Week 1_예습과제_정지은.ipynb

{
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      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "ndnAGa41dyv1",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nxuJT0ftdyv5",
        "outputId": "a19ca3f5-9e81-46e8-a186-b166c222567b",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1 type: <class 'numpy.ndarray'>\n",
            "array1 array 형태: (3,)\n",
            "array2 type: <class 'numpy.ndarray'>\n",
            "array2 array 형태: (2, 3)\n",
            "array3 type: <class 'numpy.ndarray'>\n",
            "array3 array 형태: (1, 3)\n"
          ]
        }
      ],
      "source": [
        "array1 = np.array([1, 2, 3])\n",
        "print('array1 type:', type(array1))\n",
        "print('array1 array 형태:', array1.shape)\n",
        "\n",
        "array2 = np.array([[1,2,3], [2,3,4]])\n",
        "print('array2 type:', type(array2))\n",
        "print('array2 array 형태:', array2.shape)\n",
        "\n",
        "array3 = np.array([[1,2,3]])\n",
        "print('array3 type:', type(array3))\n",
        "print('array3 array 형태:', array3.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "I1fVuJ9Jdyv8",
        "outputId": "e4f25da2-7e50-422d-f59f-baa18654e4e5",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1: 1차원, array2: 2차원, array3:  2차원\n"
          ]
        }
      ],
      "source": [
        "print('array1: {:0}차원, array2: {:1}차원, array3: {:2}차원'.format(array1.ndim, array2.ndim, array3.ndim))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "aNmdCD47dyv9",
        "outputId": "15c94375-620d-4bc0-d899-7807b217b052",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'list'>\n",
            "<class 'numpy.ndarray'>\n",
            "[1 2 3] int32\n"
          ]
        }
      ],
      "source": [
        "list1 = [1,2,3]\n",
        "print(type(list1))\n",
        "array1 = np.array(list1)\n",
        "print(type(array1))\n",
        "print(array1, array1.dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "TvXZO8zzdyv-",
        "outputId": "970c003c-a103-4cbd-bdef-0b13ae28340a",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['1' '2' 'test'] <U11\n",
            "[1. 2. 3.] float64\n"
          ]
        }
      ],
      "source": [
        "list2 = [1,2,'test']\n",
        "array2 = np.array(list2)\n",
        "print(array2, array2.dtype)\n",
        "\n",
        "list3 = [1,2,3.0]\n",
        "array3 = np.array(list3)\n",
        "print(array3, array3.dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "KtZVIA3Zdyv_",
        "outputId": "42a9fc65-97e5-476e-8ffb-4f23fc01f4ae",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1. 2. 3.] float64\n",
            "[1 2 3] int32\n",
            "[1 2 3] int32\n"
          ]
        }
      ],
      "source": [
        "array_int = np.array([1,2,3])\n",
        "array_float = array_int.astype('float64')\n",
        "print(array_float, array_float.dtype)\n",
        "\n",
        "array_int1 = array_float.astype('int32')\n",
        "print(array_int1, array_int1.dtype)\n",
        "\n",
        "array_float1 = np.array([1.1,2.1,3.1])\n",
        "array_int2 = array_float1.astype('int32')\n",
        "print(array_int2, array_int2.dtype)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "smy8LxzvdywA",
        "outputId": "792ebe78-89ef-4fe6-dea0-c9e659048cbf",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "int32 (10,)\n"
          ]
        }
      ],
      "source": [
        "sequence_array = np.arange(10)\n",
        "print(sequence_array)\n",
        "print(sequence_array.dtype, sequence_array.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "RdWLYy6SdywA",
        "outputId": "e0243f7c-c5b2-4d40-d13f-d9618afe8f6b",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0 0]\n",
            " [0 0]\n",
            " [0 0]]\n",
            "int32 (3, 2)\n",
            "[[1. 1.]\n",
            " [1. 1.]\n",
            " [1. 1.]]\n",
            "float64 (3, 2)\n"
          ]
        }
      ],
      "source": [
        "zero_array = np.zeros((3,2), dtype='int32')\n",
        "print(zero_array)\n",
        "print(zero_array.dtype, zero_array.shape)\n",
        "\n",
        "one_array = np.ones((3,2))\n",
        "print(one_array)\n",
        "print(one_array.dtype, one_array.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "H7BJkSN4dywB",
        "outputId": "62b0ae7c-bb81-4797-b667-bc7a9bb1a09d",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1:\n",
            " [0 1 2 3 4 5 6 7 8 9]\n",
            "array2:\n",
            " [[0 1 2 3 4]\n",
            " [5 6 7 8 9]]\n",
            "array3:\n",
            " [[0 1]\n",
            " [2 3]\n",
            " [4 5]\n",
            " [6 7]\n",
            " [8 9]]\n"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(10)\n",
        "print('array1:\\n', array1)\n",
        "\n",
        "array2 = array1.reshape(2,5)\n",
        "print('array2:\\n', array2)\n",
        "\n",
        "array3 = array1.reshape(5,2)\n",
        "print('array3:\\n', array3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "XNPQTQ3KdywB",
        "outputId": "d342bb16-d8af-47e5-e779-1d1e1defbc33",
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'ValueError'>",
          "evalue": "cannot reshape array of size 10 into shape (4,3)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43marray1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 10 into shape (4,3)"
          ]
        }
      ],
      "source": [
        "array1.reshape(4,3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "k6ZUM1CNdywC",
        "outputId": "56b1abbd-0edd-4f10-fd1d-8d4cd731b513",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "array2 shape : (2, 5)\n",
            "array3 shape : (5, 2)\n"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(10)\n",
        "print(array1)\n",
        "array2 = array1.reshape(-1,5)\n",
        "print('array2 shape :', array2.shape)\n",
        "array3 = array1.reshape(5,-1)\n",
        "print('array3 shape :', array3.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "y3aUHGh_dywD",
        "outputId": "41f542f5-093f-44e9-c72c-ef0c14ed9165",
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'ValueError'>",
          "evalue": "cannot reshape array of size 10 into shape (4)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[16], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m array1 \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m10\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m array4 \u001b[38;5;241m=\u001b[39m \u001b[43marray1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreshape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 10 into shape (4)"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(10)\n",
        "array4 = array1.reshape(-1,4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "hgNDTu2adywD",
        "outputId": "e748802e-6025-40ae-ff59-7b2a0162ccf5",
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array3d:\n",
            " [[[0, 1], [2, 3]], [[4, 5], [6, 7]]]\n",
            "array5:\n",
            " [[0], [1], [2], [3], [4], [5], [6], [7]]\n",
            "array5 shape: (8, 1)\n",
            "array6:\n",
            " [[0], [1], [2], [3], [4], [5], [6], [7]]\n",
            "array6 shape: (8, 1)\n"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(8)\n",
        "array3d = array1.reshape((2,2,2))\n",
        "print('array3d:\\n', array3d.tolist())\n",
        "\n",
        "#3차원 ndarray를 2차원 ndarray로 변환\n",
        "array5 = array3d.reshape(-1,1)\n",
        "print('array5:\\n', array5.tolist())\n",
        "print('array5 shape:', array5.shape)\n",
        "\n",
        "#1차원 ndarray를 2차원 ndarray로 변환\n",
        "array6 = array1.reshape(-1,1)\n",
        "print('array6:\\n', array6.tolist())\n",
        "print('array6 shape:', array6.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1: [1 2 3 4 5 6 7 8 9]\n",
            "value: 3\n",
            "<class 'numpy.int32'>\n"
          ]
        }
      ],
      "source": [
        "#1부터 9까지의 1차원 ndarray 생성\n",
        "array1 = np.arange(start=1, stop=10)\n",
        "print('array1:', array1)\n",
        "\n",
        "#index는 0부터 시작하므로 array1[2]는 3번째 index 위치의 데이터값을 의미\n",
        "value = array1[2]\n",
        "print('value:', value)\n",
        "print(type(value))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "맨 뒤의 값: 9 맨 뒤에서 두 번째 값: 8\n"
          ]
        }
      ],
      "source": [
        "print('맨 뒤의 값:', array1[-1], '맨 뒤에서 두 번째 값:', array1[-2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1: [9 2 3 4 5 6 7 8 0]\n"
          ]
        }
      ],
      "source": [
        "array1[0] = 9\n",
        "array1[8] = 0\n",
        "print('array1:', array1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n",
            "(row=0, col=0) index 가리키는 값: 1\n",
            "(row=0, col=1) index 가리키는 값: 2\n",
            "(row=1, col=0) index 가리키는 값: 4\n",
            "(row=2, col=2) index 가리키는 값: 9\n"
          ]
        }
      ],
      "source": [
        "array1d = np.arange(start=1, stop=10)\n",
        "array2d = array1d.reshape(3,3)\n",
        "print(array2d)\n",
        "\n",
        "print('(row=0, col=0) index 가리키는 값:', array2d[0,0])\n",
        "print('(row=0, col=1) index 가리키는 값:', array2d[0,1])\n",
        "print('(row=1, col=0) index 가리키는 값:', array2d[1,0])\n",
        "print('(row=2, col=2) index 가리키는 값:', array2d[2,2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3]\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(start=1, stop=10)\n",
        "array3 = array1[0:3]\n",
        "print(array3)\n",
        "print(type(array3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3]\n",
            "[4 5 6 7 8 9]\n",
            "[1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "array1 = np.arange(start=1, stop=10)\n",
        "array4 = array1[:3]\n",
        "print(array4)\n",
        "\n",
        "array5 = array1[3:]\n",
        "print(array5)\n",
        "\n",
        "array6 = array1[:]\n",
        "print(array6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array2d:\n",
            " [[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n",
            "array2d[0:2, 0:2] \n",
            " [[1 2]\n",
            " [4 5]]\n",
            "array2d[1:3, 0:3] \n",
            " [[4 5 6]\n",
            " [7 8 9]]\n",
            "array2d[1:3, :] \n",
            " [[4 5 6]\n",
            " [7 8 9]]\n",
            "array2d[:,:] \n",
            " [[1 2 3]\n",
            " [4 5 6]\n",
            " [7 8 9]]\n",
            "array2d[:2, 1:] \n",
            " [[2 3]\n",
            " [5 6]]\n",
            "array2d[:2, 0] \n",
            " [1 4]\n"
          ]
        }
      ],
      "source": [
        "array1d = np.arange(start=1, stop=10)\n",
        "arary2d = array1d.reshape(3,3)\n",
        "print('array2d:\\n', array2d)\n",
        "\n",
        "print('array2d[0:2, 0:2] \\n', array2d[0:2, 0:2])\n",
        "print('array2d[1:3, 0:3] \\n', array2d[1:3, 0:3])\n",
        "print('array2d[1:3, :] \\n', array2d[1:3, :])\n",
        "print('array2d[:,:] \\n', array2d[:,:])\n",
        "print('array2d[:2, 1:] \\n', array2d[:2, 1:])\n",
        "print('array2d[:2, 0] \\n', array2d[:2, 0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[1 2 3]\n",
            "[4 5 6]\n",
            "array2d[0] shape: (3,) array2d[1] shape: (3,)\n"
          ]
        }
      ],
      "source": [
        "print(array2d[0])\n",
        "print(array2d[1])\n",
        "print('array2d[0] shape:', array2d[0].shape, 'array2d[1] shape:', array2d[1].shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array2d[[0,1], 2] =>  [3, 6]\n",
            "array2d[[0,1], 0:2] =>  [[1, 2], [4, 5]]\n",
            "array2d[[0,1]] =>  [[1, 2, 3], [4, 5, 6]]\n"
          ]
        }
      ],
      "source": [
        "array1d = np.arange(start=1, stop=10)\n",
        "array2d = array1d.reshape(3,3)\n",
        "\n",
        "array3 = array2d[[0,1], 2]\n",
        "print('array2d[[0,1], 2] => ', array3.tolist())\n",
        "\n",
        "array4 = array2d[[0,1], 0:2]\n",
        "print('array2d[[0,1], 0:2] => ', array4.tolist())\n",
        "\n",
        "array5 = array2d[[0,1]]\n",
        "print('array2d[[0,1]] => ', array5.tolist())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1d>5 불린 인덱싱 결과 값 : [6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "array1d = np.arange(start=1, stop=10)\n",
        "# [ ] 안에 array1d > 5 Boolean indexing을 적용\n",
        "array3 = array1d[array1d>5]\n",
        "print('array1d>5 불린 인덱싱 결과 값 :', array3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([False, False, False, False, False,  True,  True,  True,  True])"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "array1d > 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "불린 인덱스로 필터링 결과 : [6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "boolean_indexes = np.array([False, False, False, False, False, True, True, True, True])\n",
        "array3 = array1d[boolean_indexes]\n",
        "print('불린 인덱스로 필터링 결과 :', array3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "일반 인덱스로 필터링 결과 : [6 7 8 9]\n"
          ]
        }
      ],
      "source": [
        "indexes = np.array([5, 6, 7, 8])\n",
        "array4 = array1d[indexes]\n",
        "print('일반 인덱스로 필터링 결과 :', array4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "원본 행렬: [3 1 9 5]\n",
            "np.sort() 호출 후 반환된 정렬 행렬: [1 3 5 9]\n",
            "np.sort() 호출 후 원본 행렬: [3 1 9 5]\n",
            "org_array.sort() 호출 후 반환된 행렬: None\n",
            "org_array.sort() 호출 후 원본 행렬: [1 3 5 9]\n"
          ]
        }
      ],
      "source": [
        "org_array = np.array([3, 1, 9, 5])\n",
        "print('원본 행렬:', org_array)\n",
        "#np.sort()로 정렬\n",
        "sort_array1 = np.sort(org_array)\n",
        "print('np.sort() 호출 후 반환된 정렬 행렬:', sort_array1)\n",
        "print('np.sort() 호출 후 원본 행렬:', org_array)\n",
        "#ndarray.sort()로 정렬\n",
        "sort_array2 = org_array.sort()\n",
        "print('org_array.sort() 호출 후 반환된 행렬:', sort_array2)\n",
        "print('org_array.sort() 호출 후 원본 행렬:', org_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "내림차순으로 정렬: [9 5 3 1]\n"
          ]
        }
      ],
      "source": [
        "sort_array1_desc = np.sort(org_array)[::-1]\n",
        "print('내림차순으로 정렬:', sort_array1_desc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "로우 방향으로 정렬:\n",
            " [[ 7  1]\n",
            " [ 8 12]]\n",
            "칼럼 방향으로 정렬:\n",
            " [[ 8 12]\n",
            " [ 1  7]]\n"
          ]
        }
      ],
      "source": [
        "array2d = np.array([[8,12],\n",
        "                   [7,1]])\n",
        "sort_array2d_axis0 = np.sort(array2d, axis=0)\n",
        "print('로우 방향으로 정렬:\\n', sort_array2d_axis0)\n",
        "\n",
        "sort_array2d_axis1 = np.sort(array2d, axis=1)\n",
        "print('칼럼 방향으로 정렬:\\n', sort_array2d_axis1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "행렬 정렬 시 원본 행렬의 인덱스: [1 0 3 2]\n"
          ]
        }
      ],
      "source": [
        "org_array = np.array([3, 1, 9, 5])\n",
        "sort_indices = np.argsort(org_array)\n",
        "print(type(sort_indices))\n",
