, 4])\n",
        "print(\"última linha, última coluna:\", x[-1, -1])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "primeira linha, segunda coluna: 20.0\n",
            "segunda linha, penúltima coluna: 90.0\n",
            "última linha, última coluna: 100.0\n",
            "última linha, última coluna: 100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X19pfvbVHT3X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9b6a41df-0825-4a24-becd-e85595eb8916"
      },
      "source": [
        "# slicing: extração de subarrays\n",
        "print(\"x:\\n\", x)\n",
        "print(\"primeira linha inteira: \", x[0, :])\n",
        "print(\"primeira linha, segunda a quarta coluna: \", x[0, 1:4])\n",
        "print(\"última coluna inteira:\\n\", x[:, [-1]])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 10.  20.  30.  40.  50.]\n",
            " [ 60.  70.  80.  90. 100.]]\n",
            "primeira linha inteira:  [10. 20. 30. 40. 50.]\n",
            "primeira linha, segunda a quarta coluna:  [20. 30. 40.]\n",
            "última coluna inteira:\n",
            " [[ 50.]\n",
            " [100.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l320gP9OH6dq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4244e1e4-7b03-481f-e18b-cd8b4f467e05"
      },
      "source": [
        "# atenção com compartilhamento de memória em subarrays!!\n",
        "x = np.array([1, 2, 3])\n",
        "print(\"x antes:\", x)\n",
        "y = x[:2]\n",
        "y[0] = -100  # alteração do valor em y altera o valor de x\n",
        "print(\"x depois:\", x)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x antes: [1 2 3]\n",
            "x depois: [-100    2    3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zfM3tL3HI4yX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "177075bc-e505-4567-f7ad-e1efb94637a5"
      },
      "source": [
        "# atenção com compartilhamento de memória em subarrays!!\n",
        "x = np.array([1, 2, 3])\n",
        "print(\"x antes:\", x)\n",
        "y = x[:2].copy()\n",
        "y[0] = -100  # alteração do valor em y altera o valor de x\n",
        "print(\"x depois:\", x)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x antes: [1 2 3]\n",
            "x depois: [1 2 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emFS9rYEJlwa"
      },
      "source": [
        "### **Funções aritméticas: soma, subtração e divisão**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cxCuJmuCJE_f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "613389ca-f566-4c25-f24e-70c30aba2940"
      },
      "source": [
        "# criação de dois arrays x e y\n",
        "x = np.ones((2, 2))\n",
        "y = np.eye(2)\n",
        "print(\"x: \\n\", x)\n",
        "print(\"y: \\n\", y)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: \n",
            " [[1. 1.]\n",
            " [1. 1.]]\n",
            "y: \n",
            " [[1. 0.]\n",
            " [0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWB48NHLJ3Pt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fd1788cc-fb18-4c57-c1af-87a7ea1ef284"
      },
      "source": [
        "# soma\n",
        "print(\"soma de dois arrays:\\n\", x + y)\n",
        "print(\"soma com float/int:\\n\", x + 2.)  # broadcasting"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "soma de dois arrays:\n",
            " [[2. 1.]\n",
            " [1. 2.]]\n",
            "soma com float/int:\n",
            " [[3. 3.]\n",
            " [3. 3.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqG4J9jjKEHu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ea0763e5-973f-42d6-b485-1c26f14e54ef"
      },
      "source": [
        "# subtração\n",
        "print(\"subtração de dois arrays:\\n\", x - y)\n",
        "print(\"subtração com float/int:\\n\", x - 2)  # broadcasting"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "subtração de dois arrays:\n",
            " [[0. 1.]\n",
            " [1. 0.]]\n",
            "subtração com float/int:\n",
            " [[-1. -1.]\n",
            " [-1. -1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "napi10qVKdJi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8d4cee20-5835-40c6-ebfa-cfdfc692ea5b"
      },
      "source": [
        "# divisão\n",
        "print(\"divisão de dois arrays:\\n\", x / y)\n",
        "print(\"divisão com float/int:\\n\", x / 2)  # broadcasting"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "divisão de dois arrays:\n",
            " [[ 1. inf]\n",
            " [inf  1.]]\n",
            "divisão com float/int:\n",
            " [[0.5 0.5]\n",
            " [0.5 0.5]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-99320470f874>:2: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  print(\"divisão de dois arrays:\\n\", x / y)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWqAQ9kfMzZU"
      },
      "source": [
        "# quando o broadcasting não funciona\n",
        "# np.array([1, 2, 3]) + np.array([1, 2])"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yTSOAIAdLpXb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cc592b90-cee3-42fa-f515-008ed340169e"
      },
      "source": [
        "# soma, subtração e divisão\n",
        "print(\"combinação de operações: \\n\", (x+y)/(x-2))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "combinação de operações: \n",
            " [[-2. -1.]\n",
            " [-1. -2.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dvkMMUaRMlsQ"
      },
      "source": [
        "### **Funções aritméticas: multiplicação**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUz-8PHIMeFh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4d9c7fd2-278c-4cf3-a4cd-7c4ee8bfe626"
      },
      "source": [
        "# multiplicação elemento a elemento\n",
        "print(\"x: \\n\", x)\n",
        "print(\"y: \\n\", y)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: \n",
            " [[1. 1.]\n",
            " [1. 1.]]\n",
            "y: \n",
            " [[1. 0.]\n",
            " [0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mHOzJbLFNu5I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "93e8937a-fa85-441b-b558-554c3957816a"
      },
      "source": [
        "# multiplicação\n",
        "print(\"multiplicação de dois arrays:\\n\", x * y)\n",
        "print(\"multiplicação com float/int:\\n\", x * 2)  # broadcasting"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "multiplicação de dois arrays:\n",
            " [[1. 0.]\n",
            " [0. 1.]]\n",
            "multiplicação com float/int:\n",
            " [[2. 2.]\n",
            " [2. 2.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ndmeNPMPN7Fn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f5209e27-5a53-4d64-bbb4-2271e84294a6"
      },
      "source": [
        "# multiplicação matricial\n",
        "print(\"multiplicação matricial (np.dot):\\n\", np.dot(x, y))\n",
        "print(\"multiplicação matricial (@):\\n\", x @ y)\n",
        "print(\"multiplicação matricial (.dot):\\n\", x.dot(y))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "multiplicação matricial (np.dot):\n",
            " [[1. 1.]\n",
            " [1. 1.]]\n",
            "multiplicação matricial (@):\n",
            " [[1. 1.]\n",
            " [1. 1.]]\n",
            "multiplicação matricial (.dot):\n",
            " [[1. 1.]\n",
            " [1. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bm7Bwx_XONkg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6db7aab7-5200-4b5c-9fdc-faefaf2623a2"
      },
      "source": [
        "\"\"\" \n",
        "Exemplo:\n",
        "Solução de um sistema de equações:\n",
        "  1*a + 2*b = 7\n",
        "  3*a - 2*b = -11\n",
        "  solução análitica: (a, b) = (-1, 4)\n",
        "Matricialmente, este problema tem a seguinte forma:\n",
        "  Ax = c, onde:\n",
        "  - x = [a, b]\n",
        "  - A = [[1, 2], [3, -2]]\n",
        "  - c = [7, -11]\n",
        "  solução numérica: x = inv(A) @ c, \n",
        "\"\"\"\n",
        " \n",
        "# definição do problema\n",
        "A = np.array([[1, 2], [3, -2]])\n",
        "c = np.array([[7], [-11]])\n",
        "print(\"A: \\n\", A)\n",
        "print(\"c: \\n\", c)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A: \n",
            " [[ 1  2]\n",
            " [ 3 -2]]\n",
            "c: \n",
            " [[  7]\n",
            " [-11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHXwax4qzKL-"
      },
      "source": [
        "Sistema de equações:\n",
        "\n",
        "$$Ax=c,\\\\\\left[\\begin{array}{cc}1&2\\\\3&-2\\end{array}\\right]\\left[\\begin{array}{c}a\\\\b\\end{array}\\right] = \\left[\\begin{array}{c}7\\\\-11\\end{array}\\right].$$\n",
        "\n",
        "Solução:\n",
        "\n",
        "$$x=A^{-1}c.$$\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4kJ62UMOQTwp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3f7e1f67-edc2-4bbd-ad47-31e9d221e36b"
      },
      "source": [
        "# solução\n",
        "x = np.dot(np.linalg.inv(A), c)\n",
        "# x = np.linalg.inv(A) @ c\n",
        "print(\"(a, b):\", x.ravel())"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(a, b): [-1.  4.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IQZaGV6jRRD5"
      },
      "source": [
        "### **Comparações**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJqFMWubQdRp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f1f16d9e-5f5f-4d2a-cdf2-6fbe9970d866"