        "print('행렬 정렬 시 원본 행렬의 인덱스:', sort_indices)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "행렬 정렬 시 원본 행렬의 인덱스: [2 3 0 1]\n"
          ]
        }
      ],
      "source": [
        "org_array = np.array([3, 1, 9, 5])\n",
        "sort_indices_desc = np.argsort(org_array)[::-1]\n",
        "print('행렬 정렬 시 원본 행렬의 인덱스:', sort_indices_desc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "성적 오름차순 정렬 시 score_array의 인덱스: [0 2 4 1 3]\n",
            "성적 오름차순으로 name_array의 이름 출력: ['John' 'Sarah' 'Samuel' 'Mike' 'Kate']\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "name_array = np.array(['John', 'Mike', 'Sarah', 'Kate', 'Samuel'])\n",
        "score_array = np.array([78, 95, 84, 98, 88])\n",
        "\n",
        "sort_indices_asc = np.argsort(score_array)\n",
        "print('성적 오름차순 정렬 시 score_array의 인덱스:', sort_indices_asc)\n",
        "print('성적 오름차순으로 name_array의 이름 출력:', name_array[sort_indices_asc])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "행렬 내적 결과:\n",
            " [[ 58  64]\n",
            " [139 154]]\n"
          ]
        }
      ],
      "source": [
        "A = np.array( [[1,2,3],\n",
        "               [4,5,6]] )\n",
        "B = np.array( [[7,8],\n",
        "               [9,10],\n",
        "               [11,12]])\n",
        "dot_product = np.dot(A, B)\n",
        "print('행렬 내적 결과:\\n', dot_product)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A의 전치 행렬:\n",
            " [[1 3]\n",
            " [2 4]]\n"
          ]
        }
      ],
      "source": [
        "A = np.array([[1,2],\n",
        "              [3,4]])\n",
        "transpose_mat = np.transpose(A)\n",
        "print('A의 전치 행렬:\\n', transpose_mat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'FileNotFoundError'>",
          "evalue": "[Errno 44] No such file or directory: '/Users/jieun/Downloads/titanic_train.csv'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[40], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m titanic_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/Users/jieun/Downloads/titanic_train.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m titanic_df\u001b[38;5;241m.\u001b[39mhead(\u001b[38;5;241m3\u001b[39m)\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/util/_decorators.py:211\u001b[0m, in \u001b[0;36mdeprecate_kwarg.<locals>._deprecate_kwarg.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m         kwargs[new_arg_name] \u001b[38;5;241m=\u001b[39m new_arg_value\n\u001b[0;32m--> 211\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/io/parsers/readers.py:950\u001b[0m, in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    935\u001b[0m kwds_defaults \u001b[38;5;241m=\u001b[39m _refine_defaults_read(\n\u001b[1;32m    936\u001b[0m     dialect,\n\u001b[1;32m    937\u001b[0m     delimiter,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    946\u001b[0m     defaults\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdelimiter\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m\"\u001b[39m},\n\u001b[1;32m    947\u001b[0m )\n\u001b[1;32m    948\u001b[0m kwds\u001b[38;5;241m.\u001b[39mupdate(kwds_defaults)\n\u001b[0;32m--> 950\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/io/parsers/readers.py:605\u001b[0m, in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    602\u001b[0m _validate_names(kwds\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnames\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    604\u001b[0m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[0;32m--> 605\u001b[0m parser \u001b[38;5;241m=\u001b[39m \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[1;32m    608\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1442\u001b[0m, in \u001b[0;36mTextFileReader.__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1439\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m kwds[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhas_index_names\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m   1441\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles: IOHandles \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1442\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/io/parsers/readers.py:1735\u001b[0m, in \u001b[0;36mTextFileReader._make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1733\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[1;32m   1734\u001b[0m         mode \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m-> 1735\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;241m=\u001b[39m \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1736\u001b[0m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1737\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1738\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1739\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcompression\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1740\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmemory_map\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1741\u001b[0m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1742\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mencoding_errors\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstrict\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1743\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage_options\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1744\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1745\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1746\u001b[0m f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhandles\u001b[38;5;241m.\u001b[39mhandle\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/io/common.py:856\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    851\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    852\u001b[0m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[1;32m    853\u001b[0m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[1;32m    854\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mencoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m    855\u001b[0m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[0;32m--> 856\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m    857\u001b[0m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    858\u001b[0m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    859\u001b[0m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mioargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    860\u001b[0m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    861\u001b[0m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    862\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    863\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[1;32m    865\u001b[0m         handle \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(handle, ioargs\u001b[38;5;241m.\u001b[39mmode)\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 44] No such file or directory: '/Users/jieun/Downloads/titanic_train.csv'"
          ]
        }
      ],
      "source": [
        "titanic_df = pd.read_csv('/Users/jieun/Downloads/titanic_train.csv')\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "titanic 변수 type: <class 'pandas.core.frame.DataFrame'>\n"
          ]
        },
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
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              "      <td>5</td>\n",
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              "      <td>Allen, Mr. William Henry</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
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              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
              "4              5         0       3   \n",
              "..           ...       ...     ...   \n",
              "886          887         0       2   \n",
              "887          888         1       1   \n",
              "888          889         0       3   \n",
              "889          890         1       1   \n",
              "890          891         0       3   \n",
              "\n",
              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
              "889      0            111369  30.0000  C148        C  \n",
              "890      0            370376   7.7500   NaN        Q  \n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "print('titanic 변수 type:', type(titanic_df))\n",
        "titanic_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DataFrame 크기: (891, 12)\n"
          ]
        }
      ],
      "source": [
        "print('DataFrame 크기:', titanic_df.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 66.2+ KB\n"
          ]
        }
      ],
      "source": [
        "titanic_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
              "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
              "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
              "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
              "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
              "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
              "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
              "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
              "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
              "\n",
              "            Parch        Fare  \n",
              "count  891.000000  891.000000  \n",
              "mean     0.381594   32.204208  \n",
              "std      0.806057   49.693429  \n",
              "min      0.000000    0.000000  \n",
              "25%      0.000000    7.910400  \n",
              "50%      0.000000   14.454200  \n",
              "75%      0.000000   31.000000  \n",
              "max      6.000000  512.329200  "
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3    491\n",
            "1    216\n",
            "2    184\n",
            "Name: Pclass, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "value_counts = titanic_df['Pclass'].value_counts()\n",
        "print(value_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n"
          ]
        }
      ],
      "source": [
        "titanic_pclass = titanic_df['Pclass']\n",
        "print(type(titanic_pclass))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    3\n",
              "1    1\n",
              "2    3\n",
              "3    1\n",
              "4    3\n",
              "Name: Pclass, dtype: int64"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_pclass.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.series.Series'>\n",
            "3    491\n",
            "1    216\n",
            "2    184\n",
            "Name: Pclass, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "value_counts = titanic_df['Pclass'].value_counts()\n",
        "print(type(value_counts))\n",
        "print(value_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "titanic_df 데이터 건수: 891\n",
            "기본 설정인 dropna=True로 value_counts()\n",
            "S    644\n",
            "C    168\n",
            "Q     77\n",
            "Name: Embarked, dtype: int64\n",
            "S      644\n",
            "C      168\n",
            "Q       77\n",
            "NaN      2\n",
            "Name: Embarked, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "print('titanic_df 데이터 건수:', titanic_df.shape[0])\n",
        "print('기본 설정인 dropna=True로 value_counts()')\n",
        "#value_counts()는 디폴트로 dropna=True이므로 value_counts(dropna=True)와 동일\n",
        "print(titanic_df['Embarked'].value_counts())\n",
        "print(titanic_df['Embarked'].value_counts(dropna=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array1 shape: (3,)\n",
            "1차원 리스트로 만든 DataFrame:\n",
            "    col1\n",
            "0     1\n",
            "1     2\n",
            "2     3\n",
            "1차원 ndarray로 만든 DataFrame:\n",
            "    col1\n",
            "0     1\n",
            "1     2\n",
            "2     3\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "col_name1=['col1']\n",
        "list1 = [1, 2, 3]\n",
        "array1 = np.array(list1)\n",
        "print('array1 shape:', array1.shape)\n",
        "#리스트를 이용해 DataFrame 생성\n",
        "df_list1 = pd.DataFrame(list1, columns=col_name1)\n",
        "print('1차원 리스트로 만든 DataFrame:\\n', df_list1)\n",
        "#넘파이 ndarray를 이용해 DataFrame 생성\n",
        "df_array1 = pd.DataFrame(array1, columns=col_name1)\n",
        "print('1차원 ndarray로 만든 DataFrame:\\n', df_array1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "array2 shape: (2, 3)\n",
            "2차원 리스트로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    12    13\n",
            "2차원 ndarray로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    12    13\n"
          ]
        }
      ],
      "source": [
        "#3개의 칼럼명이 필요함\n",
        "col_name2 = ['col1', 'col2', 'col3']\n",
        "\n",
        "#2행x3열 형태의 리스트와 ndarray 생성한 뒤 이를 DataFrame으로 변환\n",
        "list2 = [[1,2,3],\n",
        "         [11,12,13]]\n",
        "array2 = np.array(list2)\n",
        "print('array2 shape:', array2.shape)\n",
        "df_list2 = pd.DataFrame(list2, columns=col_name2)\n",
        "print('2차원 리스트로 만든 DataFrame:\\n', df_list2)\n",
        "df_array2 = pd.DataFrame(array2, columns=col_name2)\n",
        "print('2차원 ndarray로 만든 DataFrame:\\n', df_array2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "딕셔너리로 만든 DataFrame:\n",
            "    col1  col2  col3\n",
            "0     1     2     3\n",
            "1    11    22    33\n"
          ]
        }
      ],
      "source": [
        "#Key는 문자열 칼럼며으로 매핑, Value는 리스트 형(또는 ndarray) 칼럼 데이터로 매핑\n",
        "dict = {'col1':[1, 11], 'col2':[2, 22], 'col3':[3, 33]}\n",
        "df_dict = pd.DataFrame(dict)\n",
        "print('딕셔너리로 만든 DataFrame:\\n', df_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df_dict.values 타입: <class 'numpy.ndarray'> df_dict.values shape: (2, 3)\n",
            "[[ 1  2  3]\n",
            " [11 22 33]]\n"
          ]
        }
      ],
      "source": [
        "#DataFrame을 ndarray로 변환\n",
        "array3 = df_dict.values\n",
        "print('df_dict.values 타입:', type(array3), 'df_dict.values shape:', array3.shape)\n",
        "print(array3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "df_dict.values.tolist() 타입: <class 'list'>\n",
            "[[1, 2, 3], [11, 22, 33]]\n",
            "\n",
            " df_dict.to_dict() 타입: <class 'dict'>\n",
            "{'col1': [1, 11], 'col2': [2, 22], 'col3': [3, 33]}\n"
          ]
        }
      ],
      "source": [
        "#DataFrame을 리스트로 변환\n",
        "list3 = df_dict.values.tolist()\n",
        "print('df_dict.values.tolist() 타입:', type(list3))\n",
        "print(list3)\n",
        "\n",
        "#DataFrame을 딕셔너리로 변환\n",
        "dict3 = df_dict.to_dict('list')\n",
        "print('\\n df_dict.to_dict() 타입:', type(dict3))\n",
        "print(dict3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>SibSp</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  Age_0  \n",
              "0      0         A/5 21171   7.2500   NaN        S      0  \n",