      },
      "source": [
        "# criação dos arrays\n",
        "x = np.array([[1, 2], [3, 4]])\n",
        "y = np.array([1.5, 3.5])\n",
        "print(\"x: \\n\", x)\n",
        "print(\"y: \\n\", y)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x: \n",
            " [[1 2]\n",
            " [3 4]]\n",
            "y: \n",
            " [1.5 3.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJI8UlGSSo5i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "cad8d9ad-c0ae-4727-8e61-6e39119461d9"
      },
      "source": [
        "# comparações ponto a ponto\n",
        "print(\"Comparação de um array com um escalar (>): \\n\", x > 2)  \n",
        "print(\"Comparação de um array com um escalar (>=): \\n\", x >= 2)\n",
        "print(\"Comparação de um array com um escalar (<): \\n\", x < 2)  \n",
        "print(\"Comparação de um array com um escalar (<=): \\n\", x <= 2)\n",
        "print(\"Comparação entre arrays (==): \\n\", x == x)\n",
        "print(\"Comparação entre arrays (>): \\n\", x > x)\n",
        "print(\"Comparação entre arrays (>): \\n\", x > y)  # broadcasting"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Comparação de um array com um escalar (>): \n",
            " [[False False]\n",
            " [ True  True]]\n",
            "Comparação de um array com um escalar (>=): \n",
            " [[False  True]\n",
            " [ True  True]]\n",
            "Comparação de um array com um escalar (<): \n",
            " [[ True False]\n",
            " [False False]]\n",
            "Comparação de um array com um escalar (<=): \n",
            " [[ True  True]\n",
            " [False False]]\n",
            "Comparação entre arrays (==): \n",
            " [[ True  True]\n",
            " [ True  True]]\n",
            "Comparação entre arrays (>): \n",
            " [[False False]\n",
            " [False False]]\n",
            "Comparação entre arrays (>): \n",
            " [[False False]\n",
            " [ True  True]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbgWm1XGVdiq"
      },
      "source": [
        "### **Indexação booleana**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCq137a7Sr8s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a92facba-ba85-42dd-dd0e-70b0e43747b5"
      },
      "source": [
        "# indexação booleana\n",
        "x = np.array([[1, 3, 7],\n",
        "              [4, 11, 21],\n",
        "              [42, 8, 9]])\n",
        "print(\"x:\\n\", x)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 1  3  7]\n",
            " [ 4 11 21]\n",
            " [42  8  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIBCawWNYtTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c80cd869-ef26-463f-8e81-582ed1dc6d83"
      },
      "source": [
        "# indexação boolena: retornar o número de elementos\n",
        "# maiores que k\n",
        "k = 10\n",
        "cond = x > k\n",
        "print(\"condição: \\n\", cond)\n",
        "print(f\"elementos maiores que {k}:\", x[cond])\n",
        "print(f\"números de elementos maiores que {k}:\", len(x[cond]))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "condição: \n",
            " [[False False False]\n",
            " [False  True  True]\n",
            " [ True False False]]\n",
            "elementos maiores que 10: [11 21 42]\n",
            "números de elementos maiores que 10: 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dgpjx5xnV1Nf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c11b3090-25aa-4925-ef17-17b72e63e762"
      },
      "source": [
        "# indexação boolena: extração dos números pares\n",
        "cond = x % 2 == 0  # números pares\n",
        "print(\"condição: \\n\", cond)\n",
        "print(\"números pares:\", x[cond])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "condição: \n",
            " [[False False False]\n",
            " [ True False False]\n",
            " [ True  True False]]\n",
            "números pares: [ 4 42  8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4xjwnpgWZNL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1fae612e-577f-46b4-b479-b6a278019ee8"
      },
      "source": [
        "# indexação boolena: extração dos números ímpares\n",
        "cond = x % 2 == 1  # números pares\n",
        "print(\"condição: \\n\", cond)\n",
        "print(\"números pares:\", x[cond])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "condição: \n",
            " [[ True  True  True]\n",
            " [False  True  True]\n",
            " [False False  True]]\n",
            "números pares: [ 1  3  7 11 21  9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oIfWCzfoaSzL"
      },
      "source": [
        "### **Outras operações úteis em numpy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77006yraaxVQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "dd91aa2a-5319-49b5-eb4a-b1510b8e6e97"
      },
      "source": [
        "# array\n",
        "x = np.array([[1, 3, 7],\n",
        "              [4, 11, 21],\n",
        "              [42, 8, 9]])\n",
        "print(\"x:\\n\", x)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 1  3  7]\n",
            " [ 4 11 21]\n",
            " [42  8  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0SOQ7U-bkB4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "13d1a21c-73a1-4d47-e8f6-3d5d91eb48f7"
      },
      "source": [
        "# reshape: transformar a matriz em um vetor coluna\n",
        "# (3, 3) vira (9, 1): 3*3 = 9*1 = 9\n",
        "print(\"transformação de em um vetor coluna: \\n\", x.reshape(9, 1))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "transformação de em um vetor coluna: \n",
            " [[ 1]\n",
            " [ 3]\n",
            " [ 7]\n",
            " [ 4]\n",
            " [11]\n",
            " [21]\n",
            " [42]\n",
            " [ 8]\n",
            " [ 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6EWum1FbYiK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bd6212ea-55cd-4091-c734-009bbf00cb66"
      },
      "source": [
        "# transposição de matriz\n",
        "print(\"x transposta: \\n\", x.T)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x transposta: \n",
            " [[ 1  4 42]\n",
            " [ 3 11  8]\n",
            " [ 7 21  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG0LQUqzadSN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "01285dbe-de88-4692-a656-281fe23c58e3"
      },
      "source": [
        "# np.sum: soma em um dado eixo, axis = {0: linha, 1: coluna}\n",
        "print(\"x:\\n\", x)\n",
        "print(\"soma de todos elementos de x:\", np.sum(x))\n",
        "print(\"soma de x ao longo das linhas:\", np.sum(x, axis=0))\n",
        "print(\"soma de x ao longo das colunas:\", np.sum(x, axis=1))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 1  3  7]\n",
            " [ 4 11 21]\n",
            " [42  8  9]]\n",
            "soma de todos elementos de x: 106\n",
            "soma de x ao longo das linhas: [47 22 37]\n",
            "soma de x ao longo das colunas: [11 36 59]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_thkEYScPbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "14646929-3490-4b0c-a88c-fdcdaf1d5fd2"
      },
      "source": [
        "# np.mean: média em um dado eixo, axis = {0: linha, 1: coluna}\n",
        "print(\"x:\\n\", x)\n",
        "print(\"média de todos elementos de x:\", np.mean(x))\n",
        "print(\"média de x ao longo das linhas:\", np.mean(x, axis=0))\n",
        "print(\"média de x ao longo das colunas:\", np.mean(x, axis=1))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 1  3  7]\n",
            " [ 4 11 21]\n",
            " [42  8  9]]\n",
            "média de todos elementos de x: 11.777777777777779\n",
            "média de x ao longo das linhas: [15.66666667  7.33333333 12.33333333]\n",
            "média de x ao longo das colunas: [ 3.66666667 12.         19.66666667]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnDWq-cUcpVJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bd137945-bf31-4e7e-8153-d8ddfbe30c52"
      },
      "source": [
        "# np.where, indentificação dos índices onde uma dada condição\n",
        "# é atendida. Uso conjunto com indexação booleana\n",
        "cond = x % 2 == 0  # números pares\n",
        "print(\"condição: \\n\", cond)\n",
        "i, j = np.where(cond) # índices x[i, j] = x[cond]\n",
        "print(\"índice i (linhas):\", i)\n",
        "print(\"índice j (colunas):\", j) "
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "condição: \n",
            " [[False False False]\n",
            " [ True False False]\n",
            " [ True  True False]]\n",
            "índice i (linhas): [1 2 2]\n",
            "índice j (colunas): [0 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAW11bdbXWhL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "419df36d-acc1-457c-eff6-2d59966526ef"
      },
      "source": [
        "# indexação booleana e slicing: selecionar as linhas\n",
        "# de x que possuem algum número par\n",
        "print(\"x:\\n\", x)\n",
        "cond = x % 2 == 0  # números pares\n",
        "print(\"condição: \\n\", cond)\n",
        "\n",
        "# # se houver alguma condição True na linha, a soma será > 0\n",
        "i_row = np.where(np.sum(cond, axis=1))[0]\n",