              "1      0          PC 17599  71.2833   C85        C      0  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S      0  "
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df['Age_0'] = 0\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  Age_0  Age_by_10  \\\n",
              "0      0         A/5 21171   7.2500   NaN        S      0      220.0   \n",
              "1      0          PC 17599  71.2833   C85        C      0      380.0   \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S      0      260.0   \n",
              "\n",
              "   Family_No  \n",
              "0          2  \n",
              "1          2  \n",
              "2          1  "
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df['Age_by_10'] = titanic_df['Age']*10\n",
        "titanic_df['Family_No'] = titanic_df['SibSp'] + titanic_df['Parch']+1\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  Age_0  Age_by_10  \\\n",
              "0      0         A/5 21171   7.2500   NaN        S      0      320.0   \n",
              "1      0          PC 17599  71.2833   C85        C      0      480.0   \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S      0      360.0   \n",
              "\n",
              "   Family_No  \n",
              "0          2  \n",
              "1          2  \n",
              "2          1  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df['Age_by_10'] = titanic_df['Age_by_10']+100\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  Age_by_10  Family_No  \n",
              "0      0         A/5 21171   7.2500   NaN        S      320.0          2  \n",
              "1      0          PC 17599  71.2833   C85        C      480.0          2  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S      360.0          1  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_drop_df = titanic_df.drop('Age_0', axis=1)\n",
        "titanic_drop_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Age_0</th>\n",
              "      <th>Age_by_10</th>\n",
              "      <th>Family_No</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  Age_0  Age_by_10  \\\n",
              "0      0         A/5 21171   7.2500   NaN        S      0      320.0   \n",
              "1      0          PC 17599  71.2833   C85        C      0      480.0   \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S      0      360.0   \n",
              "\n",
              "   Family_No  \n",
              "0          2  \n",
              "1          2  \n",
              "2          1  "
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " inplace=True로 drop 후 반환된 값: None\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<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",
              "        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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  "
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "drop_result = titanic_df.drop(['Age_0', 'Age_by_10', 'Family_No'], axis=1, inplace=True)\n",
        "print(' inplace=True로 drop 후 반환된 값:', drop_result)\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "#### before axis 0 drop ####\n",
            "   PassengerId  Survived  Pclass            Name     Sex   Age  SibSp  Parch          Ticket     Fare Cabin Embarked\n",
            "0            1         0       3  Braund, Mr....    male  22.0      1      0       A/5 21171   7.2500   NaN        S\n",
            "1            2         1       1  Cumings, Mr...  female  38.0      1      0        PC 17599  71.2833   C85        C\n",
            "2            3         1       3  Heikkinen, ...  female  26.0      0      0  STON/O2. 31...   7.9250   NaN        S\n",
            "#### after axis 0 drop ####\n",
            "   PassengerId  Survived  Pclass            Name     Sex   Age  SibSp  Parch  Ticket     Fare Cabin Embarked\n",
            "3            4         1       1  Futrelle, M...  female  35.0      1      0  113803  53.1000  C123        S\n",
            "4            5         0       3  Allen, Mr. ...    male  35.0      0      0  373450   8.0500   NaN        S\n",
            "5            6         0       3  Moran, Mr. ...    male   NaN      0      0  330877   8.4583   NaN        Q\n"
          ]
        }
      ],
      "source": [
        "pd.set_option('display.width', 1000)\n",
        "pd.set_option('display.max_colwidth', 15)\n",
        "print('#### before axis 0 drop ####')\n",
        "print(titanic_df.head(3))\n",
        "\n",
        "titanic_df.drop([0,1,2], axis=0, inplace=True)\n",
        "\n",
        "print('#### after axis 0 drop ####')\n",
        "print(titanic_df.head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RangeIndex(start=0, stop=891, step=1)\n",
            "Index 객체 array값:\n",
            " [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
            "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
            "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
            "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
            "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
            "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
            " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
            " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
            " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
            " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
            " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
            " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
            " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
            " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
            " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
            " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
            " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
            " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
            " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
            " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
            " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
            " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
            " 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n",
            " 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431\n",
            " 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449\n",
            " 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467\n",
            " 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485\n",
            " 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503\n",
            " 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521\n",
            " 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539\n",
            " 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557\n",
            " 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575\n",
            " 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593\n",
            " 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611\n",
            " 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629\n",
            " 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647\n",
            " 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665\n",
            " 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683\n",
            " 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701\n",
            " 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719\n",
            " 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737\n",
            " 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755\n",
            " 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773\n",
            " 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791\n",
            " 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809\n",
            " 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827\n",
            " 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845\n",
            " 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863\n",
            " 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881\n",
            " 882 883 884 885 886 887 888 889 890]\n"
          ]
        }
      ],
      "source": [
        "#원본 파일 다시 로딩\n",
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "#Index 객체 추출\n",
        "indexes = titanic_df.index\n",
        "print(indexes)\n",
        "#index 객체를 실제 값 array로 변환\n",
        "print('Index 객체 array값:\\n', indexes.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(891,)\n",
            "[0 1 2 3 4]\n",
            "[0 1 2 3 4]\n",
            "6\n"
          ]
        }
      ],
      "source": [
        "print(type(indexes.values))\n",
        "print(indexes.values.shape)\n",
        "print(indexes[:5].values)\n",
        "print(indexes.values[:5])\n",
        "print(indexes[6])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'TypeError'>",
          "evalue": "Index does not support mutable operations",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[65], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mindexes\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m5\u001b[39m\n",
            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/indexes/base.py:5302\u001b[0m, in \u001b[0;36mIndex.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   5300\u001b[0m \u001b[38;5;129m@final\u001b[39m\n\u001b[1;32m   5301\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value):\n\u001b[0;32m-> 5302\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIndex does not support mutable operations\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
            "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
          ]
        }
      ],
      "source": [
        "indexes[0] = 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fair Series max 값: 512.3292\n",
            "Fair Series sum 값: 28693.9493\n",
            "sum() Fair Series: 28693.949299999967\n",
            "Fair Series + 3:\n",
            " 0    10.2500\n",
            "1    74.2833\n",
            "2    10.9250\n",
            "Name: Fare, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "series_fair = titanic_df['Fare']\n",
        "print('Fair Series max 값:', series_fair.max())\n",
        "print('Fair Series sum 값:', series_fair.sum())\n",
        "print('sum() Fair Series:', sum(series_fair))\n",
        "print('Fair Series + 3:\\n',(series_fair + 3).head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
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              "<div>\n",
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              "\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>index</th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
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              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr....</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>Cumings, Mr...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
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              "      <td>C</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, ...</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 31...</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   index  PassengerId  Survived  Pclass            Name     Sex   Age  SibSp  Parch          Ticket     Fare Cabin Embarked\n",
              "0      0            1         0       3  Braund, Mr....    male  22.0      1      0       A/5 21171   7.2500   NaN        S\n",
              "1      1            2         1       1  Cumings, Mr...  female  38.0      1      0        PC 17599  71.2833   C85        C\n",
              "2      2            3         1       3  Heikkinen, ...  female  26.0      0      0  STON/O2. 31...   7.9250   NaN        S"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "titanic_reset_df = titanic_df.reset_index(inplace=False)\n",
        "titanic_reset_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "### before reset_index ###\n",
            "3    491\n",
            "1    216\n",
            "2    184\n",
            "Name: Pclass, dtype: int64\n",
            "value_counts 객체 변수 타입: <class 'pandas.core.series.Series'>\n",
            "### After reset_index ###\n",
            "   index  Pclass\n",
            "0      3     491\n",
            "1      1     216\n",
            "2      2     184\n",
            "new_value_counts 객체 변수 타입: <class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print('### before reset_index ###')\n",
        "value_counts = titanic_df['Pclass'].value_counts()\n",
        "print(value_counts)\n",
        "print('value_counts 객체 변수 타입:',type(value_counts))\n",
        "\n",
        "new_value_counts = value_counts.reset_index(inplace=False)\n",
        "print('### After reset_index ###')\n",
        "print(new_value_counts)\n",
        "print('new_value_counts 객체 변수 타입:',type(new_value_counts))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "단일 컬럼 데이터 추출:\n",
            " 0    3\n",
            "1    1\n",
            "2    3\n",
            "Name: Pclass, dtype: int64\n",
            "\n",
            "여러 컬럼들의 데이터 추출:\n",
            "    Survived  Pclass\n",
            "0         0       3\n",
            "1         1       1\n",
            "2         1       3\n"
          ]
        }
      ],
      "source": [
        "print('단일 컬럼 데이터 추출:\\n', titanic_df[ 'Pclass' ].head(3))\n",
        "print('\\n여러 컬럼들의 데이터 추출:\\n', titanic_df[ ['Survived', 'Pclass'] ].head(3))\n",
        "print('[ ] 안에 숫자 index는 KeyError 오류 발생:\\n', titanic_df[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df[0:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df[ titanic_df['Pclass'] == 3].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "data = {'Name': ['Chulmin', 'Eunkyung','Jinwoong','Soobeom'],\n",
        "        'Year': [2011, 2016, 2015, 2015],\n",
        "        'Gender': ['Male', 'Female', 'Male', 'Male']\n",
        "       }\n",
        "data_df = pd.DataFrame(data, index=['one','two','three','four'])\n",
        "data_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "data_df.iloc[0,0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 아래 코드는 오류를 발생시킵니다.\n",
        "data_df.iloc[0, 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 아래 코드는 오류를 발생합니다.\n",
        "data_df.iloc['one', 0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print(\"\\n 맨 마지막 칼럼 데이터 [:, -1] \\n\", data_df.iloc[:, -1])\n",
        "print(\"\\n 맨 마지막 칼럼을 제외한 모든 데이터 [:, :-1] \\n\", data_df.iloc[: , :-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "data_df.loc['one', 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 아래 코드는 오류를 발생합니다. \n",
        "data_df_reset.loc[0, 'Name']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('위치기반 iloc slicing\\n', data_df.iloc[0:1, 0],'\\n')\n",
        "print('명칭기반 loc slicing\\n', data_df.loc['one':'two', 'Name'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df = pd.read_csv('titanic_train.csv')\n",