        "print(\"índice das linhas que possuem números pares:\", i_row)\n",
        "print(\"linhas que possuem número pares: \\n\", x[i_row, :])  \n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:\n",
            " [[ 1  3  7]\n",
            " [ 4 11 21]\n",
            " [42  8  9]]\n",
            "condição: \n",
            " [[False False False]\n",
            " [ True False False]\n",
            " [ True  True False]]\n",
            "índice das linhas que possuem números pares: [1 2]\n",
            "linhas que possuem número pares: \n",
            " [[ 4 11 21]\n",
            " [42  8  9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jh3dR2mFfl9F"
      },
      "source": [
        "### **Regressão linear no numpy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWgnDPcEat9K"
      },
      "source": [
        "# visualização de dados\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNrcPD63YCKf"
      },
      "source": [
        "# dados\n",
        "x = [-1., -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
        "     0.11111111, 0.33333333, 0.55555556, 0.77777778, 1.]\n",
        "y = [-1.13956201, -0.57177999, -0.21697033, 0.5425699, 0.49406657,\n",
        "     1.14972239, 1.64228553, 2.1749824, 2.64773614, 2.95684202]"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlfXmBHDggCf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3445e26f-f9ec-4525-b27e-ddbbe867924e"
      },
      "source": [
        "# plot dos dados\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(x, y, 'o', label='dados originais')\n",
        "plt.legend()\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LXMj1FbXELaj"
      },
      "source": [
        "Regressão linear:\n",
        "\n",
        "$$X\\beta=y,\\\\\\beta=\\left[\\begin{array}{c}a\\\\b\\end{array}\\right] .$$\n",
        "\n",
        "Solução via minimização do erro quadrático:\n",
        "\n",
        "$$\\beta=(X^TX)^{-1}X^Ty,$$\n",
        "onde $(X^TX)^{-1}X^T$ é a pseudo-inversa de $X$, facilmente calculada pelo método `np.linalg.pinv(X)`.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_2BzIoTgi2w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b0473b95-0e97-470a-aed6-1a45b85ed33d"
      },
      "source": [
        "\"\"\"\n",
        "Iremos estimar uma função do tipo: y = a*x + b\n",
        "ou seja, devemos achar quais os valores de a e b\n",
        "que melhor representam os dados.\n",
        "\n",
        "Os valores reais de a e b são: (a, b): 2, 1\n",
        "\"\"\"\n",
        "# transformando para numpy e vetor coluna\n",
        "x, y = np.array(x).reshape(-1, 1), np.array(y).reshape(-1, 1)\n",
        "\n",
        "# adicionando bias: para estimar o termo b\n",
        "X = np.hstack((x, np.ones(x.shape)))\n",
        "\n",
        "# estimando a e b\n",
        "beta = np.linalg.pinv(X).dot(y)\n",
        "print(\"a estimado:\", beta[0][0])\n",
        "print(\"b estimado:\", beta[1][0])"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a estimado: 2.0541495120384856\n",
            "b estimado: 0.967989262\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5QlwxE7kADi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b6a2fcb0-0d76-4137-8b50-5ad1c1d06998"
      },
      "source": [
        "# plot dos dados\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(x, y, 'o', label='dados originais')\n",
        "plt.plot(x, X.dot(beta), label='regressão linear')\n",
        "plt.legend()\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.title(\"Regressão linear com numpy\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTGu7ra9MpHO"
      },
      "source": [],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6BocI3zPlRVh"
      },
      "source": [
        "## **Pandas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PXFWm35ZlOkO"
      },
      "source": [
        "# importando as bibliotecas\n",
        "import pandas as pd"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "25Y-6lLcoJ6v"
      },
      "source": [
        "### **Leitura de dados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJ54BBHRe1ae"
      },
      "source": [
        "pd.read_csv?"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UqjkM2Jo_E9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4329707e-f375-4272-b830-dd33791839ea"
      },
      "source": [
        "# leitura dos dados csv\n",
        "df = pd.read_csv(\"https://pycourse.s3.amazonaws.com/temperature.csv\")\n",
        "df"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "3  2020-04-01         28.0         quente\n",
              "4  2020-05-01         24.0    confortavel\n",
              "5  2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
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              "      <th>date</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
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              "    <tr>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7538dc4a-cd67-4059-a216-b6d5e8359032 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7538dc4a-cd67-4059-a216-b6d5e8359032');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejKmUZpHaoQj"
      },
      "source": [
        "# leitura de planilhas excel\n",
        "# 2 abas (worksheets)\n",
        "\n",
        "# leitura do arquivo todo\n",
        "excel_file = pd.ExcelFile(\"https://pycourse.s3.amazonaws.com/temperature.xlsx\")"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R2dmextegAHe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8caaceb6-f581-4ce5-e96d-fc4b9cfe2130"
      },
      "source": [
        "type(excel_file)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.io.excel._base.ExcelFile"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3pxoXVmjb2sJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "be828cdb-215b-4bdb-ea7f-53550a664bda"
      },
      "source": [
        "# leitura da primeira aba (Sheet1)\n",
        "# dados numéricos com separador decimal = '.'\n",
        "df2 = pd.read_excel(excel_file, sheet_name='Sheet1')\n",
        "df2"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "3 2020-04-01         28.0         quente\n",
              "4 2020-05-01         24.0    confortavel\n",
              "5 2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b66a58c5-fb58-472b-8b72-8fed7efc171b\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b66a58c5-fb58-472b-8b72-8fed7efc171b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b66a58c5-fb58-472b-8b72-8fed7efc171b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b66a58c5-fb58-472b-8b72-8fed7efc171b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwEFKFjtb4Lc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "e73320e9-0ddf-494b-fa2c-750336cca6d7"
      },
      "source": [
        "# leitura da segunda aba (Sheet2)\n",
        "# dados numéricos com separador decimal = ','\n",
        "df3 = pd.read_excel(excel_file, sheet_name='Sheet2') \n",
        "df3['temperatura'] = df3['temperatura'].str.replace(',', '.').astype('float')\n",
        "df3"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "3 2020-04-01         28.0         quente\n",
              "4 2020-05-01         24.0    confortavel\n",
              "5 2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ce9093f-f831-45a7-83ca-6d255f970853\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ce9093f-f831-45a7-83ca-6d255f970853')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ce9093f-f831-45a7-83ca-6d255f970853 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1ce9093f-f831-45a7-83ca-6d255f970853');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZpdPIEhpXc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "3adeef44-1ce3-4f43-e034-fe8a657d7d8b"
      },
      "source": [
        "# visualizando as primeiras n linhas\n",
        "n = 3\n",
        "df.head(n)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
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              "\n",
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              "      <td>quente</td>\n",
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              "    <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0476c1e1-4b7d-4b3a-86de-653801a93ab2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0476c1e1-4b7d-4b3a-86de-653801a93ab2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0476c1e1-4b7d-4b3a-86de-653801a93ab2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5OOIZW_pt4m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "ff0d7a83-c22c-43e5-e947-073c3a63713a"
      },
      "source": [
        "# visualizando as últimas n linhas\n",
        "n = 3\n",
        "df.tail(n)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date  temperatura classification\n",
              "3  2020-04-01         28.0         quente\n",
              "4  2020-05-01         24.0    confortavel\n",
              "5  2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9228b80-680b-433f-bb22-ffab1df7af3d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>3</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9228b80-680b-433f-bb22-ffab1df7af3d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a9228b80-680b-433f-bb22-ffab1df7af3d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a9228b80-680b-433f-bb22-ffab1df7af3d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5ybJEyoqLzT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "fc797b73-9d65-4757-9973-776dbca36ad6"