        "titanic_boolean = titanic_df[titanic_df['Age']>60]\n",
        "print(type(titanic_boolean))\n",
        "titanic_boolean"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df[titanic_df['Age'] > 60][['Name','Age']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.loc[titanic_df['Age'] > 60, ['Name','Age']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df[ (titanic_df['Age'] > 60) & (titanic_df['Pclass']==1) & (titanic_df['Sex']=='female')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "cond1 = titanic_df['Age'] > 60\n",
        "cond2 = titanic_df['Pclass']==1\n",
        "cond3 = titanic_df['Sex']=='female'\n",
        "titanic_df[ cond1 & cond2 & cond3]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_sorted = titanic_df.sort_values(by=['Name'])\n",
        "titanic_sorted.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_sorted = titanic_df.sort_values(by=['Pclass', 'Name'], ascending=False)\n",
        "titanic_sorted.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df[['Age', 'Fare']].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_groupby = titanic_df.groupby(by='Pclass')\n",
        "print(type(titanic_groupby))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_groupby = titanic_df.groupby('Pclass').count()\n",
        "titanic_groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_groupby = titanic_df.groupby('Pclass')[['PassengerId', 'Survived']].count()\n",
        "titanic_groupby"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.groupby('Pclass')['Age'].agg([max, min])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "agg_format={'Age':'max', 'SibSp':'sum', 'Fare':'mean'}\n",
        "titanic_df.groupby('Pclass').agg(agg_format)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.isna().head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.isna( ).sum( )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Cabin'] = titanic_df['Cabin'].fillna('C000')\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Age'] = titanic_df['Age'].fillna(titanic_df['Age'].mean())\n",
        "titanic_df['Embarked'] = titanic_df['Embarked'].fillna('S')\n",
        "titanic_df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def get_square(a):\n",
        "    return a**2\n",
        "\n",
        "print('3의 제곱은:',get_square(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "lambda_square = lambda x : x ** 2\n",
        "print('3의 제곱은:',lambda_square(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "a=[1,2,3]\n",
        "squares = map(lambda x : x**2, a)\n",
        "list(squares)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Name_len']= titanic_df['Name'].apply(lambda x : len(x))\n",
        "titanic_df[['Name','Name_len']].head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Child_Adult'] = titanic_df['Age'].apply(lambda x : 'Child' if x <=15 else 'Adult' )\n",
        "titanic_df[['Age','Child_Adult']].head(8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Age_cat'] = titanic_df['Age'].apply(lambda x : 'Child' if x<=15 else ('Adult' if x <= 60 else \n",
        "                                                                                  'Elderly'))\n",
        "titanic_df['Age_cat'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 나이에 따라 세분화된 분류를 수행하는 함수 생성. \n",
        "def get_category(age):\n",
        "    cat = ''\n",
        "    if age <= 5: cat = 'Baby'\n",
        "    elif age <= 12: cat = 'Child'\n",
        "    elif age <= 18: cat = 'Teenager'\n",
        "    elif age <= 25: cat = 'Student'\n",
        "    elif age <= 35: cat = 'Young Adult'\n",
        "    elif age <= 60: cat = 'Adult'\n",
        "    else : cat = 'Elderly'\n",
        "    \n",
        "    return cat\n",
        "\n",
        "# lambda 식에 위에서 생성한 get_category( ) 함수를 반환값으로 지정. \n",
        "# get_category(X)는 입력값으로 ‘Age’ 컬럼 값을 받아서 해당하는 cat 반환\n",
        "titanic_df['Age_cat'] = titanic_df['Age'].apply(lambda x : get_category(x))\n",
        "titanic_df[['Age','Age_cat']].head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# 붓꽃 데이터 세트를 로딩합니다. \n",
        "iris = load_iris()\n",
        "\n",
        "# iris.data는 Iris 데이터 세트에서 피처(feature)만으로 된 데이터를 numpy로 가지고 있습니다. \n",
        "iris_data = iris.data\n",
        "\n",
        "# iris.target은 붓꽃 데이터 세트에서 레이블(결정 값) 데이터를 numpy로 가지고 있습니다. \n",
        "iris_label = iris.target\n",
        "print('iris target값:', iris_label)\n",
        "print('iris target명:', iris.target_names)\n",
        "\n",
        "# 붓꽃 데이터 세트를 자세히 보기 위해 DataFrame으로 변환합니다. \n",
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n",
        "iris_df['label'] = iris.target\n",
        "iris_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(iris_data, iris_label, \n",
        "                                                    test_size=0.2, random_state=11)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# DecisionTreeClassifier 객체 생성 \n",
        "dt_clf = DecisionTreeClassifier(random_state=11)\n",
        "\n",
        "# 학습 수행 \n",
        "dt_clf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 학습이 완료된 DecisionTreeClassifier 객체에서 테스트 데이터 세트로 예측 수행. \n",
        "pred = dt_clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris_data = load_iris()\n",
        "print(type(iris_data))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "keys = iris_data.keys()\n",
        "print('붓꽃 데이터 세트의 키들:', keys)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('\\n feature_names 의 type:',type(iris_data.feature_names))\n",
        "print(' feature_names 의 shape:',len(iris_data.feature_names))\n",
        "print(iris_data.feature_names)\n",
        "\n",
        "print('\\n target_names 의 type:',type(iris_data.target_names))\n",
        "print(' feature_names 의 shape:',len(iris_data.target_names))\n",
        "print(iris_data.target_names)\n",
        "\n",
        "print('\\n data 의 type:',type(iris_data.data))\n",
        "print(' data 의 shape:',iris_data.data.shape)\n",
        "print(iris_data['data'])\n",
        "\n",
        "print('\\n target 의 type:',type(iris_data.target))\n",
        "print(' target 의 shape:',iris_data.target.shape)\n",
        "print(iris_data.target)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "iris = load_iris()\n",
        "dt_clf = DecisionTreeClassifier()\n",
        "train_data = iris.data\n",
        "train_label = iris.target\n",
        "dt_clf.fit(train_data, train_label)\n",
        "\n",
        "# 학습 데이터 셋으로 예측 수행\n",
        "pred = dt_clf.predict(train_data)\n",
        "print('예측 정확도:',accuracy_score(train_label,pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "dt_clf = DecisionTreeClassifier( )\n",
        "iris_data = load_iris()\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target, \n",
        "                                                    test_size=0.3, random_state=121)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "dt_clf.fit(X_train, y_train)\n",
        "pred = dt_clf.predict(X_test)\n",
        "print('예측 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import KFold\n",
        "import numpy as np\n",
        "\n",
        "iris = load_iris()\n",
        "features = iris.data\n",
        "label = iris.target\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "# 5개의 폴드 세트로 분리하는 KFold 객체와 폴드 세트별 정확도를 담을 리스트 객체 생성.\n",
        "kfold = KFold(n_splits=5)\n",
        "cv_accuracy = []\n",
        "print('붓꽃 데이터 세트 크기:',features.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "n_iter = 0\n",
        "\n",
        "# KFold객체의 split( ) 호출하면 폴드 별 학습용, 검증용 테스트의 로우 인덱스를 array로 반환  \n",
        "for train_index, test_index  in kfold.split(features):\n",
        "    # kfold.split( )으로 반환된 인덱스를 이용하여 학습용, 검증용 테스트 데이터 추출\n",
        "    X_train, X_test = features[train_index], features[test_index]\n",
        "    y_train, y_test = label[train_index], label[test_index]\n",
        "    #학습 및 예측 \n",
        "    dt_clf.fit(X_train , y_train)    \n",
        "    pred = dt_clf.predict(X_test)\n",
        "    n_iter += 1\n",
        "    # 반복 시 마다 정확도 측정 \n",
        "    accuracy = np.round(accuracy_score(y_test,pred), 4)\n",
        "    train_size = X_train.shape[0]\n",
        "    test_size = X_test.shape[0]\n",
        "    print('\\n#{0} 교차 검증 정확도 :{1}, 학습 데이터 크기: {2}, 검증 데이터 크기: {3}'\n",
        "          .format(n_iter, accuracy, train_size, test_size))\n",
        "    print('#{0} 검증 세트 인덱스:{1}'.format(n_iter,test_index))\n",
        "    cv_accuracy.append(accuracy)\n",
        "    \n",
        "# 개별 iteration별 정확도를 합하여 평균 정확도 계산 \n",
        "print('\\n## 평균 검증 정확도:', np.mean(cv_accuracy)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "iris = load_iris()\n",
        "\n",
        "iris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\n",
        "iris_df['label']=iris.target\n",
        "iris_df['label'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "kfold = KFold(n_splits=3)\n",
        "# kfold.split(X)는 폴드 세트를 3번 반복할 때마다 달라지는 학습/테스트 용 데이터 로우 인덱스 번호 반환. \n",
        "n_iter =0\n",
        "for train_index, test_index  in kfold.split(iris_df):\n",
        "    n_iter += 1\n",
        "    label_train= iris_df['label'].iloc[train_index]\n",
        "    label_test= iris_df['label'].iloc[test_index]\n",
        "    print('## 교차 검증: {0}'.format(n_iter))\n",
        "    print('학습 레이블 데이터 분포:\\n', label_train.value_counts())\n",
        "    print('검증 레이블 데이터 분포:\\n', label_test.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "skf = StratifiedKFold(n_splits=3)\n",
        "n_iter=0\n",
        "\n",
        "for train_index, test_index in skf.split(iris_df, iris_df['label']):\n",
        "    n_iter += 1\n",
        "    label_train= iris_df['label'].iloc[train_index]\n",
        "    label_test= iris_df['label'].iloc[test_index]\n",
        "    print('## 교차 검증: {0}'.format(n_iter))\n",
        "    print('학습 레이블 데이터 분포:\\n', label_train.value_counts())\n",
        "    print('검증 레이블 데이터 분포:\\n', label_test.value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "skfold = StratifiedKFold(n_splits=3)\n",
        "n_iter=0\n",
        "cv_accuracy=[]\n",
        "\n",
        "# StratifiedKFold의 split( ) 호출시 반드시 레이블 데이터 셋도 추가 입력 필요  \n",
        "for train_index, test_index  in skfold.split(features, label):\n",
        "    # split( )으로 반환된 인덱스를 이용하여 학습용, 검증용 테스트 데이터 추출\n",
        "    X_train, X_test = features[train_index], features[test_index]\n",
        "    y_train, y_test = label[train_index], label[test_index]\n",
        "    #학습 및 예측 \n",
        "    dt_clf.fit(X_train , y_train)    \n",
        "    pred = dt_clf.predict(X_test)\n",
        "\n",
        "    # 반복 시 마다 정확도 측정 \n",
        "    n_iter += 1\n",
        "    accuracy = np.round(accuracy_score(y_test,pred), 4)\n",
        "    train_size = X_train.shape[0]\n",
        "    test_size = X_test.shape[0]\n",
        "    print('\\n#{0} 교차 검증 정확도 :{1}, 학습 데이터 크기: {2}, 검증 데이터 크기: {3}'\n",
        "          .format(n_iter, accuracy, train_size, test_size))\n",
        "    print('#{0} 검증 세트 인덱스:{1}'.format(n_iter,test_index))\n",
        "    cv_accuracy.append(accuracy)\n",
        "    \n",
        "# 교차 검증별 정확도 및 평균 정확도 계산 \n",
        "print('\\n## 교차 검증별 정확도:', np.round(cv_accuracy, 4))\n",
        "print('## 평균 검증 정확도:', np.round(np.mean(cv_accuracy), 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import cross_val_score , cross_validate\n",
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris_data = load_iris()\n",
        "dt_clf = DecisionTreeClassifier(random_state=156)\n",
        "\n",
        "data = iris_data.data\n",
        "label = iris_data.target\n",
        "\n",
        "# 성능 지표는 정확도(accuracy) , 교차 검증 세트는 3개 \n",
        "scores = cross_val_score(dt_clf , data , label , scoring='accuracy',cv=3)\n",
        "print('교차 검증별 정확도:',np.round(scores, 4))\n",
        "print('평균 검증 정확도:', np.round(np.mean(scores), 4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# 데이터를 로딩하고 학습데이타와 테스트 데이터 분리\n",
        "iris = load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target, \n",
        "                                                    test_size=0.2, random_state=121)\n",
        "dtree = DecisionTreeClassifier()\n",
        "\n",
        "### parameter 들을 dictionary 형태로 설정\n",
        "parameters = {'max_depth':[1,2,3], 'min_samples_split':[2,3]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "# param_grid의 하이퍼 파라미터들을 3개의 train, test set fold 로 나누어서 테스트 수행 설정.  \n",
        "### refit=True 가 default 임. True이면 가장 좋은 파라미터 설정으로 재 학습 시킴.  \n",
        "grid_dtree = GridSearchCV(dtree, param_grid=parameters, cv=3, refit=True)\n",
        "\n",
        "# 붓꽃 Train 데이터로 param_grid의 하이퍼 파라미터들을 순차적으로 학습/평가 .\n",
        "grid_dtree.fit(X_train, y_train)\n",
        "\n",
        "# GridSearchCV 결과 추출하여 DataFrame으로 변환\n",
        "scores_df = pd.DataFrame(grid_dtree.cv_results_)\n",
        "scores_df[['params', 'mean_test_score', 'rank_test_score', \\\n",
        "           'split0_test_score', 'split1_test_score', 'split2_test_score']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('GridSearchCV 최적 파라미터:', grid_dtree.best_params_)\n",
        "print('GridSearchCV 최고 정확도: {0:.4f}'.format(grid_dtree.best_score_))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# GridSearchCV의 refit으로 이미 학습이 된 estimator 반환\n",
        "estimator = grid_dtree.best_estimator_\n",
        "\n",
        "# GridSearchCV의 best_estimator_는 이미 최적 하이퍼 파라미터로 학습이 됨\n",
        "pred = estimator.predict(X_test)\n",
        "print('테스트 데이터 세트 정확도: {0:.4f}'.format(accuracy_score(y_test,pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "items=['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서']\n",
        "\n",
        "# LabelEncoder를 객체로 생성한 후 , fit( ) 과 transform( ) 으로 label 인코딩 수행. \n",
        "encoder = LabelEncoder()\n",
        "encoder.fit(items)\n",
        "labels = encoder.transform(items)\n",
        "print('인코딩 변환값:',labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('인코딩 클래스:',encoder.classes_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('디코딩 원본 값:',encoder.inverse_transform([4, 5, 2, 0, 1, 1, 3, 3]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "import numpy as np\n",
        "\n",
        "items=['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서']\n",
        "\n",
        "# 2차원 ndarray로 변환합니다. \n",
        "items = np.array(items).reshape(-1, 1)\n",
        "\n",
        "# 원-핫 인코딩을 적용합니다. \n",
        "oh_encoder = OneHotEncoder()\n",
        "oh_encoder.fit(items)\n",
        "oh_labels = oh_encoder.transform(items)\n",
        "\n",
        "# OneHotEncoder로 변환한 결과는 희소행렬이므로 toarray()를 이용해 밀집 행렬로 변환. \n",
        "print('원-핫 인코딩 데이터')\n",
        "print(oh_labels.toarray())\n",
        "print('원-핫 인코딩 데이터 차원')\n",
        "print(oh_labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame({'item':['TV','냉장고','전자레인지','컴퓨터','선풍기','선풍기','믹서','믹서'] })\n",
        "pd.get_dummies(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_iris\n",
        "import pandas as pd\n",
        "# 붓꽃 데이터 셋을 로딩하고 DataFrame으로 변환합니다. \n",
        "iris = load_iris()\n",
        "iris_data = iris.data\n",
        "iris_df = pd.DataFrame(data=iris_data, columns=iris.feature_names)\n",
        "\n",
        "print('feature 들의 평균 값')\n",
        "print(iris_df.mean())\n",
        "print('\\nfeature 들의 분산 값')\n",
        "print(iris_df.var())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# StandardScaler객체 생성\n",
        "scaler = StandardScaler()\n",
        "# StandardScaler 로 데이터 셋 변환. fit( ) 과 transform( ) 호출.  \n",
        "scaler.fit(iris_df)\n",
        "iris_scaled = scaler.transform(iris_df)\n",
        "\n",
        "#transform( )시 scale 변환된 데이터 셋이 numpy ndarry로 반환되어 이를 DataFrame으로 변환\n",
        "iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)\n",
        "print('feature 들의 평균 값')\n",
        "print(iris_df_scaled.mean())\n",
        "print('\\nfeature 들의 분산 값')\n",
        "print(iris_df_scaled.var())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# MinMaxScaler객체 생성\n",
        "scaler = MinMaxScaler()\n",
        "# MinMaxScaler 로 데이터 셋 변환. fit() 과 transform() 호출.  \n",
        "scaler.fit(iris_df)\n",
        "iris_scaled = scaler.transform(iris_df)\n",
        "\n",
        "# transform()시 scale 변환된 데이터 셋이 numpy ndarry로 반환되어 이를 DataFrame으로 변환\n",
        "iris_df_scaled = pd.DataFrame(data=iris_scaled, columns=iris.feature_names)\n",
        "print('feature들의 최솟값')\n",
        "print(iris_df_scaled.min())\n",
        "print('\\nfeature들의 최댓값')\n",
        "print(iris_df_scaled.max())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import numpy as np\n",
        "\n",
        "# 학습 데이터는 0 부터 10까지, 테스트 데이터는 0 부터 5까지 값을 가지는 데이터 세트로 생성\n",
        "# Scaler클래스의 fit(), transform()은 2차원 이상 데이터만 가능하므로 reshape(-1, 1)로 차원 변경\n",
        "train_array = np.arange(0, 11).reshape(-1, 1)\n",
        "test_array =  np.arange(0, 6).reshape(-1, 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# MinMaxScaler 객체에 별도의 feature_range 파라미터 값을 지정하지 않으면 0~1 값으로 변환\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# fit()하게 되면 train_array 데이터의 최솟값이 0, 최댓값이 10으로 설정.\n",
        "scaler.fit(train_array)\n",
        "\n",
        "# 1/10 scale로 train_array 데이터 변환함. 원본 10-> 1로 변환됨.\n",
        "train_scaled = scaler.transform(train_array)\n",
        "\n",
        "print('원본 train_array 데이터:', np.round(train_array.reshape(-1), 2))\n",
        "print('Scale된 train_array 데이터:', np.round(train_scaled.reshape(-1), 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# MinMaxScaler에 test_array를 fit()하게 되면 원본 데이터의 최솟값이 0, 최댓값이 5로 설정됨\n",
        "scaler.fit(test_array)\n",
        "\n",
        "# 1/5 scale로 test_array 데이터 변환함. 원본 5->1로 변환.\n",
        "test_scaled = scaler.transform(test_array)\n",
        "\n",
        "# test_array의 scale 변환 출력.\n",
        "print('원본 test_array 데이터:', np.round(test_array.reshape(-1), 2))\n",
        "print('Scale된 test_array 데이터:', np.round(test_scaled.reshape(-1), 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "scaler = MinMaxScaler()\n",
        "scaler.fit(train_array)\n",
        "train_scaled = scaler.transform(train_array)\n",
        "print('원본 train_array 데이터:', np.round(train_array.reshape(-1), 2))\n",
        "print('Scale된 train_array 데이터:', np.round(train_scaled.reshape(-1), 2))\n",
        "\n",
        "# test_array에 Scale 변환을 할 때는 반드시 fit()을 호출하지 않고 transform() 만으로 변환해야 함. \n",
        "test_scaled = scaler.transform(test_array)\n",
        "print('\\n원본 test_array 데이터:', np.round(test_array.reshape(-1), 2))\n",
        "print('Scale된 test_array 데이터:', np.round(test_scaled.reshape(-1), 2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "titanic_df = pd.read_csv('./titanic_train.csv')\n",
        "titanic_df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print('\\n ### train 데이터 정보 ###  \\n')\n",
        "print(titanic_df.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Age'].fillna(titanic_df['Age'].mean(),inplace=True)\n",
        "titanic_df['Cabin'].fillna('N',inplace=True)\n",
        "titanic_df['Embarked'].fillna('N',inplace=True)\n",
        "print('데이터 세트 Null 값 갯수 ',titanic_df.isnull().sum().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "print(' Sex 값 분포 :\\n',titanic_df['Sex'].value_counts())\n",
        "print('\\n Cabin 값 분포 :\\n',titanic_df['Cabin'].value_counts())\n",
        "print('\\n Embarked 값 분포 :\\n',titanic_df['Embarked'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df['Cabin'] = titanic_df['Cabin'].str[:1]\n",
        "print(titanic_df['Cabin'].head(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "titanic_df.groupby(['Sex','Survived'])['Survived'].count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "sns.barplot(x='Sex', y = 'Survived', data=titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "sns.barplot(x='Pclass', y='Survived', hue='Sex', data=titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 입력 age에 따라 구분값을 반환하는 함수 설정. DataFrame의 apply lambda식에 사용. \n",
        "def get_category(age):\n",
        "    cat = ''\n",
        "    if age <= -1: cat = 'Unknown'\n",
        "    elif age <= 5: cat = 'Baby'\n",
        "    elif age <= 12: cat = 'Child'\n",
        "    elif age <= 18: cat = 'Teenager'\n",
        "    elif age <= 25: cat = 'Student'\n",
        "    elif age <= 35: cat = 'Young Adult'\n",
        "    elif age <= 60: cat = 'Adult'\n",
        "    else : cat = 'Elderly'\n",
        "    \n",
        "    return cat\n",
        "\n",
        "# 막대그래프의 크기 figure를 더 크게 설정 \n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "#X축의 값을 순차적으로 표시하기 위한 설정 \n",
        "group_names = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Elderly']\n",
        "\n",
        "# lambda 식에 위에서 생성한 get_category( ) 함수를 반환값으로 지정. \n",
        "# get_category(X)는 입력값으로 'Age' 컬럼값을 받아서 해당하는 cat 반환\n",
        "titanic_df['Age_cat'] = titanic_df['Age'].apply(lambda x : get_category(x))\n",
        "sns.barplot(x='Age_cat', y = 'Survived', hue='Sex', data=titanic_df, order=group_names)\n",
        "titanic_df.drop('Age_cat', axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "def encode_features(dataDF):\n",
        "    features = ['Cabin', 'Sex', 'Embarked']\n",
        "    for feature in features:\n",
        "        le = preprocessing.LabelEncoder()\n",
        "        le = le.fit(dataDF[feature])\n",
        "        dataDF[feature] = le.transform(dataDF[feature])\n",
        "        \n",
        "    return dataDF\n",
        "\n",
        "titanic_df = encode_features(titanic_df)\n",
        "titanic_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Null 처리 함수\n",
        "def fillna(df):\n",
        "    df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
        "    df['Cabin'].fillna('N', inplace=True)\n",
        "    df['Embarked'].fillna('N', inplace=True)\n",
        "    df['Fare'].fillna(0, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 머신러닝 알고리즘에 불필요한 피처 제거\n",
        "def drop_features(df):\n",
        "    df.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 레이블 인코딩 수행.\n",
        "def format_features(df):\n",
        "    df['Cabin'] = df['Cabin'].str[:1]\n",
        "    features = ['Cabin', 'Sex', 'Embarked']\n",
        "    for feature in features:\n",
        "        le = LabelEncoder()\n",
        "        le = le.fit(df[feature])\n",
        "        df[feature] = le.transform(df[feature])\n",
        "    return df\n",
        "\n",
        "# 앞에서 설정한 데이터 전처리 함수 호출\n",
        "def transform_features(df):\n",
        "    df = fillna(df)\n",
        "    df = drop_features(df)\n",
        "    df = format_features(df)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 원본 데이터를 재로딩 하고, feature데이터 셋과 Label 데이터 셋 추출. \n",
        "titanic_df = pd.read_csv('./titanic_train.csv')\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df= titanic_df.drop('Survived',axis=1)\n",
        "\n",
        "X_titanic_df = transform_features(X_titanic_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test=train_test_split(X_titanic_df, y_titanic_df, \\\n",
        "                                                  test_size=0.2, random_state=11)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 결정트리, Random Forest, 로지스틱 회귀를 위한 사이킷런 Classifier 클래스 생성\n",
        "dt_clf = DecisionTreeClassifier(random_state=11)\n",
        "rf_clf = RandomForestClassifier(random_state=11)\n",
        "lr_clf = LogisticRegression(solver='liblinear')\n",
        "\n",
        "# DecisionTreeClassifier 학습/예측/평가\n",
        "dt_clf.fit(X_train , y_train)\n",
        "dt_pred = dt_clf.predict(X_test)\n",
        "print('DecisionTreeClassifier 정확도: {0:.4f}'.format(accuracy_score(y_test, dt_pred)))\n",
        "\n",
        "# RandomForestClassifier 학습/예측/평가\n",
        "rf_clf.fit(X_train , y_train)\n",
        "rf_pred = rf_clf.predict(X_test)\n",
        "print('RandomForestClassifier 정확도:{0:.4f}'.format(accuracy_score(y_test, rf_pred)))\n",
        "\n",
        "# LogisticRegression 학습/예측/평가\n",
        "lr_clf.fit(X_train , y_train)\n",
        "lr_pred = lr_clf.predict(X_test)\n",
        "print('LogisticRegression 정확도: {0:.4f}'.format(accuracy_score(y_test, lr_pred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import KFold\n",
        "\n",
        "def exec_kfold(clf, folds=5):\n",
        "    # 폴드 세트를 5개인 KFold객체를 생성, 폴드 수만큼 예측결과 저장을 위한  리스트 객체 생성.\n",
        "    kfold = KFold(n_splits=folds)\n",
        "    scores = []\n",
        "    \n",
        "    # KFold 교차 검증 수행. \n",
        "    for iter_count , (train_index, test_index) in enumerate(kfold.split(X_titanic_df)):\n",
        "        # X_titanic_df 데이터에서 교차 검증별로 학습과 검증 데이터를 가리키는 index 생성\n",
        "        X_train, X_test = X_titanic_df.values[train_index], X_titanic_df.values[test_index]\n",
        "        y_train, y_test = y_titanic_df.values[train_index], y_titanic_df.values[test_index]\n",
        "        \n",
        "        # Classifier 학습, 예측, 정확도 계산 \n",
        "        clf.fit(X_train, y_train) \n",
        "        predictions = clf.predict(X_test)\n",
        "        accuracy = accuracy_score(y_test, predictions)\n",
        "        scores.append(accuracy)\n",
        "        print(\"교차 검증 {0} 정확도: {1:.4f}\".format(iter_count, accuracy))     \n",
        "    \n",
        "    # 5개 fold에서의 평균 정확도 계산. \n",
        "    mean_score = np.mean(scores)\n",
        "    print(\"평균 정확도: {0:.4f}\".format(mean_score)) \n",
        "# exec_kfold 호출\n",
        "exec_kfold(dt_clf , folds=5) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters = {'max_depth':[2,3,5,10],\n",
        "             'min_samples_split':[2,3,5], 'min_samples_leaf':[1,5,8]}\n",
        "\n",
        "grid_dclf = GridSearchCV(dt_clf , param_grid=parameters , scoring='accuracy' , cv=5)\n",
        "grid_dclf.fit(X_train , y_train)\n",
        "\n",
        "print('GridSearchCV 최적 하이퍼 파라미터 :',grid_dclf.best_params_)\n",
        "print('GridSearchCV 최고 정확도: {0:.4f}'.format(grid_dclf.best_score_))\n",
        "best_dclf = grid_dclf.best_estimator_\n",
        "\n",
        "# GridSearchCV의 최적 하이퍼 파라미터로 학습된 Estimator로 예측 및 평가 수행. \n",
        "dpredictions = best_dclf.predict(X_test)\n",
        "accuracy = accuracy_score(y_test , dpredictions)\n",
        "print('테스트 세트에서의 DecisionTreeClassifier 정확도 : {0:.4f}'.format(accuracy))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.base import BaseEstimator\n",
        "\n",
        "class MyDummyClassifier(BaseEstimator):\n",
        "    # fit( ) 메소드는 아무것도 학습하지 않음. \n",
        "    def fit(self, X , y=None):\n",
        "        pass\n",
        "    \n",
        "    # predict( ) 메소드는 단순히 Sex feature가 1 이면 0 , 그렇지 않으면 1 로 예측함. \n",
        "    def predict(self, X):\n",
        "        pred = np.zeros( ( X.shape[0], 1 ))\n",
        "        for i in range (X.shape[0]) :\n",
        "            if X['Sex'].iloc[i] == 1:\n",
        "                pred[i] = 0\n",
        "            else :\n",
        "                pred[i] = 1\n",
        "        \n",
        "        return pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "# Null 처리 함수\n",
        "def fillna(df):\n",
        "    df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
        "    df['Cabin'].fillna('N', inplace=True)\n",
        "    df['Embarked'].fillna('N', inplace=True)\n",
        "    df['Fare'].fillna(0, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 머신러닝 알고리즘에 불필요한 피처 제거\n",
        "def drop_features(df):\n",
        "    df.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)\n",
        "    return df\n",
        "\n",
        "# 레이블 인코딩 수행.\n",
        "def format_features(df):\n",
        "    df['Cabin'] = df['Cabin'].str[:1]\n",
        "    features = ['Cabin', 'Sex', 'Embarked']\n",
        "    for feature in features:\n",
        "        le = LabelEncoder()\n",
        "        le = le.fit(df[feature])\n",
        "        df[feature] = le.transform(df[feature])\n",
        "    return df\n",
        "\n",
        "# 앞에서 설정한 데이터 전처리 함수 호출\n",
        "def transform_features(df):\n",
        "    df = fillna(df)\n",
        "    df = drop_features(df)\n",
        "    df = format_features(df)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# 원본 데이터를 재로딩, 데이터 가공, 학습 데이터/테스트 데이터 분할.\n",
        "titanic_df = pd.read_csv('./titanic_train.csv')\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df= titanic_df.drop('Survived', axis=1)\n",
        "X_titanic_df = transform_features(X_titanic_df)\n",
        "X_train, X_test, y_train, y_test=train_test_split(X_titanic_df, y_titanic_df,\n",
        "                                                  test_size=0.2, random_state=0)\n",
        "\n",
        "# 위에서 생성한 Dummy Classifier를 이용해 학습/예측/평가 수행.\n",
        "myclf = MyDummyClassifier()\n",
        "myclf.fit(X_train, y_train)\n",
        "\n",
        "mypredictions = myclf.predict(X_test)\n",
        "print('Dummy Classifier의 정확도는: {0:.4f}'.format(accuracy_score(y_test, mypredictions)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import load_digits\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.base import BaseEstimator\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "class MyFakeClassifier(BaseEstimator):\n",
        "    def fit(self,X,y):\n",
        "        pass\n",
        "    \n",
        "    # 입력값으로 들어오는 X 데이터 셋의 크기만큼 모두 0값으로 만들어서 반환\n",
        "    def predict(self,X):\n",
        "        return np.zeros( (len(X), 1) , dtype=bool)\n",
        "\n",
        "# 사이킷런의 내장 데이터 셋인 load_digits( )를 이용하여 MNIST 데이터 로딩\n",
        "digits = load_digits()\n",
        "\n",
        "# digit 번호가 7번이며 True이고 이를 astype(int)로 1로 변환, 7번이 아니면 False이고 0으로 변환\n",
        "y = (digits.target == 7).astype(int)\n",
        "X_train, X_test, y_train, y_test = train_test_split( digits.data, y, random_state=11)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# 불균형한 레이블 데이터 분포도 확인. \n",
        "print('레이블 테스트 세트 크기 :', y_test.shape)\n",
        "print('테스트 세트 레이블 0 과 1의 분포도')\n",
        "print(pd.Series(y_test).value_counts())\n",
        "\n",
        "# Dummy Classifier로 학습/예측/정확도 평가\n",
        "fakeclf = MyFakeClassifier()\n",
        "fakeclf.fit(X_train , y_train)\n",
        "fakepred = fakeclf.predict(X_test)\n",