      },
      "source": [
        "# dtypes\n",
        "df.dtypes"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date               object\n",
              "temperatura       float64\n",
              "classification     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uMlDizuqRuj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "13849b27-e20d-4d0d-fb95-83720fb1e6f7"
      },
      "source": [
        "# estatísticas básicas\n",
        "df.describe()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-89515e70-228d-4ca2-8e4e-cf898f21316f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-89515e70-228d-4ca2-8e4e-cf898f21316f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-89515e70-228d-4ca2-8e4e-cf898f21316f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-89515e70-228d-4ca2-8e4e-cf898f21316f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2lPQBYfhqi-G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d31d2a58-e28e-43e6-b99e-dafef30370b5"
      },
      "source": [
        "# dataframe info\n",
        "df.info()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6 entries, 0 to 5\n",
            "Data columns (total 3 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            6 non-null      object \n",
            " 1   temperatura     6 non-null      float64\n",
            " 2   classification  6 non-null      object \n",
            "dtypes: float64(1), object(2)\n",
            "memory usage: 272.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DLRY7u-LrTMI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "aefc6ce1-eb06-4db8-d2a4-f0d777180fd8"
      },
      "source": [
        "# nomes das colunas\n",
        "df.columns"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'temperatura', 'classification'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gU2Pi7crNNq"
      },
      "source": [
        "### **Indexação**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYXjp9_ozlyo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f0185bbb-8bdd-476c-9615-306e38bb502b"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "3  2020-04-01         28.0         quente\n",
              "4  2020-05-01         24.0    confortavel"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4801eaac-dfdf-4f56-9312-48cad5dc61b4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4801eaac-dfdf-4f56-9312-48cad5dc61b4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4801eaac-dfdf-4f56-9312-48cad5dc61b4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4801eaac-dfdf-4f56-9312-48cad5dc61b4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFsKjAv9rPpS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6a063810-5962-43ea-ea14-fbb735a252e6"
      },
      "source": [
        "# seleção de uma coluna\n",
        "df['temperatura']"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    29.1\n",
              "1    31.2\n",
              "2    28.5\n",
              "3    28.0\n",
              "4    24.0\n",
              "5    20.0\n",
              "Name: temperatura, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyshmpFeroZn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "641c33cd-2caa-4e15-b646-8cae73f4f4b5"
      },
      "source": [
        "# tipo\n",
        "type(df['temperatura'])"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNi1uHGyrv_J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ce161742-d45e-423e-e45d-2736ae5cdf41"
      },
      "source": [
        "# seleção de múltiplas colunas\n",
        "df[['date', 'classification', 'temperatura']]"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date classification  temperatura\n",
              "0  2020-01-01         quente         29.1\n",
              "1  2020-02-01   muito quente         31.2\n",
              "2  2020-03-01         quente         28.5\n",
              "3  2020-04-01         quente         28.0\n",
              "4  2020-05-01    confortavel         24.0\n",
              "5  2020-06-01           frio         20.0"
            ],
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              "\n",
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              "      <th>classification</th>\n",
              "      <th>temperatura</th>\n",
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              "    <tr>\n",
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              "      <td>quente</td>\n",
              "      <td>28.5</td>\n",
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              "      <td>quente</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>confortavel</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>frio</td>\n",
              "      <td>20.0</td>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e982590d-00a6-48e9-b9db-91298cca7932')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e982590d-00a6-48e9-b9db-91298cca7932 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e982590d-00a6-48e9-b9db-91298cca7932');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o793Y9Hxr9tR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9ce268d2-16be-4200-8fde-7fdedb2deeb2"
      },
      "source": [
        "# tipo\n",
        "type(df[['date', 'classification']])"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Aak_N4UMuIpV"
      },
      "source": [
        "# iloc\n",
        "df.iloc?"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhQdVkbmsGsU"
      },
      "source": [
        "# indexação por índice\n",
        "# selecionado todas as linhas e a coluna 1\n",
        "# coluna 1: temperatura\n",
        "a = df.iloc[:, 1]"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0FrYgFTpsKro",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "40c3f9ba-9ff7-4b25-c91a-6d332360c45e"
      },
      "source": [
        "# indexação por nome\n",
        "# selecionado todas as linhas e a coluna 1\n",
        "df.loc[:, 'temperatura']"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    29.1\n",
              "1    31.2\n",
              "2    28.5\n",
              "3    28.0\n",
              "4    24.0\n",
              "5    20.0\n",
              "Name: temperatura, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pV9onCPesS7h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5359400b-2bce-49db-e987-7975455345d8"
      },
      "source": [
        "# indexação por índice de múltiplas colunas\n",
        "df.iloc[:, 1:]"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   temperatura classification\n",
              "0         29.1         quente\n",
              "1         31.2   muito quente\n",
              "2         28.5         quente\n",
              "3         28.0         quente\n",
              "4         24.0    confortavel\n",
              "5         20.0           frio"
            ],
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-80342e84-9e87-4e9d-ab69-2610cb38f453 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-80342e84-9e87-4e9d-ab69-2610cb38f453');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMC0RktesdLO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1678088a-bdb8-48ae-f363-d7abc8f267d1"
      },
      "source": [
        "# indexação por nome de múltiplas colunas\n",
        "df.loc[:, ['temperatura', 'classification']]"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   temperatura classification\n",
              "0         29.1         quente\n",
              "1         31.2   muito quente\n",
              "2         28.5         quente\n",
              "3         28.0         quente\n",
              "4         24.0    confortavel\n",
              "5         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d75b50d-7fa1-4ae9-8630-355ab4da7c7f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d75b50d-7fa1-4ae9-8630-355ab4da7c7f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5d75b50d-7fa1-4ae9-8630-355ab4da7c7f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5d75b50d-7fa1-4ae9-8630-355ab4da7c7f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mw0tAQCFsm17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c9ecf444-908c-4954-903d-0eaa9bb951bb"
      },
      "source": [
        "# indexação por nome de múltiplas colunas\n",
        "df.loc[:, 'temperatura':]"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   temperatura classification\n",
              "0         29.1         quente\n",
              "1         31.2   muito quente\n",
              "2         28.5         quente\n",
              "3         28.0         quente\n",
              "4         24.0    confortavel\n",
              "5         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a45493c7-0612-41e0-bbb3-28263c9e7358\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29.1</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a45493c7-0612-41e0-bbb3-28263c9e7358')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a45493c7-0612-41e0-bbb3-28263c9e7358 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a45493c7-0612-41e0-bbb3-28263c9e7358');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5RHAvSdsvws"
      },
      "source": [
        "### **Indexação booleana**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2cWyNMvu6ydj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6cfd6c3f-51c7-43b3-b787-db7e7fd34d33"
      },
      "source": [
        "df"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "3  2020-04-01         28.0         quente\n",
              "4  2020-05-01         24.0    confortavel\n",
              "5  2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-580682a6-4a89-4602-aee2-0051e40c7639\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-04-01</td>\n",
              "      <td>28.0</td>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2020-06-01</td>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-580682a6-4a89-4602-aee2-0051e40c7639')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-580682a6-4a89-4602-aee2-0051e40c7639 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-580682a6-4a89-4602-aee2-0051e40c7639');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQj5l5_Ht5yt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1010be22-6f8d-45c8-8cc0-9d9c384265c9"
      },
      "source": [
        "# dtype\n",
        "df.dtypes"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date               object\n",
              "temperatura       float64\n",
              "classification     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5u4E73Ut0nq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "acfccd2c-35f7-4fbc-f614-6c179667a919"
      },
      "source": [
        "# transformando o tipo da coluna date para datetime\n",
        "df['date'] = pd.to_datetime(df['date'])\n",
        "df.dtypes"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date              datetime64[ns]\n",
              "temperatura              float64\n",
              "classification            object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cy-Yg0WJt_ir"
      },
      "source": [
        "# setando o índice\n",
        "df = df.set_index('date')"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAS-zBkvuCo0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d26b23aa-96f6-411c-91fd-27cc635c539a"
      },
      "source": [
        "# visualizando o índice \n",
        "df"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4c547f7-f584-41af-bec8-188c72b97043\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4c547f7-f584-41af-bec8-188c72b97043')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4c547f7-f584-41af-bec8-188c72b97043 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c4c547f7-f584-41af-bec8-188c72b97043');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dMkmDHk4ujTz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7085b8ee-513d-44ed-fd09-8678ab01687a"
      },
      "source": [
        "# 5 primeiras linas\n",
        "df.head()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0d2d1bf1-31da-4d23-92be-faef79fcae97\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d2d1bf1-31da-4d23-92be-faef79fcae97')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d2d1bf1-31da-4d23-92be-faef79fcae97 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d2d1bf1-31da-4d23-92be-faef79fcae97');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZqnYBJEuyvi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ad0cbecb-dd0e-4c69-8db9-484effc8bcca"
      },
      "source": [
        "# indexação booleana\n",
        "# seleção de exemplos acima de 25 graus\n",
        "cond = df['temperatura'] >= 25\n",
        "df[cond]"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bbdf4556-f76e-4b9b-844f-ede1ab3771f8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bbdf4556-f76e-4b9b-844f-ede1ab3771f8')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          "metadata": {},
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          "height": 0
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        "outputId": "37861d8f-31a3-4b36-8b4b-25ce57cc9a83"
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        "# indexação booleana considerando datetime\n",
        "# seleção de entradas até Março de 2020\n",
        "cond = df.index <= '2020-03-01'\n",
        "df[cond]"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
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              "      <td>29.1</td>\n",
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              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
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              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
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              "\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f62d2c9-a012-4855-b13f-83652c7fb965');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "outputId": "8e1b3e8a-f89c-4e45-c1d4-69b7c2a88b3d"
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      "source": [
        "# indexação booleana considerando datetime\n",
        "# seleção de entradas até Março de 2020 e \n",
        "# slice na coluna classification\n",
        "df.loc[df.index <= '2020-03-01', ['classification']]"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "date                     \n",
              "2020-01-01         quente\n",
              "2020-02-01   muito quente\n",
              "2020-03-01         quente"
            ],
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              "      <td>quente</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-807ad30b-9699-4c75-bc49-65e1647c1232')\"\n",
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              "\n",
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              "          document.querySelector('#df-807ad30b-9699-4c75-bc49-65e1647c1232 button.colab-df-convert');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
          "execution_count": 85
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    {
      "cell_type": "code",
      "metadata": {
        "id": "PB3ZZ-qLu84Z",
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          "height": 0
        },
        "outputId": "9e53dfd8-5b1c-4aa5-b897-6f8f1730f017"
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      "source": [
        "# indexação booleana considerando datetime\n",
        "# seleção de entradas até Março de 2020 e \n",
        "# slice na coluna classification\n",
        "df.iloc[df.index <= '2020-03-01', [-1]]"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "date                     \n",
              "2020-01-01         quente\n",
              "2020-02-01   muito quente\n",
              "2020-03-01         quente"
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3bcf844e-e373-4df7-a53b-892fe24df90f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3bcf844e-e373-4df7-a53b-892fe24df90f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0sJc-R3uvEwU"
      },
      "source": [
        "### **Ordenação**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7FqE0WNvKGd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f3493a92-09a5-4710-eec3-406c331ed44b"
      },
      "source": [
        "# df.sort_values\n",
        "df"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d0af9a43-c84a-449b-bcda-971a30b9f2c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d0af9a43-c84a-449b-bcda-971a30b9f2c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d0af9a43-c84a-449b-bcda-971a30b9f2c7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d0af9a43-c84a-449b-bcda-971a30b9f2c7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUdLqE_PvigB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "9bea2adc-6c8a-47fe-c71e-414271e8c191"
      },
      "source": [
        "# ordenação crescente por uma coluna\n",
        "df.sort_values(by='temperatura')"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-551f9013-f87d-4ba5-96bc-75800f50c5c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-551f9013-f87d-4ba5-96bc-75800f50c5c0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-551f9013-f87d-4ba5-96bc-75800f50c5c0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-551f9013-f87d-4ba5-96bc-75800f50c5c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6HOyiWOvQ_VB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a92510fa-0631-4481-90dd-5ed9c8bc8fac"
      },
      "source": [
        "# ordenação crescente por uma coluna\n",
        "df.sort_values(by='classification')"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-01-01         29.1         quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-04-01         28.0         quente"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c9f8b97e-2c16-4827-a158-84f3cb3aa199\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</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",
              "    <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c9f8b97e-2c16-4827-a158-84f3cb3aa199')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9f8b97e-2c16-4827-a158-84f3cb3aa199 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c9f8b97e-2c16-4827-a158-84f3cb3aa199');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xxy50lOsQ3Fq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "200a3895-3b4b-4fe6-b023-a9f1b36f4c95"
      },
      "source": [
        "# ordenação crescente por mais de uma coluna\n",
        "df.sort_values(by=['classification', 'temperatura'])"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-04-01         28.0         quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-01-01         29.1         quente"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temperatura</th>\n",
              "      <th>classification</th>\n",
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              "    <tr>\n",
              "      <th>date</th>\n",
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              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</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>2020-04-01</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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0d0f4745-3b37-4cc8-afbb-baa687165292')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0d0f4745-3b37-4cc8-afbb-baa687165292 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0d0f4745-3b37-4cc8-afbb-baa687165292');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7gLEKNEvpfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "bc903e98-4a53-4d07-8e33-af6e37548543"
      },
      "source": [
        "# ordenação decrescente por uma coluna\n",
        "df.sort_values(by=['classification', 'temperatura'], ascending=False)"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            temperatura classification\n",
              "date                                  \n",
              "2020-01-01         29.1         quente\n",
              "2020-03-01         28.5         quente\n",
              "2020-04-01         28.0         quente\n",
              "2020-02-01         31.2   muito quente\n",
              "2020-06-01         20.0           frio\n",
              "2020-05-01         24.0    confortavel"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0da2a7b7-5741-43f1-8410-698a189c4ee0\">\n",
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              "    }\n",
              "\n",
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              "<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>2020-03-01</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",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\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",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0da2a7b7-5741-43f1-8410-698a189c4ee0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0da2a7b7-5741-43f1-8410-698a189c4ee0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0da2a7b7-5741-43f1-8410-698a189c4ee0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xQz2ONYJvyv-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "777fa665-1923-4240-8d44-ce362e6c9b5a"
      },
      "source": [
        "# ordenação crescente pelo índice\n",
        "df.sort_index()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
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              "      <td>quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>31.2</td>\n",
              "      <td>muito quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>28.5</td>\n",
              "      <td>quente</td>\n",
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              "      <th>2020-04-01</th>\n",
              "      <td>28.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e56b97c7-5891-4ead-be6c-98415939caed')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e56b97c7-5891-4ead-be6c-98415939caed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e56b97c7-5891-4ead-be6c-98415939caed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXSBvENCv3PW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "449717b1-5d54-4e1b-93a1-8d8d159e88dd"
      },
      "source": [
        "# ordenação decrescente pelo índice\n",
        "df.sort_index(ascending=False)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d449e68d-df22-4ed9-806a-5115756bd6dc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d449e68d-df22-4ed9-806a-5115756bd6dc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d449e68d-df22-4ed9-806a-5115756bd6dc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d449e68d-df22-4ed9-806a-5115756bd6dc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjlWCwS_ycmt"
      },
      "source": [
        "### **Visualização**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kc4SHEckVTzo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a658be1a-e22e-4d15-e62b-53d664283e9b"
      },
      "source": [
        "# plot de linhas\n",
        "df.plot();"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7f134FdaX1ws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "420f8054-53f9-4529-a5c3-3a6f05821830"
      },
      "source": [
        "# plot de linhas: tamanho\n",
        "df.plot(figsize=(10, 5));"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KW7H1RMCYCgC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "13e1dd26-ec84-4293-a3a9-9db4d2836d23"
      },
      "source": [
        "# plot de linhas: grid\n",
        "df.plot(figsize=(10, 5), grid=True);"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zUMF1OvygdZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c7093b86-69e0-4fc1-bcf3-3fe8b580bfe3"
      },
      "source": [
        "# plot de linhas: style\n",
        "df.plot(style='-o', figsize=(10, 5), grid=True);\n",
        "# df.plot(style='--', figsize=(10, 5), grid=True);\n",
        "# df.plot(style='-.', figsize=(10, 5), grid=True);"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-vMPaxaLYSkK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a6f5e26f-1ae0-4ca4-db62-5dd08ba5d049"
      },
      "source": [
        "# plot de linhas: linewidth\n",
        "df.plot(style='-o', linewidth=2, figsize=(10, 5), grid=True);"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kONqALFXY9oZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "6cf9b066-e6f5-4ef8-e637-19822323286c"
      },
      "source": [
        "# plot de linhas: color\n",
        "df.plot(style='-o', linewidth=2.5, color='red', figsize=(10, 5), grid=True);"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIgIZnZ2Ye_x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a7694af6-3c73-42d6-f21a-f681e372a45d"
      },
      "source": [
        "# plot de linhas: color\n",
        "df.plot(style='-o', linewidth=2.5, color='#822fb5', figsize=(10, 5), grid=True);"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zk6vTv8HdRmz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "990934a7-ea0b-4412-fe5f-51981af3aee6"
      },
      "source": [
        "df"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-145b7072-16cd-4406-9347-1398b49edc35\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-145b7072-16cd-4406-9347-1398b49edc35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-145b7072-16cd-4406-9347-1398b49edc35 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-145b7072-16cd-4406-9347-1398b49edc35');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGohI5T50tBf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7af92415-6cc2-4231-c5bb-bdd12111c336"
      },
      "source": [
        "# plot de barras\n",
        "df['classification'].value_counts().plot.bar(figsize=(10, 5),\n",
        "                                             rot=0);"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gsz3n6CTy1W-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "3a51cd38-7c40-4a43-f951-13f473fbf543"
      },
      "source": [
        "# plot de barras\n",
        "df.plot(kind='bar', figsize=(10, 5), rot=30);"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1T12zZNW03AS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "080bdd18-023c-4c7c-d057-234d202ae0d9"
      },
      "source": [
        "# pie plot\n",
        "df['classification'].value_counts().plot.pie(autopct='%1.1f%%',\n",
        "                                             shadow=True,\n",
        "                                             figsize=(10, 7));"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZUFrYst7YWb"
      },
      "source": [
        "### **Outras operações úteis no pandas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w05p6hO17wTE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "9a4d1a8a-beb1-4f09-edd9-cc29cbd52f9b"
      },
      "source": [
        "# dataframe\n",
        "df.head(6)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-34e0b6dc-71bd-4194-b4b6-30c9bf6c3f9d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>24.0</td>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>20.0</td>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-34e0b6dc-71bd-4194-b4b6-30c9bf6c3f9d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-34e0b6dc-71bd-4194-b4b6-30c9bf6c3f9d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-34e0b6dc-71bd-4194-b4b6-30c9bf6c3f9d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eSULlalQ79M7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "87fb23fd-f23a-4230-8886-ab168616ed47"
      },
      "source": [
        "# groupby: agrupamento por valores únicos de uma ou mais colunas\n",
        "df.groupby(by='classification')"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fedfb68b4f0>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNq-dHV57d3m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "17cb8d98-76fb-4b8e-f041-29a7805d822f"
      },
      "source": [
        "# groupby: agrupamento por valores únicos de uma ou mais colunas\n",
        "df.groupby(by='classification').mean()"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                temperatura\n",
              "classification             \n",
              "confortavel       24.000000\n",
              "frio              20.000000\n",
              "muito quente      31.200000\n",
              "quente            28.533333"
            ],
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              "\n",
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              "  <tbody>\n",
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              "      <td>24.000000</td>\n",
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              "    <tr>\n",
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              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>muito quente</th>\n",
              "      <td>31.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quente</th>\n",
              "      <td>28.533333</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52caf28f-6ed2-42c6-8bb5-cbf095dd3a1e')\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52caf28f-6ed2-42c6-8bb5-cbf095dd3a1e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52caf28f-6ed2-42c6-8bb5-cbf095dd3a1e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLwXeO2F73id",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "5d4f4145-d6b9-452d-a962-ba56ef7a9093"
      },
      "source": [
        "# groupby: agrupamento por valores únicos de uma ou mais colunas\n",
        "df.groupby(by='classification').sum()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                temperatura\n",
              "classification             \n",
              "confortavel            24.0\n",
              "frio                   20.0\n",
              "muito quente           31.2\n",
              "quente                 85.6"
            ],
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              "\n",
              "  <div id=\"df-418155ce-3449-44db-85e5-08023a35b1b9\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temperatura</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>classification</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>confortavel</th>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>frio</th>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>muito quente</th>\n",
              "      <td>31.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>quente</th>\n",
              "      <td>85.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-418155ce-3449-44db-85e5-08023a35b1b9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-418155ce-3449-44db-85e5-08023a35b1b9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-418155ce-3449-44db-85e5-08023a35b1b9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkqq85JF8pb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "920e9d6d-7640-4e12-d981-785025aa4ebf"
      },
      "source": [
        "# drop: remoção de uma coluna\n",
        "df.drop('temperatura', axis=1)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "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"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>2020-01-01</th>\n",
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              "    <tr>\n",
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              "      <th>2020-04-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
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      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "df"
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      "execution_count": 110,
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        {
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          "data": {
            "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\n",
              "2020-05-01         24.0    confortavel\n",
              "2020-06-01         20.0           frio"
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olsUSu1w9FKQ"
      },
      "source": [
        "# cópia de um dataframe: evita compartilhamento de memória\n",
        "# sem copy(), operações inplace em df2 também alteram df\n",
        "# df2 = df.copy() \n",
        "df2 = df"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBsx7tjb80WA"
      },
      "source": [
        "# argumento inplace\n",
        "# inplace=True aplica a transformação no próprio objeto\n",
        "df3 = df2.drop(\"temperatura\", axis=1)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zmYjdq0B9WFi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "809bf6ca-e2be-433b-ad4c-033132a98898"
      },
      "source": [
        "# sem inplace, df2 continua o mesmo\n",
        "df3.head()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "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"
            ],
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              "  </thead>\n",
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              "      <td>quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>muito quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>quente</td>\n",
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              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a2c76679-deca-4019-a7cf-298ae0f84f50');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XHAEEuei9jsY"
      },
      "source": [
        "# argumento inplace\n",
        "# inplace=True aplica a transformação no próprio objeto\n",
        "df2.drop(\"temperatura\", axis=1, inplace=True)"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2_TboJ89oBA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "30b66879-b6d2-4192-f28c-6b61c4cba647"
      },
      "source": [
        "# com inplace, df2 é alterado\n",
        "df2.head()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "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"
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              "    }\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>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-28267564-7e11-4ae2-bc23-a7d3735f070c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-28267564-7e11-4ae2-bc23-a7d3735f070c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-28267564-7e11-4ae2-bc23-a7d3735f070c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bG2efm6khpMS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "730577e5-6019-4c3c-c1f4-174d5631a772"
      },
      "source": [
        "# df\n",
        "df.head()"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02b8570f-1919-48c7-a52b-e1745f73d4bb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02b8570f-1919-48c7-a52b-e1745f73d4bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02b8570f-1919-48c7-a52b-e1745f73d4bb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-02b8570f-1919-48c7-a52b-e1745f73d4bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8PmXTv0nnG9"
      },
      "source": [
        "## **Scikit-learn**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5O28yN93phC"
      },
      "source": [
        "### **Classificação no scikit-learn**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TdYlunqkpXMP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "29afed66-cbdc-414e-e157-1e66f338003c"
      },
      "source": [
        "df"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           classification\n",
              "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"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f8a8bb3a-04b0-46a1-b705-aa717ccf9058\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>classification</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>muito quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>quente</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>confortavel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>frio</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8a8bb3a-04b0-46a1-b705-aa717ccf9058')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f8a8bb3a-04b0-46a1-b705-aa717ccf9058 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f8a8bb3a-04b0-46a1-b705-aa717ccf9058');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "THBZz9mJw6u8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "b6ca798d-1049-47d3-d5b1-bb27d0c7bb93"
      },
      "source": [
        "# extração 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": 118,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-118-4473877849e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# extração de x e y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'temperatura'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'classification'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"y:\\n\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3462\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3464\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3465\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3466\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1314\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis)\u001b[0m\n\u001b[1;32m   1372\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1374\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"None of [Index(['temperatura'], dtype='object')] are in the [columns]\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R_lQuLB33tyK"
      },
      "source": [
        "# pré-processamento\n",
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa8-1KSfpxaI"
      },
      "source": [
        "LabelEncoder?"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wyaba5xK3vye"
      },
      "source": [
        "# conversão de y para valores numéricos\n",
        "le = LabelEncoder()  # label enconder\n",
        "y = le.fit_transform(y.ravel())\n",
        "print(\"y:\\n\", y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko6_zxRZ3xYW"
      },
      "source": [
        "# modelo\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "06xNwo0d4CGy"
      },
      "source": [
        "# classificador\n",
        "clf = LogisticRegression()\n",
        "clf.fit(x, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixg2gews4Fn5"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Frtax63f4Kkl"
      },
      "source": [
        "print(y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "icEpsEJi4HaX"
      },
      "source": [
        "# conversão de y_pred para os valores originais\n",
        "y_pred = le.inverse_transform(y_pred)\n",
        "# print(y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzxLJVPR4JxM"
      },
      "source": [
        "# output\n",
        "output = {'new_temp': x_test.ravel(),\n",
        "         'new_class': y_pred.ravel()}\n",
        "output = pd.DataFrame(output)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLRJrXql4_47"
      },
      "source": [
        "output.tail()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xG8FuGy24MgQ"
      },
      "source": [
        "# estatisticas\n",
        "output.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZqxmxUom4N6w"
      },
      "source": [
        "# estatisticas\n",
        "output.describe()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgHAbbTP4PJs"
      },
      "source": [
        "# contagem de valores gerados\n",
        "output['new_class'].value_counts().plot.bar(figsize=(10, 5),\n",
        "                                            rot=0,\n",
        "                                            title=\"# de novos valores gerados\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWK6GzV04RpY"
      },
      "source": [
        "# distribuição do output 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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KlNss4v9qlw-"
      },
      "source": [
        "# sistema automático\n",
        "def classify_temp():\n",
        "  \"\"\"Classifica o input do usuário.\"\"\"\n",
        "\n",
        "  ask = True\n",
        "  while ask:\n",
        "    # input de temperatura\n",
        "    temp = input(\"Insira a temperatura (graus Celsius): \")\n",
        "\n",
        "    # transformar para numpy array\n",
        "    temp = np.array(float(temp)).reshape(-1, 1)\n",
        "    \n",
        "    # realiza classificação\n",
        "    class_temp = clf.predict(temp)\n",
        "\n",
        "    # transformação inversa para retornar a string original\n",
        "    class_temp =  le.inverse_transform(class_temp)\n",
        "\n",
        "    # classificação\n",
        "    print(f\"A classificação da temperatura {temp.ravel()[0]} é:\", class_temp[0])\n",
        "\n",
        "    # perguntar\n",
        "    ask = input(\"Nova classificação (y/n): \") == 'y'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YqHRWOfqrFyH"
      },
      "source": [
        "# rodando programa \n",
        "classify_temp()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXYRe1uZ4Y3M"
      },
      "source": [
        "### **Regressão linear no scikit-learn - I**\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVuf6gNN4THE"
      },
      "source": [
        "# dados do segundo capítulo\n",
        "x = [-1., -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
        "     0.11111111, 0.33333333, 0.55555556, 0.77777778, 1.]\n",
        "y = [-1.13956201, -0.57177999, -0.21697033, 0.5425699, 0.49406657,\n",
        "     1.14972239, 1.64228553, 2.1749824, 2.64773614, 2.95684202]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ExzD2e7e5qCl"
      },
      "source": [
        "# plot dos dados\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(x, y, 'o', label='dados originais')\n",
        "plt.legend()\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANMu9pMX5SEN"
      },
      "source": [
        "# transformando em numpy array\n",
        "x, y = np.array(x).reshape(-1, 1), np.array(y).reshape(-1, 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tVwybFtM44gR"
      },
      "source": [
        "# modelo\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyZzrOhd5MiM"
      },
      "source": [
        "# treinando o modelo: y = a*x + b, valores reais (a, b) = (2, 1)\n",
        "reg = LinearRegression()\n",
        "reg.fit(x, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lCukYdH_53eF"
      },
      "source": [
        "# coeficientes a, b estimados:\n",
        "# valores estimados usando o numpy diretamente\n",
        "# a estimado no numpy: 2.05414951\n",
        "# b estimado no numpy: 0.96798926\n",
        "print(\"a estimado:\", reg.coef_.ravel()[0])\n",
        "print(\"b estimado:\", reg.intercept_[0])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3PoSALG5vIV"
      },
      "source": [
        "# predição do modelo\n",
        "y_pred = reg.predict(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hgkubn3D6ocw"
      },
      "source": [
        "# score do modelo\n",
        "score = reg.score(x, y)\n",
        "print(\"score:\", score)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jwca7UQA5QYO"
      },
      "source": [
        "# plot dos dados\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(x, y, 'o', label='dados originais')\n",
        "plt.plot(x, y_pred, label='regressão linear (R2: {:.3f})'.format(score))\n",
        "plt.legend()\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.title(\"Regressão linear no scikit-learn\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECTpxDdC-6Bs"
      },
      "source": [
        "### **Regressão linear no scikit-learn - II**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_CqpevpU50t7"
      },
      "source": [
        "# plot dos dados\n",
        "plt.figure(figsize=(10, 5))\n",
        "plt.plot(x, y, 'o', label='dados originais')\n",
        "plt.plot(x, y_pred, label='regressão linear (R2: {:.4f})'.format(score))\n",
        "plt.hlines(y=y.mean(), xmin=x.min(), xmax=x.max(), linestyle='dashed',\n",
        "           label='Modelo de referência do $R^2$')\n",
        "plt.legend()\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.title(\"Regressão linear no scikit-learn\")\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOxmlPL7Lj7A"
      },
      "source": [
        "O erro quadrático médio de um modelo de regressão é dado por:\n",
        "\n",
        "$$MSE_{reg} = \\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\hat{y}_i)^2.$$\n",
        "\n",
        "O coeficiente de determinação $R^2$ representa o quão melhor um modelo é em relação a um modelo de referência que prevê sempre a média dos pontos, ou seja, o MSE do modelo de referência é dado por:\n",
        "\n",
        "$$MSE_{ref} = \\frac{1}{n}\\sum_{i=1}^{n} (y_i - \\bar{y})^2,$$\n",
        "\n",
        "onde $\\bar{y}$ representa a média do vetor $y$.\n",
        "\n",
        "Sendo assim:\n",
        "\n",
        "$$R^2 = 1 - \\frac{MSE_{reg}}{MSE_{ref}}$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Wg8zsdmLitv"
      },
      "source": [
        "# função para cálculo do MSE\n",
        "def mse(y_true, y_pred, is_ref = False):\n",
        "\n",
        "  # mse modelo\n",
        "  if is_ref:\n",
        "    mse = ((y_true - y_true.mean())**2).mean()\n",
        "  else:\n",
        "    mse = ((y_true - y_pred)**2).mean()\n",
        "\n",
        "  return mse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oLm3PiVe_CND"
      },
      "source": [
        "# função para cálculo do coeficiente de determinação R2\n",
        "def r2(mse_reg, mse_ref):\n",
        "  return 1 - mse_reg/mse_ref"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLE-CsnrOXgs"
      },
      "source": [
        "# visualizando y e y_pred\n",
        "print(\"y_true:\", y.ravel())\n",
        "print(\"y_pred:\", y_pred.ravel())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5kSqL55IOH7J"
      },
      "source": [
        "# calculando o mse dos modelos\n",
        "mse_reg = mse(y_true=y, y_pred=y_pred)\n",
        "print(\"MSE do modelo de regressão:\", mse_reg)\n",
        "mse_ref = mse(y_true=y, y_pred=y_pred, is_ref=True)\n",
        "print(\"MSE do modelo de referência:\", mse_ref)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rqB6OLfFOx_M"
      },
      "source": [
        "# calculando o R2 score\n",
        "r2_score = r2(mse_reg=mse_reg, mse_ref=mse_ref)\n",
        "print(\"Coeficiente R2 do modelo implementado (calculado):\", r2_score)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_oX5Ef7IPM6C"
      },
      "source": [
        "# score retornado pelo scikit-learn\n",
        "r2_score_skl = reg.score(x, y)\n",
        "print(\"Coeficiente R2 do modelo implementado (scikit-learn):\", r2_score_skl)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jKKAUuKsPvTb"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}