        "print('모든 예측을 0으로 하여도 정확도는:{:.3f}'.format(accuracy_score(y_test , fakepred)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "confusion_matrix(y_test, fakepred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n",
        "\n",
        "def get_clf_eval(y_test, pred):\n",
        "    confusion = confusion_matrix( y_test, pred)\n",
        "    accuracy = accuracy_score(y_test, pred)\n",
        "    precision = precision_score(y_test, pred)\n",
        "    recall = recall_score(y_test, pred)\n",
        "    print('오차 행렬')\n",
        "    print(confusion)\n",
        "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f}'.format(accuracy, precision, recall))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# 원본 데이터를 재로딩, 데이터 가공, 학습데이터/테스트 데이터 분할. \n",
        "titanic_df = pd.read_csv('./titanic_train.csv')\n",
        "y_titanic_df = titanic_df['Survived']\n",
        "X_titanic_df= titanic_df.drop('Survived', axis=1)\n",
        "X_titanic_df = transform_features(X_titanic_df)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_titanic_df, y_titanic_df, \\\n",
        "                                                    test_size=0.20, random_state=11)\n",
        "\n",
        "lr_clf = LogisticRegression(solver='liblinear')\n",
        "\n",
        "lr_clf.fit(X_train , y_train)\n",
        "pred = lr_clf.predict(X_test)\n",
        "get_clf_eval(y_test , pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "pred_proba = lr_clf.predict_proba(X_test)\n",
        "pred  = lr_clf.predict(X_test)\n",
        "print('pred_proba()결과 Shape : {0}'.format(pred_proba.shape))\n",
        "print('pred_proba array에서 앞 3개만 샘플로 추출 \\n:', pred_proba[:3])\n",
        "\n",
        "# 예측 확률 array 와 예측 결과값 array 를 concatenate 하여 예측 확률과 결과값을 한눈에 확인\n",
        "pred_proba_result = np.concatenate([pred_proba , pred.reshape(-1,1)],axis=1)\n",
        "print('두개의 class 중에서 더 큰 확률을 클래스 값으로 예측 \\n',pred_proba_result[:3])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[0. 0. 1.]\n",
            " [1. 0. 0.]\n",
            " [0. 0. 1.]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "X = [[ 1, -1, 2],\n",
        "     [ 2, 0, 0],\n",
        "     [0, 1.1, 1.2]]\n",
        "\n",
        "#X의 개별 원소들이 threshold값보다 같거나 작으면 0을, 크면 1을 반환\n",
        "binarizer = Binarizer(threshold=1.1)\n",
        "print(binarizer.fit_transform(X))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "오차 행렬\n",
            "[[90 10]\n",
            " [21 33]]\n",
            "정확도: 0.7987, 정밀도: 0.7674, 재현율: 0.6111\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import Binarizer\n",
        "\n",
        "#Binarizer의 thresold 설정값. 분류 결정 임곗값임.\n",
        "custom_threshold = 0.5\n",
        "\n",
        "#predict_proba() 반환값의 두 번째 칼럼, 즉 Positive 클래스 칼럼 하나만 추출해 Binarizer를 적용\n",
        "pred_proba_1 = pred_proba[:, 1].reshape(-1, 1)\n",
        "\n",
        "binarizer = Binarizer(threshold=custom_threshold).fit(pred_proba_1)\n",
        "custom_predict = binarizer.transform(pred_proba_1)\n",
        "\n",
        "get_clf_eval(y_test, custom_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#Binarizer의 threshold 설정값을 0.4로 설정. 즉 분류 결정 임곗값을 0.5에서 0.4로 낮춤\n",
        "custom_threshold = 0.4\n",
        "pred_proba_1 = pred_proba[:, -1].reshape(-1,1)\n",
        "binarizer = Binarizer(threshold=custom_threshold).fit(pred_proba_1)\n",
        "custom_predict = binarizer.transform(pred_proba_1)\n",
        "\n",
        "get_clf_eval(y_test, custom_predict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "임곗값:  0.4\n",
            "오차 행렬\n",
            "[[80 20]\n",
            " [17 37]]\n",
            "정확도: 0.7597, 정밀도: 0.6491, 재현율: 0.6852\n",
            "임곗값:  0.45\n",
            "오차 행렬\n",
            "[[85 15]\n",
            " [18 36]]\n",
            "정확도: 0.7857, 정밀도: 0.7059, 재현율: 0.6667\n",
            "임곗값:  0.5\n",
            "오차 행렬\n",
            "[[90 10]\n",
            " [21 33]]\n",
            "정확도: 0.7987, 정밀도: 0.7674, 재현율: 0.6111\n",
            "임곗값:  0.55\n",
            "오차 행렬\n",
            "[[91  9]\n",
            " [23 31]]\n",
            "정확도: 0.7922, 정밀도: 0.7750, 재현율: 0.5741\n",
            "임곗값:  0.6\n",
            "오차 행렬\n",
            "[[91  9]\n",
            " [26 28]]\n",
            "정확도: 0.7727, 정밀도: 0.7568, 재현율: 0.5185\n"
          ]
        }
      ],
      "source": [
        "#테스트를 수행할 모든 임곗값을 리스트 객체로 저장\n",
        "thresholds = [0.4, 0.45, 0.50, 0.55, 0.60]\n",
        "\n",
        "def get_eval_by_threshold(y_test, pred_proba_c1, thresholds):\n",
        "    #thresholds list객체 내의 값을 차례로 iteration하면서 Evaluation 수행\n",
        "    for custom_threshold in thresholds:\n",
        "        binarizer = Binarizer(threshold=custom_threshold).fit(pred_proba_c1)\n",
        "        custom_predict = binarizer.transform(pred_proba_c1)\n",
        "        print('임곗값: ', custom_threshold)\n",
        "        get_clf_eval(y_test, custom_predict)\n",
        "\n",
        "get_eval_by_threshold(y_test, pred_proba[:, 1].reshape(-1, 1), thresholds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# 레이블 값이 1일때의 예측 확률을 추출 \n",
        "pred_proba_class1 = lr_clf.predict_proba(X_test)[:, 1] \n",
        "\n",
        "# 실제값 데이터 셋과 레이블 값이 1일 때의 예측 확률을 precision_recall_curve 인자로 입력 \n",
        "precisions, recalls, thresholds = precision_recall_curve(y_test, pred_proba_class1 )\n",
        "print('반환된 분류 결정 임곗값 배열의 Shape:', thresholds.shape)\n",
        "\n",
        "#반환된 임계값 배열 로우가 147건이므로 샘플로 10건만 추출하되, 임곗값을 15 Step으로 추출. \n",
        "thr_index = np.arange(0, thresholds.shape[0], 15)\n",
        "print('샘플 추출을 위한 임계값 배열의 index 10개:', thr_index)\n",
        "print('샘플용 10개의 임곗값: ', np.round(thresholds[thr_index], 2))\n",
        "\n",
        "# 15 step 단위로 추출된 임계값에 따른 정밀도와 재현율 값 \n",
        "print('샘플 임계값별 정밀도: ', np.round(precisions[thr_index], 3))\n",
        "print('샘플 임계값별 재현율: ', np.round(recalls[thr_index], 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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O22/Hjh1kZmZSVFTEkCFDTnq+6dOn88Ybb7Bjxw5yc3MpKSkhKCioWrLXJipm6zOfw//A1TMrIiJ1gaefo4fUqPd2kr//0Z7c3Nxczj//fF588cXj9mvYsCGJiYmnPNeyZcu4+uqreeqppxg+fDjBwcF8/fXXvPrqq07ncjcqZuuzsp7ZHGNziIiIuILJ5PRH/bVFt27d+O6772jatCkeHseXZ/Hx8fj6+jJ37lxuuumm415funQpTZo0KbsYDGD37t3Vmrm20Dyz9ZmPhhmIiIjUBnfeeScZGRlcddVV/P333+zYsYM//viD66+/ntLSUnx8fHj44Yd56KGHmDZtGjt27GD58uV89NFHgKPY3bNnD19//TU7duzgjTfe4IcffjC4VTVDPbP1mXew4z5rLzwV5rrzevrBpR9C63Ncd04REZE6rFGjRixZsoSHH36Ys88+m6KiIpo0acI555yD2ezoe3ziiSfw8PBgwoQJHDhwgIYNG3LbbbcBcMEFF3Dfffcxbtw4ioqKOPfcc3niiSeYOHGiga2qGSpm67OQOAhpApm7wV7quvMW5zgWfIgfBmbL6fcXERGpZ+bPn3/ctvj4eL7//vuTHmM2m3nsscfKDSU41ksvvVQ2O8IR9957b9njiRMnlitup06d6kzkWkvFbH3m4Q13rYL8g647Z0khvDcADm6DjT9Ax8tcd24REam0klIbszYm89fmVPq3asBFXWOMjiTiEipm6zuLJwRGu/acvcfBvGdhwUvQ/mL1zoqI1AIZecWM+3I1AMsSD+JpMTOiQzRmc+WXSxepDXQBmLher1vAJxjSt8Cmn4xOIyIiQInNXvY4KauQO7/8l5FvLGLWhmTsdvspjhSp3VTMiuv5BMOZdzgeL3gJbDZj84iICKXHFLN3D4kn0NuDhOQcbvt8FRN/3ljudRF3omJWqkev2xzz2KZthoRfjE4jIlLvHemZDfTxYPywVix+eDC3DWgBwKfLdvPR4lNPyi9SW2nMrFQP3xBHQbvwJUfvbHBsjb21qaSEkLxETAf+hRNMPH38ARaIbAceXtUfTkTEICWljk/JPA6PkQ328+SREW3oGBPMZ8t3MaZ3UwPTVY2GSbgnV33fVMxK9Tnzdlj+LqRsgA8G19jbegADALY6cVDHUXDpB9UTSESkFjjSM2sxl/9Q9txODRnZMRqT6eiFYKU2OxY3uDDM09MTgPz8fHx9fQ1OI84qLi4GwGKp2oXiKmal+viFwfDnYPGkGh03a8dOQUEBvr6+mDjdD2O7Y9GITT/BeZPAO7BGMoqI1LQjY2I9TlCkHilk7XY7k+dsJbuwhIkXtK/RfJVhsVgICQkhNTUVAD8/v3JFeW1gs9koLi6msLCwbPGDuqyi7bXZbKSlpeHn53fC5XudoWJWqlf3sY5bDSqxWpkzcyYjR44s+6v9pOx2eKsHHNwOW//QvLgi4lKfLt1FSnYh95/d2vCezvAAL27t3xx/75P/6l+64yBv/LWdJuF+TKT2F7MA0dGO6SWPFLS1jd1+TAdLLSu0q4Mz7TWbzTRu3LjKXxcVs1K/mUzQ9gJH7/Gmn1TMiojLpGQX8uTPGwHILSrhkRFt8PMy7tduw2BfHh3Z9pT7NG/gD8D+QwUUlZTi7VH75wk3mUw0bNiQyMhIrFar0XGOY7VaWbhwIf379z99B0sd4Ex7vby8XNJbrWJWpN2FjmJ22xwozgMvf6MTiUgdMHtjctnjact2M7x9NH1aRgCwNyOfQmspjUO8jYp3QlGBPnh5mCkusdHj2T/pH9+AQW0iGdi6AREBtSvrf1ksliqPvawOFouFkpISfHx86kUxa0R7VcyKNOwMIU0gczds/9NR3IqIVNHvGxzFbKifJwE+HnSKDS577bPlu3l/YSKBPh5EelqYm7ee2DA/GoX4EhPiS+8W4fh4urYwKyguJT23CG9PM5GBPifcx2w2cf+wVry3MJGMvGJ+W5/Eb+uTuHtIPOOHtQIcY2/NJurFR+biHlTMiphMjgJ26RuOoQYqZkWkijLyilmxMwOAn+7sS+Nwv3KvF5fY8PW0kFNYQk6hiR3rksq9vmbCsLJi9p352/l7ZwaNQnwP33xoFOzrKH6DfSpcVK7clcHYj1fSrmEQM+/pd9L9bh3Qgpv6NWftvkzmJaTyV0IqQ9pElr0+Z1MyT/68kUGtIxnUJpL+8Q3w9ap9PaJSf6iYFYGjxezWPxzTiR2ZBcFkguYDoUFrI9OJiJvZnppLoI8HjYJ9jytkASZe0J7Hz23Lhn2H+OHPJUQ1b0NKdjH7Mws5mFdEsO/Rj2f/3Z3JvC1pJ3yfAG8PFjw4kPDDQwD2ZuTj52Upe36s0sOzynhYTl/8WswmujUOpVvjUO4/u/zPvwVb00nJLuLrv/fy9d97aR0VyKx7+6mnVgyjYlYEIKY7BMVC9j6Y9Uj51wKi4K5/wTvAmGwi4nZ6Ngvj78eGkpJdeNJ9PCxm2jcKYneEnZF9m510fOHtA5szpG0kSZkF7M8s5EBmAUlZBezPLMBsgjD/owu+PPPrJmZvSiEiwIvW0YG0igqkTXQgw9tHYy09+dRczph4QTtGdIjmr4RUvlixmy0pOexIy6NlpH5GijFUzIqAowf2ordh9edgP2ZO3F1LIDcZlr0FAx85+fEiIv/haTETG3p8r6yzujcJo3uTsOO2W0ttJGcVlusRzS0qASA9t5j07QdZsv0gAG/P28HtAx1L13pU8epxbw8L/Vs1oH+rBiQkZ7M8MYMVOw+qmBXDqJgVOaL5QMftWBu+h2+vhyVvQPfrITDKiGQi4kayC60EentU+8funhYzcWHli+Uvbz6TvKIStqXmsjU5hy0pOfy2Lok9Gfk8/uMGAJfOd3tm83CWJ2awPDGDq3s1cdl5RZxR95eiEKmK9hc7hiBY82D+C0anERE38PC36+j74jz+Skgx5P39vT3oEhfCqDPieOK8dnw4tgfdGodw9+B4oGJjZiuqV7NwANpEa/VEMY6KWZFTMZng7Gcdj/+dBmlbjM0jIrVaQXEp87eksT+zgAYBJ57+qqZ1iAnmu9vPolGII09Vx8we64ymoUy+ojN3DmrpsnOKOEvFrMjpNDkLWp8L9lL4c6LRaUSkFluwNY0CaykxIb50iAkyOk4Zk8lE8wYBXHtmEwYdM81WVXlYzFzcNbbseU6hlU0Hsl12fpGK0JhZkYoYOhG2zoItMx0XhTXtY3QiEamFZm1wzBd7TofoWjdVVfcmoXRvElpt588tKuG6T/5ma3IO027sSdfG1fdeIsdSMStSEQ1aQfex8M/H8OPtENvj+H0iWsOAhxxDE0Sk3ikusTF3cyoAIzpEG5ym5plNjiEMOUUlXPvRSj4c24Mzm4cbHUvqARWzIhU14BFYN8Ox7G3m7hPv02o4NOpSo7FEpHZYsiOdnKISGgR6060e9kr6eXnwyfVncMPUvx2zG3y4gkdHtOHGvs1qXS+11C0qZkUqKjAKrp8Ju5cd/9rfH8LBbZC+TcWsSD31x4ZkAIa3j8Lswous3ImflwefXNeTR75fx09rDvDsb5v5d88hXrqsMwHeKjmkeuhflogzGnZ23P4rZYOjmD24veYziUitcFHXGHy9LJzXqaHRUQzl62XhtSu60L1JKM/8uomZ65Ox2WDKtd2NjiZ1lIpZEVcIPzwtTcYOY3OIiGHObB6uMaKHmUwmxvRuSoeYYB75bh0PndPa6EhSh2lqLhFXCHcsE6meWRGRo7o1DmXWPf1p3uDoUrcJmSaKS2ynOErEOSpmRVzhSM/swR1gtxubRURq3GfLd7N0e7qKtBM4dvzwssSDTNlsZuzUfyi0lhqYSuoSFbMirhDaDDBBUTbkpRudRkRqUE6hlad+3sjoD1eQlFVgdJxarbjEho8F/tmdyZcr9hgdR+oIjZkVcQVPHwiJg8w9MKUvWLyO38fiCWc/A23Orfl8IlJtlu04SInNTtNwP5qE+xsdp1Yb0KoBFzSxMT3RwtvztnPFGXH4a5YDqSL1zIq4SpPDq4LlJkPWnuNvGTvgr2c1DEGkjlmwNQ1wFGpyer0a2GkS5sfBvGI+WbLT6DhSB+jPIRFXueAt6HUb2E8wDqykGKZdCKmbIGmt5qIVqSPsdvvRYra1itmKsJjhniEtGP/Net5bmMg1ZzYhxO8En2aJVJB6ZkVcxeLhKFJjuh9/a9L76PCCtV8ZGlNEXCcxPY99hwrwspg1LZcTzu0QTZvoQHIKS3hvYaLRccTNqZgVqSldRjvu13/j6KkVEbe3YIujV7ZnszD8vPRhZ0WZzSYeOLs1Yf5eNArxNTqOuDn9zxOpKc0HQUAU5KbA9jm6EEykDvh3zyFA42UrY0jbSBY9NEgXgEmVqWdWpKZYPKDTKMfjNV8am0VEXOKNK7vy2919ubBrI6OjuB2TyVSukM0vLjEwjbgzFbMiNanz4aEGW/+AtK2Qk3z8TUMQRNyG2WyifaNgIgN9jI7i1uZtSaXfi/PKerpFnKG+fZGaFNUOGnZ2zGjw9hkn3iewEdz1D3hpvkoRqfvsdjtfLN/Nwbxirv/kb765rTetogKNjiVuRD2zIjWt3/3gFQAmy/E3gJwDkL7N2IwiclofLkrk9s9XMS8h1egobs1kMvHGVV3p2jiErAIr1360gr0Z+UbHEjeinlmRmtbuQsftRN7tCynrIV9L4orUdmv2ZvL7hmR6NgszOorb8/Py4JPrzmDUe8vYmpLLmI9X8s1tvYkI8DY6mrgB9cyK1Cb+h+epzDtobA4ROa0ji/mZTSZjg9QRIX5eTLuhFzEhvuxMz2PsxyvJKbQaHUvcgIpZkdrE73Axq55ZkVrPjqOaNauWdZnoYB8+v6kX4f5ebDyQzceLdxkdSdyAhhmI1CZ+EY77PBWzIrWdzXb4gXpmXapZhD+f3tCTb1ft485BLYyOI25AxaxIbeJ/uJhVz6xIrWezq2e2unSICaZDTHDZc/vhr7VJfzjICaiYFalNjgwzSNkEm34++X6xZ0BQw5rJJCInZNOY2RpRUmrj/35YT6i/F4+OaGt0HKmFVMyK1CYBkY77/f/AjGtPvp9XAFw/0zFnrYgYoqy30OAcdd2yxIPM+GcfAPGRgVzWPdbgRFLbqJgVqU1aDIb2F0N20sn3yUmCzN3wxeVw05/grx5aESO8e013bHY7Fo0zqFb94htw79B4XvtzGxN+2kD3JqE0i9CiMnKUilmR2sTLHy6feup9CrPg43MgdRN8fhmM+a1GoolIeV4emhCoptw1OJ7liQdZnpjB3V+t5rvbz9LXX8roX4KIu/EJhqu/cSx7m74Fy7djMNs0F6OI1F0Ws4nXruhKiJ8n6/dn8crsLUZHklpExayIOwqOdRS0XoGY9yyl6+4PwG47/XEi4jLvzt/B+OlrWLU7w+go9UJ0sA8vXdoJgPcXJrJwa5rBiaS2UDEr4q6iO8AV07CbPYjNXI553jNGJxKpV2auT+L71fvZd6jA6Cj1xtntoxnTuwlBPh4Ul+gPeHFQMSvizloMpvTc1wCwLHsTVn5gbB6ReuLfPYdYvz8LD7OJM5qGGR2nXvm/kW2ZdW9/hraLMjqK1BK6AEzEzdk7Xcnmf/6ibdL38PtDkJYAnr6nPzCwEfS6FcyW6g8pUse8M28HABd3jaFRSAX+v4nL+Hhayn3Ni0pK8fbQz7H6TMWsSB2wNepCWkf5YV7zOfz9oRNH2qH3ndWWS6Qu2pKcw5+bUzCZ4LaBWm7VSH8lpPB/32/gw7E9yq0YJvWLilmRusBkonT4y5hjusGhnaffPycZ1n8Dfz0H7S50XFAmIhXy7vztAIzoEE2LBgEGp6nfZvy9j+TsQu76ajW/3tUXf2+VNfWR4d/1t99+m5dffpnk5GQ6d+7Mm2++Sc+ePU+6/xdffMFLL73Etm3bCA4OZsSIEbz88suEh4fXYGqRWsjiCWfcWLF9bTbI3At7l8PvD8OVX1RvNpE6Ys/BfH5Z51jU5I6BLQ1OI/+7tCNr92WyMz2PiT9v5OXLtSpifWToBWDTp09n/PjxPPnkk/z777907tyZ4cOHk5qaesL9lyxZwpgxY7jxxhvZuHEj33zzDStXruTmm2+u4eQibs5shvMmg9kDEn6FLb8bnUjELTQI9GbCee24okecPtauBUL8vHjtii6YTfDNqn38tGa/0ZHEAIYWs5MmTeLmm2/m+uuvp127dkyZMgU/Pz8+/vjjE+6/bNkymjZtyt13302zZs3o27cvt956KytXrqzh5CJ1QFQ76D3O8Xjmg1CcZ2weETfg62Vh7FlNefGyTkZHkcN6NQ9n3OB4AB7/YQN7DuYbnEhqmmHFbHFxMatWrWLo0KFHw5jNDB06lGXLlp3wmN69e7N3715mzpyJ3W4nJSWFb7/9lpEjR9ZUbJG6ZcBDENwYsvbC/P8ZnUbkpPKKSvhnVwZ2u73G3jOn0MrWlJwaez+pvLsHt6RHk1Byikq4++vVWEs1B219YtiY2fT0dEpLS4mKKj9PXFRUFAkJCSc8pk+fPnzxxRdcccUVFBYWUlJSwvnnn8/bb7990vcpKiqiqKio7Hl2djYAVqsVq7V+LAF6pJ1qb91UpfaavDAN/x8eM0ZjX/Y2Je0vg8h2Lk7oWvr+1m0na+/jP27ABHRqFIDJZKqRLC/M3MSXK/fx7IXtOKd9FDdO+5dresVxfqeGWMyuyaDvr+u8clkHLnh7GfGR/hQWFYNn7ZiuS9/jqp2nIkz2mvwz9xgHDhwgJiaGpUuX0rt377LtDz30EAsWLGDFihXHHbNp0yaGDh3Kfffdx/Dhw0lKSuLBBx/kjDPO4KOPPjrh+0ycOJGnnnrquO1ffvklfn5+rmuQiBs7I/ENGmX9Q4FnKPleERU+zmbyJKHhxWQEtK7GdFLf2ezw+D8Won3hwialNAl0bHNRPXlSu3Ng0gYPzCY77ULsbDhkppGfnYc6lVJD9bQ4KasYgr2MTiGukJ+fz+jRo8nKyiIoKOiU+xpWzBYXF+Pn58e3337LRRddVLZ97NixZGZm8tNPPx13zLXXXkthYSHffPNN2bbFixfTr18/Dhw4QMOGDY875kQ9s3FxcSQlJdWbGRCsVitz5sxh2LBheHp6Gh2n2qm9lZB9AI/3+2Aqcv4jVVv8OZSO+rxy71sJ+v7WbSdq76akbC58Zzn+Xhb+/r9BWEwmbv9yDbGhvtzYp0m1LVpgt9sZ/816fl2fXLZt8uUdOa/T8b9rKkvf3+pjt9ux28Fc3X/1nIa+x5WTnZ1NREREhYpZw4YZeHl50b17d+bOnVtWzNpsNubOncu4ceNOeEx+fj4eHuUjWyyOjxFOVpN7e3vj7e193HZPT8968Y/qWPWtzWqvE8KbwK0LIXlDxY9J3Qzzn8ecuQuzAV9nfX/rtmPbu3JXFgA9m4Xh5+PNnE0p/LUlDYAvV+7lwi4x3DagOfFRgVV+35/W7GfRtnSevagDPl4WXhnVhQNZy/l3TyZNwv04v0ssHhbXX25Sn7+/1eFAZgGP/bCefvENuKFvs2p7H2foe+z88RVl6Dyz48ePZ+zYsfTo0YOePXvy2muvkZeXx/XXXw/Ao48+yv79+5k2bRoA559/PjfffDPvvvtu2TCDe++9l549e9KoUSMjmyLi/sKaO24VFd0R5j8PGTsd89aaDZ0cReqwpTvSATirhWMIzNC2kUy7oSdTFuxg6Y6DfPfvPr77dx9D20Zx24DmdG8SWqlxtRv2Z/HQt+soKrHRKTaYMb2b4uNp4YMxPXhr3nbO79yoWgpZcb35W9KYtyWN5YkZDGsXRVyYhhXWZYYWs1dccQVpaWlMmDCB5ORkunTpwqxZs8ouCktKSmLPnj1l+1933XXk5OTw1ltvcf/99xMSEsLgwYN58cUXjWqCSP0VHOeYp7a0CHIOaBUxqRbWUhsrd2YAcFZLx9Awk8lE/1YN6N+qAWv2ZjJl/g7+2JTMn5tT+HNzCt/dfhbdm4Q69T4Hc4u49bNVFJXYGNi6AVf3alL2WniAN0+e3951jZJqd+UZcfy4Zj8rd2bwfz+sZ9oNPWvswkGpeYavADZu3LiTDiuYOnXqcdvuuusu7rrrrmpOJSKnZfGAkCaQsQMyElXMSrVYty+TvOJSQv08aRt9/Li5LnEhTLm2OzvScnl/QSJr9mbSrXFI2etfrdxDqJ8ng9tE4eVx4l7VklIb475czf7MApqG+/H6lV1dNluBGMNsNvG/Szoy4vVFLNqWzrer9nF5jzijY0k1MbyYFRE3Ftb8aDHbrL/RaaQOWrPXMV62d4vwU17I06JBAC9e1olSm72sB66opJQXZyWQmW8l1M+TC7vEcOuA5jQMLn/B2PMzE1iWeBB/Lwvvj+lBsG/9GddYlzVvEMC9Q1vx4qwEnv1tMwNbR9Ig8PhraMT9afCPiFTekTG2GYnG5pA668a+zVj00CDGD6vY9G/H9qgWldi48ozGRAZ6cyjfytSluxjx+iLmJRxdMv37f/fx8ZKdALw6qgutXHARmdQeN/drRvtGQWQVWJn480aj40g1Uc+siFTekWJ2yeuw9K2T7+cbAld/AzHdaySW1C2VvXgnyMeTR0a04YGzW7FoWzqT5mxl/f4srp/6N3cMbMH4Ya2ICvIhxM+Ta89swjkdol2cXIzmYTHz4qWduPDtJWw8kEVmfjEhfpqItq5RMSsildd8AHj4QEkh2EtPvl/+Qdj6h4pZcUpGXjFh/lUvPDwsZga1ieSsluE8/9tmPl22mw8X7eTCLjH0aRnBrHv6E6mPn+usDjHBfDS2B2c2D8enlqwKJq6lYlZEKi+yLTyUCKdabGHl+7DoVcjcc/J9RP4jp9DKoEmL6dksjJcv6+SS3jRvDwtPXdiBM5qFkVtYQutox5CC6GCfKp9bareBrSONjiDVSMWsiFSNl7/jdjKR7Rz3mXtrJo/UCZ8s3U1WgZWd6XkE+rj2gqzzOmle8vqq1GZn6tJdtG0YWDZvsbg/XQAmItUrpLHjXj2zUkF5Vvh46W4A7hvaStNkict8sCiRZ37dxCPfraeg+BRDo8StqJgVkeoVfHhux+z9UFpibBZxC38dMJNXVErbhkGM0EVZ4kJX92pMw2Af9mTkM2nOFqPjiIuomBWR6hUQBRYvxwVi2fuNTiOnselANhl5xS4/b2pOIdd8uII7v/yXn9bsJ6vAesL90nOLWJjs6IkdP6zVKeeWFXFWoI8nz13cAYCPFu9k7d5MYwOJS6iYFZHqZTYf7Z3N0rjZ2mzN3kxGvrGIy6csdfm5v121j8Xb0/ltXRL3fL2G7s/M4ZoPVzBt2S4OZBaU7ff+ol0U20x0igliaFtdtCOuN7hNFBd2aYTNDg9/t47iEpvRkaSKVMyKSPU7Mm720C5DY8ipTVu2C4AdaXkuP/ftA1rw/MUd6RcfQcvIAEpsdhZvT2fCTxs5639/8c+uDKylNuYeXtDg3iEty1byEnG1Cee1I8zfi4TkHN5bsMPoOFJFKmZFpPpFtHLcp242Noec0vp9WWWPraWu7a0ymUyM7tWYz27sxZ/jB/DX/QN4dEQbejQJJcjHg06xIXhazPw27iyubVlK35bhLn1/kWOFB3jz5PmOmVbeXbCDrPwTD3sR96BiVkSqX7RjjBrJ643NIaf02Y29yh4/8M1abDZ7lc+ZmlN4wkKheYMAbh3Qgm9vP4tljw7By8Px68jH00KPBnb1ykq1u6BzI24d0Jzvbj+LYD/XTv8mNUvFrIhUv+iOjvvk9WCveoEk1SM62IePxvbAw2zipzUHWLrjYJXP+b/fE+j70l/8vPbASffx99aU51LzTCYTj45oS9uGQUZHkSrSTxARqX4N2oLJAgUZkJMEQZq0vrYa0jaKyVd0Ia+ohL7xjknl03KKGPvxSlpFBRAfFUh8pOO+cZjfKeeA3ZGWy4+r92OzQ5Mwv5pqgkilZOVb8fEy4+2hJW/djYpZEal+nj6OcbNpmx29sypma533FuzgYF4xl3SL4fzO5b8/21Jy2JSUzaak7HLbvTzMtGgQwE19m3Fp91jAscKS3W7Hw2LmjbnbsNlhaNsoOseF1FRTRJw2fsYafl5zgPeu7c6QtlFGxxEnqZgVkZoR3fFwMbsOWg03Oo38x7er9rEtNZceTUJpE13+Y9d2jYL4YEwPtqbksD01t+y+qMTG5qRsio+5WGzN3kNc9f4KmjfwZ0tKDgD3Do2v0baIOCvA24MSm50/N6eomHVDKmZFpGZEd4T1M3QRWC2071A+21JzMZugR9Ow414P8fNiWLsohrU7+ku+1GZ3HJeSS7tGR4vfbSm5FJfaSEh2FLLntI+mQ0xw9TdCpAqGto1i2rLdzN2cis1m12IdbkbFrIjUjKj2jvvUBGNzyHH+2JgCwBlNwwjz96rQMRaziSbh/jQJ9y+3fVSPOPq0jGBbag5JWYWc27Ghy/OKuFqv5mH4e1lIzSli/f4sDYtxMypmRaRm+DsuJqIo+9T7SY2btSEJgBEdoqt8LrPZRFyYH3G64EvciLeHhf6tGvD7hmTmbk5RMetmNDWXiNQMz8M9eMX5xuaQclJzCvln9yEAzm5f9WJWxF0NPTxW9s/NqQYnEWepmBWRmuF1uKfO6vqlUqXy5mxKwW6HznEhNArxNTqOiGEGtYnEbIJNSdnszywwOo44QcMMRKRmeB4uZm0lUFIMHhUbmynVy2azExnozTnqlZV6Lszfi5v6NadxmB8BWsjDrei7JSI1w+uYC4WKc8Hj+KvmpeZd27spV/dqUm56LZH66v9GtjU6glSChhmISM2weIL58PrnVo2brU3MZhM+nlr1SETck4pZEak5R8bN/joevrsJts81Nk899syvm/hwUSJ2u93oKCK1SlJWAZ8t28XfuzKMjiIVpGEGIlJzgmKgMAu2/eF4nrQOxq00NlM99NXKPXy0eCcAPZuF0Sk2xNhAIrXIBwt38vGSnVzaLZYzTrCIiNQ+6pkVkZozahqMeAkG/p/jeeZuUM9gjVqeeJAnftwAwANnt1IhK/IfQ9tFAjBvSyqlNv18cgcqZkWk5kTEQ69boe99YDJDSSHkak7HmrI3I5/bP19Fic3O+Z0bceeglkZHEql1zmgaRqCPBxl5xazZe8joOFIBKmZFpOZ5eEHg4WVOs/Yam6WeyC0q4aZP/+FQvpVOscG8fFknTCatPy/yX54WMwNbO3pn52zSH9vuoErFbGFhoatyiEh9ExznuM/cY2yOeuKer1azJSWHyEBv3r+2h2YvEDmFoW0dxezczSkGJ5GKcLqYtdlsPPPMM8TExBAQEEBiYiIATzzxBB999JHLA4pIHRVyuJhVz2yNGN4+Gl9PC++P6UF0sI/RcURqtYGtIrGYTWxLzWX3Qa1aWNs5Xcw+++yzTJ06lZdeegkvr6Mr+HTo0IEPP/zQpeFEpA4Laey4z1QxWx0O5RWzYX9W2fPLe8Qy9/4BdIkLMS6UiJsI9vOkZ9MwPMwmNuzPNjqOnIbTU3NNmzaN999/nyFDhnDbbbeVbe/cuTMJCQkuDScidViwemary7yEVB76bh0eZhOz7u1PsK8nJpOJRiG+RkcTcRvPXdyBiEBvgnw8jY4ip+F0Mbt//35atjz+ClibzYbVanVJKBGpB4JiHPfZB4zNUYfkFZXw7G+b+WqlYxxyy8gADuYWEeyrX8YizmreIMDoCFJBThez7dq1Y9GiRTRp0qTc9m+//ZauXbu6LJiI1HH+4Y77Ak19c6yiklI+W7ab685qiofl9CPBNidlk11g5VC+lednbmZPhmOp4Bv7NuPB4a11oZeIC5Ta7FjMmv2jtnK6mJ0wYQJjx45l//792Gw2vv/+e7Zs2cK0adP49ddfqyOjiNRFvodX1sk/aGyOWsRut3PXl6uZvSmF9fuzmDSqy2mP+WBhIt+v3l/2PCbEl5cv78RZLSKqMalI/bBkezovzUqgWYQ/r12pDrvayuli9sILL+SXX37h6aefxt/fnwkTJtCtWzd++eUXhg0bVh0ZRaQu8jvcM2vNh+J88PIzNk8tYDKZuLR7LH8lpPLTmgNk5BVzbocoiopOfszi7elljy/rHsuE89tpjJ+Ii3h7mFm7L4ud6XlYS214VuDTEql5ThezAP369WPOnDmuziIi9Yl3IJg9wWaFgox6X8zO25LK8sSDDG8fzZtXdWXcV6tZtC2dRdvSAQ8+3bOEe4e14oLOjcod5+flGEZwS//m/N/ItgYkF6m7ujYOJczfi4y8Yv7ZdYjeLcKNjiQnoD8xRMQYJtPR3lkNNeDXtUm8tyCR2RtTGNGxIb+M68u4QS3pFBuECTuJ6XkcO2RvS3IOr/25lZRsR7ftFWfEGZRcpO6ymE0MOrwa2J9aQKHWcrpn1mw2n3IJxNLS0ioFEpF6xC8McpPh4HYIiKrYMVYr3tZMyEkGz1r+cbqHD/iGnHY3u93O8kRHQX/W4Z6fdo2CaNcoiHsGN+fbn2cS2KI7/Vo2KDtmzqZkXvtzW9nz6CAthCBSHYa2jeS7f/cxd3MKj5/bVstA10JOF7M//PBDuedWq5XVq1fz6aef8tRTT7ksmIjUA0d6Zr+9ocKHeALnAGyojkCuZsJ+6UeYOl56yr0+WryT/ZkFeHmY6dE09LjX/TxgePsoPI8p3ts1CuLcTg1ZkZhB9yYh+HtXatSYiJxGv1YN8LKY2XUwnx1pebSM1JRdtU2lLgD7r8suu4z27dszffp0brzxRpcEE5F6oN2FsO9vKK34HNV2HD2ZJpMJI/tH7Efu7WDHjv3wBpMJLCYTdrsNE3b++vULBra/5KTT+szakMxzMzcD8NDw1vh5VezH8uA2UQxuU8HebBGptABvD85sEc7CrWn8uTlFxWwt5LI/5c8880xuueUWV51OROqDnjc7bk4osVqZOXMmI0eOLNdTWVPsdjujP1jB37syKLHZy70W6O3BgFYNeGt0N6xrvsHrx5sIKtjL58t3M6Z3k+M+nly95xD3Tl+N3Q7XnNmYG/s2q8mmiEgFnd+pIYHeHrRrGGR0FDkBlxSzBQUFvPHGG8TExLjidCIihrLb7ew+mM/fuzL4Z9ch0nOL+Oi6MwDKCtISm53oIB/OaBbGGU1D6dEkjNbRgWU9sF6R8QA0NSVz+c8b+XLFHm4d0JzzOzcqm97npzUHKLTaGNS6ARPPb6+xeCK11OU94ri8hy6yrK2cLmZDQ0PL/cC12+3k5OTg5+fH559/7tJwIiI1ZUtyDku2p/P3rgz+PlzAHisjr5gwfy8AJpzfjkAfD2JCfE9egIY1B6CBKZso72K2pOQwfsZaXp29lZv7NWPsWU158vx2NIvw59LusRVa7UtERI7ndDE7efLkcj+8zWYzDRo0oFevXoSGHn/hgoiIO/hocSIz/tlX9tzLYqZTbDA9mjp6Xo/M5wrQtiIfNfoEgX8DyEvjz+vimLYrhE+WOC70WrrjINf1cQwpGHtWU1c3RUSqgd1uZ0daLjvS8hjePtroOHIMp4vZ6667rhpiiIjUnEJrKR8v2cml3WKJOjylVb/4BqTnFtOjaShnNA2jY0wwPp6W05zpNMKaQ14agXm7uXNQD27s24xvV+2jc2xI1RshIjVq44FszntzMQHeHgx6IhIvD32aUltUqJhdt25dhU/YqVOnSocREalOdrud3zck8/zMzew7VMD21FwmjeoCwPmdG3H+f1bXqrKwFrB3BRxMBMDH08I1ZzZx7XuISI1o1zCIBoHepOUUsWLnQfrFNzj9QVIjKlTMdunSBZPJhN1uP+V+JpNJiyaISK208UAWT/+yiRU7MwDHIgMDWlXzL6Nwx7hZEudBv/vBrJ4cEXdlNpsY0iaSr//ey9zNqSpma5EKFbM7d+6s7hwiItUiPbeIV2dv4eu/92K3g7eHmVsHtOC2Ac0rPKdrpbW/BBa+AruXwIp3ofed1ft+IlKtBrZ2FLMLt6UZHUWOUaGf5E2a6GMxEXFPHy/eyVcr9wKOoQSPjGhDTIhvzbx5eAsY/hz8dj/8ORGa9oOGGool4q56twjHYjaRmJbHvkP5xIb6GR1JqMI8s5s2bWLPnj0UFxeX237BBRdUOZSIiKvcNrAFGw9kM25wS85oGlbzAXrcCNvnwpaZ8N1NcMt88NIvQBF3FOzrSZe4EFbtPsTibelc2bOx0ZGEShSziYmJXHzxxaxfv77cONoj03VpzKyIGGlrSg5frtjDhPPaYTabCPLx5NMbehoXyGSCC96Ed1dB+haY8wSc+6pxeUSkSvrFRziK2e0qZmsLp69GuOeee2jWrBmpqan4+fmxceNGFi5cSI8ePZg/f341RBQRqZhvV+3jgrcWM3XpLqYu3WV0nKP8I+Cidx2P//4QtvxubB4RqbRLusYy9fozePmyzkZHkcOcLmaXLVvG008/TUREBGazGbPZTN++fXnhhRe4++67qyOjiMgpFRSX8uA3a3ngm7UUWm30i4/ggi4unmarqloOgTMPXwD2052Qk2xsHhGplMbhfgxsHYmvVxXnoRaXcXqYQWlpKYGBgQBERERw4MABWrduTZMmTdiyZYvLA4qInEpRSSmXv7eUDfuzMZvgvqGtuHNQS8zmkywza6ShT8LOhZCyHr66Cpr2OX4fr0A483bHCmIiInJaThezHTp0YO3atTRr1oxevXrx0ksv4eXlxfvvv0/z5s2rI6OIyEl9vnwPG/ZnE+LnyTtXd+OsFhFGRzo5D2+49EN4fwAc+NdxOxGbFQY/XrPZRKTCkrIKmLpkF4fyi3lJww0M53Qx+/jjj5OXlwfA008/zXnnnUe/fv0IDw9n+vTpLg8oInIyhdZS3p63HYCHz2lTuwvZIyLbwNXfwLbZx7+WuRc2/Qgbf4RBjzkuHhORWqek1M57CxOxmE08fl47gnw8jY5UrzldzA4fPrzsccuWLUlISCAjI4PQ0NCyGQ1ERGqCj6eFj8b24IsVe7i8e6zRcSquWX/H7b8Ksx1TeB3cBmkJENm25rOJyGnFhfnRPMKfxPQ8lu04yPD20UZHqtecvgDs888/L+uZPSIsLEyFrIgYomvjUF65vDMeljqwVKxPELQY7Hi86Sdjs4jIKfWLd3wStEirgRnO6Z/+9913H1FRUYwePZqZM2dqXlkRqXGZRfDNqv1Gx6gebQ8vPLPpZ2NziMgp9YtvAMCibekGJxGni9mkpCS+/vprTCYTo0aNomHDhtx5550sXbq0OvKJiJSzLPEgL6+z8H8/bmTu5hSj47he6xFg9oDUjZC+3eg0InISZ7YIx8NsYvfBfHYfzDv9AVJtnC5mPTw8OO+88/jiiy9ITU1l8uTJ7Nq1i0GDBtGiRYvqyCgigt1u553527lu6ipyS0y0iQ4kPjLQ6Fiu5xcGzQY4Hm/WUAOR2irA24NuTUIBWKjeWUM5fQHYsfz8/Bg+fDiHDh1i9+7dbN682VW5RETKZBdauX/GWuZscvTE9mxg46NbehLo52NwsmrS7gLYMRfWfQPdbzY6jYicRP/4CBLT8rCW2IyOUq9VqpjNz8/nhx9+4IsvvmDu3LnExcVx1VVX8e2337o6n4jUc6t2Z3D/jLXsOpiPl8XME+e2ITB1HT6edXj1nTbnwaxHIW0zHp+OxC98rNGJROQEburXnDsHtdRF8AZzepjBlVdeSWRkJPfddx/Nmzdn/vz5bN++nWeeeYY2bdpUR0YRqccy863sOphPTIgv39zWmyvPiK3706/6R8Do6eAXgSllPQO2TMB0onlpRcRQPp4WFbK1gNM9sxaLhRkzZjB8+HAsljrcMyIihjiQWcDWlBwGto4EYHCbSB4/ty2Xdosl1N8Lq9VqcMIa0qw/3LoQ24wxeO3/B2aMhv4PwcBHwKyfvSK1ic1m51B+MeEB3kZHqZec7pn94osvGDlypApZEXGp7EIrL85KYNAr87n7q9Vk5hcDYDKZuKlfc0L9vQxOaIDgGEqv/ZnEiKGO5wtfgi8ug/wMY3OJSJnliQfp+fxcbpr2j9FR6q06MMu4iLgzm83Ox4t3MuClebw7fwdFJTbaRAeRVVBPemBPx+LF+rgxlFzwDnj4wo6/4L3+sP9fo5OJCNAk3I/03CLW7s0kK18/t4ygYlZEDPX96v08/esmDuVbaRkZwIdjejD91jNpEu5vdLRaxd5xFNz0J4Q1h6y98PFwWDUV7Hajo4nUaw2DfYmPDMBmhyU7NEWXEVTMiki1sdnsfL58Nxv2Z7E/s4C8opLj9vl7p+Mj81E9Ypl1Tz+GtovSBRUnE90Bbp4Hrc+F0mL45R74eRxYC4xOJlKvHV0NTEvbGkHFrIhUmx1puTz+4wbOe3Mxff73F58v333cPjvTHSvn9GkZgYdFP5JOyzcErvgchkwAkxlWfw7PRTvmpBURQ/RrFQHAwq3p2PVpSY2r0G+O7OzsCt+c9fbbb9O0aVN8fHzo1asXK1euPOX+RUVFPPbYYzRp0gRvb2+aNm3Kxx9/7PT7ikj1+yshtdzzN//aTmp2IQAlpTZW7szglv7NGT+sFV3iQgxI6KbMZuh3P1z7w9FtW2cZl0eknuvVLAwvi5n9mQVlf6BLzanQ1FwhISGn/djPbrdjMpkoLS2t8JtPnz6d8ePHM2XKFHr16sVrr73G8OHD2bJlC5GRkSc8ZtSoUaSkpPDRRx/RsmVLkpKSsNm08oZIbTNrQzIv/bEFgP8b2Ybf1iWxdl8Wr8zewtMXdmDQK/NJyirkz/EDGNouyuC0bqr5QDjnfzDrEbDpwhMRo/h5edCjaShLdxxk4dY0mjcIMDpSvVKhYnbevHnV8uaTJk3i5ptv5vrrrwdgypQp/Pbbb3z88cc88sgjx+0/a9YsFixYQGJiImFhYQA0bdq0WrKJSOXN35LKXV/9S6nNzqXdYrmpb3M6NApm9Icr+H19Mi9d1pm2DYNIyirkh9X7eHC4FlypNE8/x32pilkRI13WPZZOsSGc0SzM6Cj1ToWK2QEDBrj8jYuLi1m1ahWPPvpo2Taz2czQoUNZtmzZCY/5+eef6dGjBy+99BKfffYZ/v7+XHDBBTzzzDP4+vq6PKOIOG954kFu/WwV1lI753ZsyIuXdsRsNtG+UTAAOUUlzFyfxEVdY/grIZW35+1gdK8mxITo/3ClWA7Pv1tabGwOkXrukm6xRkeotypUzK5bt67CJ+zUqVOF9ktPT6e0tJSoqPIfL0ZFRZGQkHDCYxITE1m8eDE+Pj788MMPpKenc8cdd3Dw4EE++eSTEx5TVFREUVFR2fMj43qtVmu9WUnoSDvV3rrJyPZm5lsJ8vHAbD46DGnDvkMUldgY1DqCly5pj91WitVWip/n0eNmb0ji4XNalT1/6ucNvH1Vlwq9p76/5Zkw4wHYSooorQNfE31/67b61l6of212VXudOd5kr8Bld2azGZPJdNor9JwZM3vgwAFiYmJYunQpvXv3Ltv+0EMPsWDBAlasWHHcMWeffTaLFi0iOTmZ4GBHL8/333/PZZddRl5e3gl7ZydOnMhTTz113PYvv/wSPz+/CmUVkeOtSjfx2TYzQ2PsnNf46Lh1ux0Wp5g4M9KO538uMV2eamJPromLmtjwssA9yxx/T3cItXFzG419r4yGmX/Tc+ebHPRvxeJWjxsdR6ReKyqFHdkm7ED7UM1qUBX5+fmMHj2arKwsgoKCTrlvhXpmd+7c6ZJgx4qIiMBisZCSklJue0pKCtHR0Sc8pmHDhsTExJQVsgBt27bFbrezb98+4uPjjzvm0UcfZfz48WXPs7OziYuLY9CgQYSHh7uoNbWb1Wplzpw5DBs2DE9Pz9Mf4ObU3up3KL+Yia8vwY6VRakWwho24uHh8QT6ON7/3JMcN/I/z7f7bOfdBTt5/qo+tG0YWKH31ve3PNNWM+x8kzA/M+d2OfHPTueYsEe1Pzp8oYbp+1u31fX2/rD6AO99v4GOMUE8ePWZQN1v83+5qr3OzJBVoWK2SZMmlQ5zMl5eXnTv3p25c+dy0UUXAWCz2Zg7dy7jxo074TF9+vThm2++ITc3l4AAx5WCW7duxWw2Ext74rEq3t7eeHt7H7fd09OzXvyjOlZ9a7PaW30mz93MocPLNhZabUz/Zx85RSW8c3V3p84z/uw23DesTblhChWl7+9h3o5PmExpCXh8crZr3qzlMLjmW9ecq5L0/a3b6mp7B7aJAjaw4UA2OcV2wvyP/lFYV9t8MlVtrzPHVqiYPZFNmzaxZ88eiovLX3RwwQUXVPgc48ePZ+zYsfTo0YOePXvy2muvkZeXVza7waOPPsr+/fuZNm0aAKNHj+aZZ57h+uuv56mnniI9PZ0HH3yQG264QReAidSgMb2bsi0ll+5NQ3lvQSIxIb7cMbCl0+cxmUxosa8qiusJTfpA5t6qn6u0CHJTIH1r1c8lUg9FBvnQJjqQhOQclmxP5/zOjYyOVC84XcwmJiZy8cUXs379+nLjaI/MQ+vMPLNXXHEFaWlpTJgwgeTkZLp06cKsWbPKLgpLSkpiz549ZfsHBAQwZ84c7rrrLnr06EF4eDijRo3i2WefdbYZIlIFbRsG8c1tvTGZTFzSNZbYUF/8vSv9t7FUhXcgXD/TNefavwo+GAx2jV8Wqax+8REkJOewaFuaitka4vRvn3vuuYdmzZoxd+5cmjVrxsqVKzl48CD3338/r7zyitMBxo0bd9JhBVOnTj1uW5s2bZgzZ47T7yMiVZdVYCXY1/HRz5E/YFtHV2ysq7gB0+Er9lTMilRav/gGfLBop5a2rUFOL4S+bNkynn76aSIiIjCbzZjNZvr27csLL7zA3XffXR0ZRaQW2J9ZQN8X/+L5mZspLlGxUyeZLI57W8U/YROR8no2C8Pbw0xydiHbU3ONjlMvOF3MlpaWEhjo6ImJiIjgwIEDgOMisS1btrg2nYjUGu8v2EFOYQmr9xzCoxIXbIkbUM+sSJX5eFroeXgVsOU7MwxOUz84PcygQ4cOrF27lmbNmtGrVy9eeuklvLy8eP/992nevHl1ZBSRWmDF4R/KN/RpVqnZB8QNmA/3zNrVMytSFQ+f04YnzzfTokEAJSUlRsep85wuZh9//HHy8vIAePrppznvvPPo168f4eHhTJ8+3eUBRcR4OYVWtqTkANC9SajBaaTaqGdWxCU6xASffidxGaeL2eHDh5c9btmyJQkJCWRkZBAaGlp2QYiI1C2rdh/CbofYUF8ig3yMjiPVxXJ4XkdrAZRajz4XEanFnC5ms7KyKC0tJSwsrGxbWFgYGRkZeHh4nHbJMRFxP9/8sw+AAa0aGJxEqlVIE/CLgPx02L0Emg80OpGI21qReJDPlu+mdaQ/jY0OU8c5fQHYlVdeyddff33c9hkzZnDllVe6JJSI1B7JWYXM2pgMwLW9Xb8aoNQiZgu0HuF4nPCbsVlE3FxydiG/rkvi940pRkep85wuZlesWMGgQYOO2z5w4EBWrFjhklAiUnsE+Hjw2Mi2XNY9ljbR+uSlzmtzruM+YSZojkyRSuvTMgKATUk5ZBefZmepEqeHGRQVFZ3wyjyr1UpBQYFLQolI7RHg7cENfZsZHUNqSvOB4OkH2fsgaS006mJ0IhG3FBHgTftGQWw8kM3WLF1TVJ2c7pnt2bMn77///nHbp0yZQvfu3V0SSkREDOLpCy0GOx5vcdEyuSL1VL94x3UGCSpmq5XTPbPPPvssQ4cOZe3atQwZMgSAuXPn8vfffzN79myXBxQR42w6kE1CcjY9m4URG+pndBypKW3OhYRfHeNmB/2f0WlE3Fb/+AimLNjBlkyTlratRk73zPbp04dly5YRGxvLjBkz+OWXX2jZsiXr1q2jX79+1ZFRRAxQVFLK/d+sZfyMtbz113aj40hNanWOY87ZlA2wa4nRaUTcVvemofh4msm2mtiaoqVtq4vTPbMAXbp04csvv3R1FhGpRV7/cxubk7IJ8/fi/rNbGx1HapJfmGPs7I6/YOpIaDEE+t0PTfsYnUzErXh7WDizWRiJ+9PIKdJKYNXF6Z5ZgB07dvD4448zevRoUlNTAfj999/ZuHGjS8OJiDHW7s1kyoIdADx/cUcaBHobnEhq3EXvQqcrwWSBHXMdRe3H58C2OZrlQMQJ747uwv2dSumh1ROrjdPF7IIFC+jYsSMrVqzgu+++IzfX0W2+du1annzySZcHFJGa9+nSXdjscH7nRpzTIdroOGKEwGi45D24axX0uAEsXrBnGXxxGbzXHzb+CDYteytyOh6WSvUbihOc/go/8sgjPPvss8yZMwcvL6+y7YMHD2b58uUuDSciNa+guJQ/Di+ScN1ZWiSh3gtrBudNhnvWQe9x4OkPyevgm7HwTi9Y85Vj6VsROaXcohIO5hYZHaNOcrqYXb9+PRdffPFx2yMjI0lPT3dJKBExzpzNKeQVlxIb6ku3xvpYTA4LagjDn4P7NkD/h8AnGNK3wo+3wZvd4O8PwVpodEqRWmnOfhNnPD+vbPiWuJbTxWxISAhJSUnHbV+9ejUxMTEuCSUixhkQ34CLujTisu6xmEyaG1H+wy8MBj8G926AoRPBvwFk7oHf7ofXO8GSN6BIV22LHCvcG0psdhZtU6dfdXC6mL3yyit5+OGHSU5OxmQyYbPZWLJkCQ888ABjxoypjowiUoOC/Tx57cqu3DU43ugoUpv5BEHf++De9TDiZQiKhdwUmPMEvNYB5v8P8jOMTilSK7QKtmMyQUJyDqnZ+gTD1Zyemuv555/nzjvvJC4ujtLSUtq1a0dpaSmjR4/mscceq46MIlIN9mbkk1dcQpvooBO+bjGrV1YqwNMXet0C3a+D9TNg0STI2AHzX4DFrzl6bo8wmx0zJAx42PFYpJ4I8IQOjYJYvz+bRdvSubR7rNGR6hSni1kvLy8++OADJkyYwPr168nNzaVr167Ex6sXR8SdvDJ7Cz+tOcCIDtHcPSSe39YlsX5/Ftf3acrA1pFGxxN34+EFXa+BzlfBph8dRW3KBsjaU36/Bf+DjES48G3HMSL1RN8W4azfn83CbWkqZl2sUosmAMTFxREXF1f2/Pvvv2fixImsW7fOJcFExLXyi0s4lG8lJsQXm82OzQ4mE/y+IZnfNyTj62mhwFqqH7JSNWYLdLgU2l8CqZuhpODoawdWw+8PO3pw81Lhis/BO9C4rCI1qE/LcN5duJPF29Kx2eyY9emXyzj1Oc97773HZZddxujRo1mxYgUAf/31F127duXaa6+lTx+tDiNS29jtduYlpHLhW0u49sMVZOYXYzabePOqrvxxb3/O69QQkwkKrKWA46MwkSozmSCqHcR0P3o74yYYPd0xvVfifJh6LuSmGp1UpEZ0jQvB38vCwbxiNiVlGx2nTqlwz+z//vc/JkyYQKdOnUhISOCnn37iscce48033+See+7h1ltvJTRU0/iI1Cb/7jnE/35PYOVOx4U44f5e7DtUQIif4+PdVlGBvDW6G3en5PDhokTC/L1pFuFvZGSp61oOhet+hS8uh6S18NEwuOZ7CG9hdDKRauXlYeaOQS0J8vGgYbCP0XHqlAoXs5988gkffPABY8eOZdGiRQwYMIClS5eyfft2/P31y0+kNtl9MI/nftvM7E0pgOOH6PVnNeX2gS3KCtljtYoK5KXLOtd0TKmvYrrBjbPh80vg0C746Gy4eoaj91akDrtzUEujI9RJFS5m9+zZw+DBgwHo168fnp6ePPXUUypkRWoZu93O9Z/8TWJ6HiYTjOoex73D4mkY7Gt0NJGjwlvAjXMO99CugannwajPoOkAo5OJiJupcDFbVFSEj8/RbnEvLy/CwsKqJZSIVF6BtZTmDfzJyC/mi5t60b5RsNGRRE4sINIx5GDGGNjxF3x1BaYRr+JtNUFOMnh6lt/fy18XjInb25uRz8JtaXSKCaFjrH4+u4JTsxk88cQT+Pn5AVBcXMyzzz5LcHD5b8SkSZNcl05ETupQXjGh/scPGfDz8uDDsWcYkEikErwD4arp8NOdsH4GHr/dwzkAG06wr8kCY36CZv1qOKSI67wzfwdfrdzD9X2aqph1kQoXs/3792fLli1lz8866ywSExPL7aOlL0VqxoHMAvq9NI+eTcMY2SEKi9XoRCJV4OEFF78HIY2xL38Hu7UQk8lEud8odhvYS2HxZBWz4tb6x0fw1co9WtrWhSpczM6fP78aY4hIRdntdpYnHqTUZmdZ4kGWJR7EbLLwR/a/RAX50KdlBBd2iTE6pohzzGYY8gQl/R9h5syZjBw5Es9jhxlk7IQ3usKOuZC+HSJ0IY24p7NaRGA2wfbUXA5kFtAoRNczVJXWExRxA/szC3h/4Q5u+2wViel5XNQlhkUPDeLhc9rQNjoQm93Egq3pzPhnH/d8vYa1ezONjiziWmHNoNU5jsd/f2BsFpEqCPbzpHNcCACL1TvrEpVeAUxEqldqdiG/rU/i13VJrNp9qGz7nM0pvHFlV87t1JDbB7bgpj6N+eS7meSEtmLBtoMMbhNZ9oNSpE7peTNs/R3WfAmDH9fFYOK2+sc3YPWeTBZuS2PUGXGnP0BOScWsSA1Izipk+t97Gd4hijbRJ15ha/fBPH5bn0RKViHbUnNZlngQu93xmskEvZqFcV6nRozoEE14gHe5Y6N84frBLbl/eNvqboqIcZoPgvCWcHA7rJvuWFFMxA31bxXB63O3sXh7OqU2OxYtbVslKmZFqtlfCSncP2Mth/KtLNmezvRbzyy7WDI1u5Bf1iXx89oD5YYGnNE0lAAvD+KjAji/cyNGdmxIVJBWjJF6zmyGnrfA7w/Byg+gx42Ov/RE3Ezn2BACvT3IKSwhMS2X+Ch9ylAVKmZFqom11MbLf2zh/YVHZ/146bJOmEwm0nOLuOvL1SzfebT31WwCmx06xgTzyIi2NArx0UIHIv/V+SqY+zSkJcDOhdBciyyI+/GwmPn0xp60jAwgyMfz9AfIKVWomF23bl2FT9ipU6dKhxGpS96cu62skL3urKY8OrIN3h4WAEL9vNiZnofdDt0ah3BB50ac26kRDQK9T3VKEfEJgs5Xwt8fwrznoWlfMFuMTiXitG6NQ42OUGdUqJjt0qULJpMJu91+2rlkS0tLXRJMxJ0dzC3iw8U7AXjx0o5ccUbjcq9bzCYmjepMXJgfcWF+RkQUcV9n3Q1rp8Pe5bDwFRj4sNGJRMRAFZqaa+fOnSQmJrJz506+++47mjVrxjvvvMPq1atZvXo177zzDi1atOC7776r7rwibuH3DcnkF5fSMSaYUT1OfKXqWS0jVMiKVEZoEzj3VcfjBf+DPcuNzSNSSZPmbGXUe8vYnpprdBS3VqGe2SZNmpQ9vvzyy3njjTcYOXJk2bZOnToRFxfHE088wUUXXeTykCLu5qqejVmzN5NLusVoZTyR6tD5CscCCuumw3c3w22LwDfE6FQiTlmeeJCVOzNYvecQLSMDjI7jtpxeNGH9+vU0a9bsuO3NmjVj06ZNLgkl4m7u/PJfBr48j7ScIsAxjOCVyztzVosIg5OJ1GEjX4HQppC1B369j7KrKUXcROfYYADW7csyOIl7c7qYbdu2LS+88ALFxcVl24qLi3nhhRdo21ZzXEr9sz01l9/WJbHrYD5nPPdnWUErItXMJwgu/QjMHrDxe8diCiJupFNsCADr9mUamsPdOV3MTpkyhT/++IPY2FiGDh3K0KFDiY2N5Y8//mDKlCnVkVGkVvtx9f5yz++dvtqgJCL1UGwPGPR/jsczH4T07cbmEXFC58PF7OakHIpLbMaGcWNOF7M9e/YkMTGRZ599lk6dOtGpUyeee+45EhMT6dmzZ3VkFDGMvQIfWy7afnRtbZMJ7hzYsjojich/9bkXmvYDax58dwOU6NMRcQ9xYb6E+nlSXGojITnb6Dhuq1KLJvj7+3PLLbe4OotIrZKeW8Q5ry0iyMeDcYNbcmGXmBMuOfjg2a1ZuSuDi7vGANAswr+mo4rUb2YLXPI+vHsWJK2Fb2+Ayz8Fi9YFktrNZDLRMTaEhVvTWLsvq2zYgTinUv/Tt23bxrx580hNTcVmK98tPmHCBJcEE6kpq3Y7riIN9i2/CktEgDfPX9yBWz5bxfgZa3lr3nbuGRJPiwYBfLlyDxd2bkSv5uH0jY+gb7wu9BIxVFAjRwH7xWWQ8Cv8dCdc9K5jCVyRWqxzbDAb92dRZNU8/ZXldDH7wQcfcPvttxMREUF0dHS5aYdMJpOKWXErczalcPO0f2gU7MMHY3vQvlEw+cUl5BaVEBnoQ5+WETx0TmveX5hIYloe93y9puzYzPxiejUPNy68iJTXfICjoJ1+Daz7GrwDHDMeaHo8qcXuGhzP+GGtNI1jFThdzD777LM899xzPPywVlwR9zf97z0AHMgq5LJ3lzG6V2N+XL2fM1uE8/bobvh7e3DHwJZce2YTPl26iw8W7SSvqIThHaIZc2aT05xdRGpcm5Fw8Xvw/c2OJW+9A2HoRKNTiZyUl4c+Pagqp4vZQ4cOcfnll1dHFpEaN+mKLny2bDffrtrHzvQ8Pjq8BO3G/VnkF5fg5+X4LxLo48m4wfHc0r8FJTZb2XYRqYU6XQ7FufDrvbB4MngFQP8HjE4lclqlNvsJr82QU3P6z4HLL7+c2bNnV0cWkRoX5OPJnYNaMue+/tw6oDmdY4N5+bJOzBk/4IQFq5eHWYWsiDvocT2c/azj8V/PwIr3jM0jcgrvzt/Bmc/P5aPFiUZHcUtO/1Zu2bIlTzzxBMuXL6djx454epa/aObuu+92WTiR6pJdaCXQ26NsjJKHxcyjI7Toh0idctZdUJQDC16E3x9y9NB2vdroVCLHsdntJGcXslYrgVWK08Xs+++/T0BAAAsWLGDBggXlXjOZTCpmpdY7kFnAbZ+vIrvAyguXdKJ3C13EJVJnDXwUinJh+dvw8zjwC4PWI4xOJVJOZ60EViVOF7M7d+6sjhwi1W5/ZgHvzNvOjH/2Yi214+VhJibE1+hYIlKdTCYY/hwUZcPqzxw9tC2GgIeX0clEynSMDQZgb0YBGXnFhPnr36czdAmd1AuLtqUx8OV5fLFiD9ZSO2c2D+Orm3vRONzP6GgiUt1MJhj5MgREQeYeWPul0YlEygn29SxbcEe9s86r1JUs+/bt4+eff2bPnj0UFxeXe23SpEkuCSbiSkt3HMRaaqd1VCBPXdieMzU/rEj94unrWPb2j0dh4avQebR6Z6VW6RQbzM70PNbty2Jg60ij47gVp4vZuXPncsEFF9C8eXMSEhLo0KEDu3btwm63061bt+rIKFJlezLyARjSNlKFrEh91eN6WPIaZO2BNV84novUEp1iQ/hpzQH1zFaC08MMHn30UR544AHWr1+Pj48P3333HXv37mXAgAGaf1ZqrWcu7ECb6EBGdmxodBQRMYqnL/S9z/F40atQUnzq/UVqULfGIXRtHEKHmGCjo7gdp4vZzZs3M2bMGAA8PDwoKCggICCAp59+mhdffNHlAUVcIczfi9/u7qcfEiL1XffrICAasvbCms+NTiNSpmvjUH64ow/3Dm1ldBS343Qx6+/vXzZOtmHDhuzYsaPstfT0dNclE6mC4lL4c3MqHy5KJCvfit2uVVVEhPK9swvVOytSFzg9ZvbMM89k8eLFtG3blpEjR3L//fezfv16vv/+e84888zqyChSIYXWUuYlpPLrugP8udFC0co1ADz722b+uLc/raMDjQ0oIrVD9+scy9xm74PnooBT/KEbHAM3zoHA6JpKJ/VcfnEJh/KtmjrSCU4Xs5MmTSI3NxeAp556itzcXKZPn058fLxmMhDDZOQVM/qD5SQk5xzeYqJhsA8jOzbkoi4xKmRF5ChPHxjyBPx0J9htp943cw+s/8axmphINftl7QHu+Xo1vVuE88VN6iCsKKeL2ebNm5c99vf3Z8qUKS4NJOIsu93ObZ+vIiE5hzB/Ly7p2oigzO3cNqofXl6aekdETqDrNdDmXCgpOvk+a7+CPydCwm8qZqVGNIvwx2aHdfuysNnsmDU8rkK0aIK4FZvNzob9WUxZsIPliQcBxzLKj4xoQ/MG/sy4tTcPD29Fk0DHdhGRk/INdQwfONmt4+EZevYsh9w0Y7NKvdA6OhBvDzM5hSXsOphndBy3oWJW3Mbug3n0ffEvzntzMf/7PYEfV+8ve61b41Dm3DeAlpEBBiYUkTolOBYadgHssGWm0WmkHvC0mGnXKAhw9M5KxaiYFbexYmcGB7IK8fYwM6RNJD2bhZV7XbMViIjLtT3PcZ/wm7E5pN7oHBsCwFotnlBhKmbFbaRkFQIwsHUDPrruDC7pFmtwIhGp89ocLmYT50FRzqn3FXGBTrGO+dDVM1txTl8AJmKE1OxCPlm6C4DBbbRmtYjUkAZtIKw5ZCTCqqnQ5CznzxHaDPzCTr+fCI5lbQE2HsiipNSGh0X9jqfjdDFbWlrK1KlTmTt3Lqmpqdhs5ac1+euvv1wWTuSIp3/dREZeMS0a+HOpemRFpKaYTI7e2aVvwOzHK3cOnxC4byN4a0y/nF7zCH9G9YilbcMgSmx2PCxGJ6r9nC5m77nnHqZOncq5555Lhw4ddMW41IhnLuxAobWUCee111+pIlKzetwAifOhINP5Y3MOQGEmpG+FmG4uDiZ1kdls4qXLOhsdw604Xcx+/fXXzJgxg5EjR1ZHHpETCvX34sOxZxgdQ0Tqo7BmcNuiyh370XDYuxwO7VIxK1JNnO7i8vLyomXLltWRReQ4KxIPkpVvNTqGiEjlhDZ13B/aZWQKcTOlNjtbU3L4Y2Oy0VHcgtPF7P3338/rr7+O3W6vjjwiZex2O3d88S8jXl/Ibk0eLSLuSMWsVEJSVgFnT17IHV/8S6G11Og4tZ7TwwwWL17MvHnz+P3332nfvj2enp7lXv/+++9dFk7qt8x8KwfzigFoEOhtcBoRkUpQMSuVEBPiS0SAF+m5xWw8kE33JqFGR6rVnC5mQ0JCuPjii6sji0gZu93Oh4sTAYgL88XPS7PIiYgbCmvmuFcxK04wmUx0ig3hr4RU1u3LVDF7Gk5XCJ988olLA7z99tu8/PLLJCcn07lzZ95880169ux52uOWLFnCgAED6NChA2vWrHFpJjFOSamN39Yn8e78HSQkOyYof3B4G4NTiYhU0pGe2ax9UGoFi+cpdxc5olNs8OFiVosnnE6l5zhKS0tj8eLFLF68mLS0tEqdY/r06YwfP54nn3ySf//9l86dOzN8+HBSU1NPeVxmZiZjxoxhyJAhlXpfqb2em7mZe75eQ0JyDv5eFu4dGs/5nRoaHUtEpHICosDTH+yl8O80o9OIG9GythXndDGbl5fHDTfcQMOGDenfvz/9+/enUaNG3HjjjeTn5zt1rkmTJnHzzTdz/fXX065dO6ZMmYKfnx8ff/zxKY+77bbbGD16NL1793Y2vtQyM/7Zy8YDR//qHNImijB/L+4f1oqljwzh3qGtNJexiLgvkwn63ud4PPMBSJhpbB5xG0eWtU1MyyO7ULP6nIrTxez48eNZsGABv/zyC5mZmWRmZvLTTz+xYMEC7r///gqfp7i4mFWrVjF06NCjYcxmhg4dyrJly0563CeffEJiYiJPPvmks9GlFnrx9wSmLtnFwdwiAHq3CGfZo4O5a0g8wX76OE5E6oD+D0DXa8Fug29vgL0rjU4kbiA8wJuYEF8ANmiowSk5PWb2u+++49tvv2XgwIFl20aOHImvry+jRo3i3XffrdB50tPTKS0tJSoqqtz2qKgoEhISTnjMtm3beOSRR1i0aBEeHhWLXlRURFFRUdnz7OxsAKxWK1Zr/fhL50g7a1t7f1mXxKH8Yr5ZtY/bBzQlyNvxt5UZsFptpz74FGpre6uL2lu3qb11xDkvY8lJxrx9DvYvR1EyZiZExNfd9p5EfWsvVK3ND50dj6+XhTZRfm7zNXPV99iZ450uZvPz848rQAEiIyOdHmbgjNLSUkaPHs1TTz1Fq1atKnzcCy+8wFNPPXXc9nnz5uHn5+fKiLXenDlzjI5QZs5+E7/ucSw43THUxrql81nv4tEEtam9NUHtrdvUXvdn8RtFH78dhOYnYv3kfBa2mkCRZwhQN9t7KvWtvVD5NhcAi3a4NktNqOr32Jma0mR3cvWDIUOGEB4ezrRp0/Dx8QGgoKCAsWPHkpGRwZ9//lmh8xQXF+Pn58e3337LRRddVLZ97NixZUMXjpWZmUloaCgWi6Vsm81mw263Y7FYmD17NoMHDz7ufU7UMxsXF0dSUhLh4eHONN1tWa1W5syZw7Bhw46bF7g6FJfY8PI48QiWg7lFfLlyH2/Mc/zPvLVfM+4b2hKL2XWVbE2312hqb92m9tYxeel4TBuJKSMRe2QHCq76njkLl9fd9v5Hnf/+nkB9a7Or2pudnU1ERARZWVkEBQWdcl+ne2Zff/11hg8fTmxsLJ07dwZg7dq1+Pj48Mcff1T4PF5eXnTv3p25c+eWFbM2m425c+cybty44/YPCgpi/fr15ba98847/PXXX3z77bc0a9bshO/j7e2Nt/fxE+57enrWi39Ux6qJNr89bzuvzN7CkDZRjB/WinaNjv4DtNvtXPreIvZnFgBwa//mPDqybbVlqW/fY7W3blN764iQhnDN9/DR2ZhSN+Dz002Ygq+ru+09ifrWXqh8m+duTmHN3kyuO6sp4QHus4BQVb/HzhzrdDHboUMHtm3bxhdffFE2tvWqq67i6quvxtfX16lzjR8/nrFjx9KjRw969uzJa6+9Rl5eHtdffz0Ajz76KPv372fatGmYzWY6dOhQ7vjIyEh8fHyO2y7GmLUhiZf/2ALAn5tT+HNzCud2bMh9w+JpGRmIyWTiwi6NWLI9nat7NeGy7rEGJxYRMUBYM7j6G5h6LuZdC+kRUgD5fSH4+CF8Is/P3MyOtDy6xIUwpK3+jZxIpZZV8vPz4+abb67ym19xxRWkpaUxYcIEkpOT6dKlC7NmzSobk5uUlMSePXuq/D5S/Q5kFjB+xloARvWIpcBq45e1B/htfRKXdIuhZWQgAOOHteKhc7QIgojUc426wBWfYf/ichpl/o39nR7Q737odSt4OtcxJHVb57gQdqTlsXZflorZk6hQMfvzzz8zYsQIPD09+fnnn0+57wUXXOBUgHHjxp1wWAHA1KlTT3nsxIkTmThxolPvJ9WjYbAPD5zdmvlb03j+4o54WMzcMbAFr/25lcl/bmVAqwZ4WMx4WCq9ToeISN3SYjClo78l79u7CC7cC38+CSs/gMGPQacrwGw5/TmkzuscG8L3/+5nnRZPOKkKFbMXXXQRycnJREZGlrtY679MJhOlpaWuyia13JFrB00mEyaTiRv6NuO6s5piPnwxV9uGQbx3bQ8jI4qI1Gr2Jn2Z3+YZzo3Lw2PBC5C9D368HZa9DcOeghZDHAsvSL11ZPGEdfuysNvtWkjoBCrUTWaz2YiMjCx7fLKbCtn65eMluxjx+iJ+X59Uts3swlkJRETqBZMZe6cr4K5/YOhT4B0MKRvg80vhs4sgaa3RCcVAbRsG4WE2kZFXzL5DBUbHqZUqNWb2vzIzMwkJCXHFqaQWKrXZWZF4kMT0PHb+5waQePheRESqwNMX+t4L3cbAoldh5fuQOB/e6w8dR8HgxyG0idEppYb5eFpo0zCQDfuzWbcvi7iw+jVHfkU4PYDxxRdfZPr06WXPL7/8csLCwoiJiWHtWv31WFddN/VvHv9xAx8t3slfCallhewdA1twx8AWBqcTEalD/MJg+HMw7h9HEQuwfga81QP+eAzyM4zNJzWuU2wIABsPaFnbE3G6Z3bKlCl88cUXgGN1hz///JNZs2YxY8YMHnzwQWbPnu3ykFKz9h3K5/2FiTx+bju8PMxYzCb6tozABDSN8KdZhD/NI/xpGRlAZJCP0XFFROqm0CZw6QfQ+w6YMwF2LoRlb8HqzxwzH/S8FTz1M7g+uKVfc27o04zmEf5GR6mVnC5mk5OTiYuLA+DXX39l1KhRnH322TRt2pRevXq5PKDUvM+X72Hast3Y7fDMRY45fD++7gyDU4mI1FONusKYn2H7XEdRm7rRcb/yA7joHWjW3+iEUs2aqog9JaeHGYSGhrJ3714AZs2axdChQwHHle26AKxumL8lFaDc6l0iImIgkwnih8Jti+CidyEoBrL2wrQLYf7/wKbfv1J/OV3MXnLJJYwePZphw4Zx8OBBRowYAcDq1atp2bKlywNK9bGW2soeFxSXMuPvvZz/5mISknMAiAvVIHMRkVrFbIEuo2Hc39D1GrDbYP4LjqI2O+n0x4vb+m1dEnd9tZrZG5ONjlLrOD3MYPLkyTRt2pS9e/fy0ksvERAQADhW67rjjjtcHlCqR6G1lJGvL6JtwyD6xUfw/MzNZBeWAOBlMXNx1xjOaBZqcEoRETkhL3+48G1oNgB+vQ92LYIpfeGS96DlUKPTSTVYtfsQv6w9QKifJ2e3jzY6Tq3idDHr6enJAw88cNz2++67zyWBpPptT83l2d82kZieR05RCfcNa0VuUQlxYb5c06sJl/eII8zfy+iYIiJyOp1GQaNu8O11kLzeMTdtn3sd03hZPI1OJy7UOc6xeMLafZrR4L8MX85Was76/Vl8unwvv65LotRmx2I28eKlHWkZGcCPd/ahQ6NgLXogIuJuIlrCjX/C7Mfh7w9gyWuweylc9hGENDY6nbhI58PTc20+kE1xiQ0vDy0Pf4SWs60HsgqsvLvJTMKyFWXbhraN4qFzWtMqKhA4OoediIi4IU8fOPcVaNYPfroL9q2EKf0csx20OdfodOICTcL9CPb1JKvAypbkHDoeXuZWtJxtvRDg7UGxzYSH2cTFXWP49a6+fDi2R1khKyIidUS7C+G2hRDTHQoz4evR8PsjUFJkdDKpIpPJRKfYI0MNMo0NU8u4ZDlbqZhvV+0jMtCb7k1C8fd27Zc+r6iErSk5JCTnkJCUTUJyDg8Ob02PpmFYzCauaVlK3/59adUwxKXvKyIitUxoU7h+Fsx9yrHIwop3oSADLnnf6GRSRZ1ig1m0LZ11+zIBLW18hNMV1d13303Lli25++67y21/66232L59O6+99pqrstUpJaU2Jv68kdyiEjzMJjrGBtOrWTi9mofRo0kogT6VG6i/YX8WD3+3jo0Hso97bd2+LHo0DQMg3AeaadJlEZH6wcPLsSRuTHf49nrY/CvYbGDWOEt31ik2BIvZRF6xPgk/ltPF7HfffXfCi8DOOuss/ve//6mYPYm8olLObh/FisQM9mcWsHpPJqv3ZDJlwQ7MJrjijDheuKSTU+dcuiOd6z/5m6ISx3yxDQK9aRMdSJvoQFpHB9GrWVh1NEVERNxFuwvBwxeseXBwOzRoZXQiqYKBrRuw8anh+HhajI5SqzhdzB48eJDg4OMHHQcFBZGenu6SUHVRsJ8nk0Z1AWBvRj4rdmawIvEgK3ZmsCcjn0bBvmX7ZhVYeernjQxsE0nv5uHkFpWw71A+ezMK2Hson9E9GxMX5keb6CDO6RBNZr6Vly7rRFSQ1ugWEZFjmC0Q3dFxQVjSWhWzbs7bQ0XsiThdzLZs2ZJZs2Yxbty4ctt///13mjdv7rJgdVlcmB9xYX5c1j0WgAOZBXgfM8XGom1pfL96P9+v3n/C47s1DiUuzI8wfy9ev7Jr2TRbIiIix2nU5XAxuwY6XW50GnERu92OyaTf/VCJYnb8+PGMGzeOtLQ0Bg8eDMDcuXN59dVXNcSgkhqF+JZ73joqkFv7N2fellS2puTi42kmLtRRAMeF+tIwuHwPrApZERE5qYZdHPdJaw2NIa4xf0sqk+ZsJT4ykFdHdTY6Tq3gdDF7ww03UFRUxHPPPcczzzwDQNOmTXn33XcZM2aMywPWR/FRgTw6si2PjmxLfnEJvp4W/fUlIiKV0/BwwZO0VheB1QFmk4l1+7LIKrAaHaXWqNT8ULfffju33347aWlp+Pr6EhAQ4Opccpifl2ZPExGRKmjQBjx8oCgbDu2E8BZGJ5IqODLX7O6D+WTmFxPip+XnK/XnWUlJCX/++Sfff/89drsdgAMHDpCbm+vScCIiIlJFFg+Iau94nLTG0ChSdSF+XjQJ9wMcU3BKJYrZ3bt307FjRy688ELuvPNO0tLSAHjxxRd54IEHXB5QREREqiiqg+M+dbOxOcQljixBv04rgQGVKGbvueceevTowaFDh/D1PXrh0sUXX8zcuXNdGk5ERERcILKt417FbJ3QuWxZW/XMQiXGzC5atIilS5fi5VV+jEbTpk3Zv//EU0mJiIiIgVTM1inqmS3P6Z5Zm81Gaenxy6jt27ePwMBAl4QSERERF2pwuJg9tBOsBcZmkSrrEBNEXJgv3ZuEUmjV0rZOF7Nnn312uflkTSYTubm5PPnkk4wcOdKV2URERMQVAiLBNxTsNkjfanQaqSI/Lw8WPTSYd67urqVtqUQx+8orr7BkyRLatWtHYWEho0ePLhti8OKLL1ZHRhEREakKkwki2zkepyYYm0XExZweMxsXF8fatWuZPn06a9euJTc3lxtvvJGrr7663AVhIiIiUos0aAO7l8CKd6HzFUanERew2+2k5RYRGehz+p3rMKeKWavVSps2bfj111+5+uqrufrqq6srl4iIiLhS1OGe2QOroaQIPLyNzSNVsis9j4veWYLdDmsmDKvXK4U6NczA09OTwsLC6soiIiIi1aXTlUcfF+UYl0NcolGIL/lFpWQVWNl9MN/oOIZyeszsnXfeyYsvvkhJSUl15BEREZHq4B0Ano6Vo1TMuj8vDzNtGwUBsLaeT9Hl9JjZv//+m7lz5zJ79mw6duyIv79/ude///57l4UTERERF/IKAGs+FGv5+bqgc2wwa/dmsm5fFhd2iTE6jmGcLmZDQkK49NJLqyOLiIiIVCfvAMhLhSIVs3WBY/GE3fV+8QSni9lPPvmkOnKIiIhIdfMKcNyrZ7ZOOLKs7Yb92ZSU2vCwOD16tE6ocKttNhsvvvgiffr04YwzzuCRRx6hoECriIiIiLgN78MrdWrMbJ3QvEEA/l4WCqylbE+rv3+gVLhn9rnnnmPixIkMHToUX19fXn/9dVJTU/n444+rM5+IiIi4inpm6xSL2cRVPRvjYTHj7+X0h+11RoVbPm3aNN555x1uvfVWAP7880/OPfdcPvzwQ8zm+tmtLSIi4la8DxezGjNbZzx+XjujIxiuwlXonj17GDlyZNnzoUOHYjKZOHDgQLUEExERERdTz6zUQRUuZktKSvDxKb9cmqenJ1ar1eWhREREpBpozGydlJlfzMKtaRSVlBodxRAVHmZgt9u57rrr8PY+uvxdYWEht912W7m5ZjXPrIiISC2lntk6x263M3TSAtJzi/nhjrPo2jjU6Eg1rsLF7NixY4/bds0117g0jIiIiFQjjZmtc0wmEx1igpm/JY11+7JUzJ6K5pcVERFxc+qZrZM6xYYwf0tavV3WVtMQiIiI1BcaM1snHVk8Yd2+LIOTGEPFrIiISH1xpGe2KNvYHOJSjmVtYUdaLrlFJcaGMYCKWRERkfoiIt5xn7weDu0yNIq4ToNAbxoF+2C3w/p62DurYlZERKS+CG8BLYaA3QbLpxidRlzoSO/suno4blbFrIiISH3S+07H/erPoCDT0CjiOlf2jOOFSzpyTodoo6PUuPq7kK+IiEh91GIwRLaD1E3w76fQ5x6jE4kLDGwdaXQEw6hnVkREpD4xmY72zq54D0q1kqe4NxWzIiIi9U3Hy8E/ErL3w8YfjU4jLrIlOYfPlu9m04H6NVuFilkREZH6xsMbet7ieLzsTbDbjc0jLjFlwQ6e+HEDszclGx2lRqmYFRERqY963AAevpC0FvauNDqNuECnerp4gopZERGR+sg/HNqe53i89Xdjs4hLHDs9l70e9barmBUREamv4oc77rfNMTaHuET7RkF4mE2k5xZzIKvQ6Dg1RsWsiIhIfdVyCGCClA2Qtd/oNFJFPp4WWkUFArBub6axYWqQilkREZH6yi8MYs9wPN6u3tm6oHOcY9zs2no0blbFrIiISH0Wf7bjXkMN6oT6uKytilkREZH6LH6Y4z5xPpQUGRpFqm5Im0i+vKkXU67tbnSUGqNiVkREpD6L7gQBUVCcC3uWGZ1GqigyyIezWkYQ5ONpdJQao2JWRESkPjOboeXh3lkNNRA3pGJWRESkvjsy1GDbbGNziEts2J/Fs79uYuqSnUZHqREqZkVEROq7FoPAZIH0rZBRPwqgumx7ai4fLt7JT2sPGB2lRqiYFRERqe98gqFxb8fj7X8am0Wq7MiytpsOZGMttRmcpvqpmBURERENNahDmob7E+jjQVGJjS3JOUbHqXYqZkVEROTofLM7F4K1wNgsUiVms6msd3ZdPVg8QcWsiIiIQGRbCIqFkkLYtdjoNFJF9WnxBBWzIiIiAiaThhrUIZ1j68+ytipmRURExKH5QMf9vn8MjSFVd6RnNiOvqM5fBOZhdAARERGpJfwjHPfFucbmkCprGOzDkkcG0yjYB5PJZHScaqViVkRERBy8/B33xXnG5pAqM5lMxIT4Gh2jRmiYgYiIiDh4BTju1TMrbkTFrIiIiDgc2zNrtxubRapsb0Y+N0/7hyveW2Z0lGpleDH79ttv07RpU3x8fOjVqxcrV6486b7ff/89w4YNo0GDBgQFBdG7d2/++OOPGkwrIiJSh3n6Oe5tJVBabGwWqTJ/bw/mbEphxc4MsgqsRsepNoYWs9OnT2f8+PE8+eST/Pvvv3Tu3Jnhw4eTmpp6wv0XLlzIsGHDmDlzJqtWrWLQoEGcf/75rF69uoaTi4iI1EFHemZB42brgDB/L+LCHONm19fhKboMLWYnTZrEzTffzPXXX0+7du2YMmUKfn5+fPzxxyfc/7XXXuOhhx7ijDPOID4+nueff574+Hh++eWXGk4uIiJSB1k8weLteHxwu7FZxCWOTNG1tg4vnmBYMVtcXMyqVasYOnTo0TBmM0OHDmXZsoqN7bDZbOTk5BAWFlZdMUVEROqXRl0c959eAP98rLGzbq5z2bK2mcYGqUaGTc2Vnp5OaWkpUVFR5bZHRUWRkJBQoXO88sor5ObmMmrUqJPuU1RURFFRUdnz7OxsAKxWK1Zr3R0/cqwj7VR76ya1t25Te+u2Wtneiz/C8ss4zDvnw6/3Ydv2J6UjJ4Nf1TuOamV7q5nRbW4X7ZihYu3ezBrJ4Kr2OnO8yW435k+uAwcOEBMTw9KlS+ndu3fZ9oceeogFCxawYsWKUx7/5ZdfcvPNN/PTTz+V6939r4kTJ/LUU0+d8Hg/P7/KN0BERKSusttokfoH7ZJmYLaXUuAZyr9NbiU9sJ3RycRJhaXwyEoLdkw83b2EYC+jE1VMfn4+o0ePJisri6CgoFPua1jPbEREBBaLhZSUlHLbU1JSiI6OPuWxX3/9NTfddBPffPPNKQtZgEcffZTx48eXPc/OziYuLo5BgwYRHh5e+Qa4EavVypw5cxg2bBienp5Gx6l2am/dpvbWbWpvbXIepUk3YfrxFnwzdnDW9hexnXU3tv6POMbWVkLtbm/1qA1t/nz/cjwtZrr3bkurqMBqfS9XtffIJ+kVYVgx6+XlRffu3Zk7dy4XXXQR4BgDO3fuXMaNG3fS47766ituuOEGvv76a84999zTvo+3tzfe3t7Hbff09Kw3/5GOqG9tVnvrNrW3blN7a4nGPeC2RTDrEUz/TsOy9HUsuxbBpR9CeItKn7bWtrcaGdnmn8f1rfElbavaXmeONXQ2g/Hjx/PBBx/w6aefsnnzZm6//Xby8vK4/vrrAUev6pgxY8r2//LLLxkzZgyvvvoqvXr1Ijk5meTkZLKy6u50EyIiIoby8ocL3oRR08AnBA78C+/1hzVf6uIwN1HThWxNM7SYveKKK3jllVeYMGECXbp0Yc2aNcyaNavsorCkpCT27NlTtv/7779PSUkJd955Jw0bNiy73XPPPUY1QUREpH5odyHcvgSa9HUsd/vj7fDdjVCQaXQyqaD84hIMulSqWhk2zOCIcePGnXRYwdSpU8s9nz9/fvUHEhERkRMLjoWxP8PiyTDvedjwHWTtgxtnG51MTsFms3PB24vZdCCb+Q8MonF43boA3vDlbEVERMSNmC3Q/wG4YZbj+d4VUJRjbCY5JbPZhNlkwmavm4snqJgVERER58X1dIyhBcjca2gUOb3Oh1cCq4uLJ6iYFRERkcoJaey4z1IxW9t1OrwS2Np9de+ieRWzIiIiUjlHitnMPafeTwzXOS4EgA37syi11a2LwFTMioiISOUExznuVczWei0aBODnZSG/uJQdablGx3EpFbMiIiJSOSGHi1kNM6j1LGYTHWIODzXYm2lsGBczfGouERERcVNlPbMqZt3B2e2iiA3xJTa0bk3NpWJWREREKufImNn0rVCYBT7BxuaRU7qpX3OjI1QLDTMQERGRyonqAOHxUJTtWERBxAAqZkVERKRyLB4w8mXH45XvQ9I6Y/PIaZXa7GxJziE9t8joKC6jYlZEREQqr8UgaH8x2G0w8wGw2YxOJKdw62erGP7aQn5fn2R0FJdRMSsiIiJVc/Zz4OnvWNp27ZdGp5FTaNswEKhbiyeomBUREZGqCY6BgY84Hs+ZAPkZxuaRk6qLy9qqmBUREZGqO/N2aNAW8g/CX88YnUZOolOcY8aJ7am55BWVGJzGNVTMioiISNVZPOHcVxyP//kE9q8yNo+cUGSgDw2DfbDZHUvb1gUqZkVERMQ1mvaFTlcAdvj6ashINDqRnECnWEfv7Lo6Mm5WxayIiIi4zjn/g8h2kJMEn14AmXuMTiT/0enwuNm1dWTcrIpZERERcR2/MBjzk2Mxhay98On5kH3A6FRyjAGtGnDv0HiuObOJ0VFcQsWsiIiIuFZAJIz9GUKbwqFdjoI2J8XoVHJYh5hg7h3aijObhxsdxSVUzIqIiIjrBTWCsb9AcBwc3A7TLnTMdCDiYipmRUREpHqENHYMOQhsCGmb8fjyMjxL8oxOJcDB3CJmb0xm6Y50o6NUmYpZERERqT7hLWDMz+DfAFPKenrveBmKcoxOVe/9uOYAt3y2io8X7zQ6SpWpmBUREZHq1aAVjPkZu28YofmJWKZfBaVWo1PVa50PT8+1dl8Wdrvd4DRVo2JWREREql9UO0pGf4vV4od573JY86XRieq19o2CsZhNpOUUkZxdaHScKlExKyIiIjUjuhMJ0Rc5Hi98BUqKDY1Tn/l6WWgVFQjA2r3uvXiCilkRERGpMbsiBmP3j4SsPbDmc6Pj1Gudy1YCyzQ2SBWpmBUREZEaYzN7Yetzr+PJwlehpMjQPPXZkZXA3H1ZWxWzIiIiUqNsXcc4puvK3gerPzM6Tr3V6ZieWZvNfS8CUzErIiIiNcvDB/rd73i8aBJY3fsCJHfVOjqQyVd05sc7+2AyGZ2m8lTMioiISM3rNgaCYiB7P/w7zeg09ZKnxczFXWNp3iAAkxtXsypmRUREpOZ5eB/tnV34MqRvNzaPuC0VsyIiImKMrtdCeDzkpcKHQ2DnQqMT1TtpOUV8sDCRSbO3GB2l0lTMioiIiDE8vOD6mRB7BhRmwmcXa8hBDcsqsPLczM28vyiRklKb0XEqRcWsiIiIGCcgEsb+Ah0uBVsJ/HwXzH4CbKVGJ6sXmkf4E+jtQaHVxtaUXKPjVIqKWRERETGWpy9c+hEMfNTxfOkbMP1aKHLP4sqdmM0mOrr54gkqZkVERMR4JhMMfMRR1Fq8Yctv8Mk5kLXf6GR13pHFE9a66eIJKmZFRESk9uh4GVz3K/g3gOT1jgvDDqw2OlWd5u7L2qqYFRERkdolrifcNBcatIWcJPh4BGz62ehUdVanuBAAtiTnUGh1v7HKKmZFRESk9gltAjfOhpZDoaQAZlwLc5/WhWHVoFGwDxEBXgAkpuUZnMZ5KmZFRESkdvIJgqumQ6/bHc8XvQqfXQS5qYbGqmtMJhPTb+3NhqeG065RkNFxnKZiVkRERGoviweM+J/jwjBPf8fCClP6we6lRierU1o0CMDH02J0jEpRMSsiIiK1X8fL4JZ50KAN5CbD1PNgyRtgtxudTAymYlZERETcQ4PWcPNf0HEU2EthzhMw/RooyDQ6mdsrKbXx+I/rufCtxWQXWo2O4xQVsyIiIuI+vPzhkvfh3Elg8YKEX+H9AZC01uhkbs3DYmZeQhpr92Wxwc3mm1UxKyIiIu7FZIIzboQb/oDgxnBoF3w4DP6dpmEHVdA5zjHfrLstnqBiVkRERNxTTDe4dQHED4fSIvj5LvjxDijMNjqZW+p8eCUwd1s8QcWsiIiIuC+/MLjqaxgyAUxmWPslvNnd0UurOWmd0qmsmFXPrIiIiEjNMZuh3/0w5mcIawF5qY5e2vcHwq4lRqdzGx1jgzGZYH9mAem5RUbHqTAVsyIiIlI3NOsHdyyHs58D72BIXgdTR8KMMY5xtXJKAd4etGgQALjXUAMVsyIiIlJ3eHjBWePg7n+hxw2OoQebfoK3esKfT0FRjtEJa7VOscHEhPiSV+Q+QzQ8jA4gIiIi4nL+EXDeZOhxI/zxqGPlsMWTYM0XMORJ6HyVY3iClPPipZ3wtLjX18W90oqIiIg4I7qDYyztlV9CaDPITYGf7oAPBsHuZUanq3XcrZAFFbMiIiJS15lM0OZcuHMFDHsGvIMgaQ18cg58cx1k7jE6Ya1jt9sptbnHnL0qZkVERKR+8PCGPnfDXf9C9+sAE2z8Ad46A+b/D0pLjE5YK0z8eSNdn5nDHxuTjY5SISpmRUREpH4JaADnvw63LYKm/aCkEOa/4Jj5IHOv0ekMV1xqIzPfytq9mUZHqRAVsyIiIlI/RXeEsb/ApR85hh7sXQFT+sDmX4xOZqjOsUeWtc00NkgFqZgVERGR+stkgo6XOXppY7pDYRZMvwZ+ewCshUanM0TnuBAANuzPxuYG42ZVzIqIiIiENoUb/oA+9zie//0BfDgE0rYaGssILRsE4OtpIbeohMT0XKPjnJaKWREREREAiycMexqu/g78IiBlA7w/AFZ/Afba30PpKh4WMx1iggBYuzfL4DSnp2JWRERE5FjxQ+H2JdCsP1jzHfPS/nBrvVo9rFNsCOAey9qqmBURERH5r8BouPZHGPw4mCywbjq81x8OrDE6WY3o2SyMs1qEEx8VaHSU09JytiIiIiInYrZA/wehSV/47ibISISPhkG3sdDjBohqZ3TCajO8fTTD20cbHaNC1DMrIiIicipNejtmO2hzHpQWOy4Oe7c3fDQc1k6vt7Me1BYqZkVEREROxy8MrvjcMfSg7QWOoQd7l8MPt8CkNvDHY5C+3eiULncor5ikrAKjY5ySilkRERGRijCZoMUguOIzuG8jDHocgmKh4BAsewve6g6fnu9YIrek2Oi0Vfbegh10fWYOk+fU7unJNGZWRERExFlBDWHAg9BvPGybA/98DNtmw86Fjpt/JHS9BjpfbXTSSmsa4Q/Aun21e3ouFbMiIiIilWW2QOtzHLfMPfDvNMctNwUWT8Jj8WTODOqIaasJ2owEi/uUXp0PT8+1NSWH/OIS/LxqZ3YNMxARERFxhZDGjqm87tsIo6ZB84GYsBOVvQ6Pb66F1zvB/P9B9gGjk1ZIdLAPUUHe2Oyw8UC20XFOSsWsiIiIiCtZPKHdhTDmJ6y3r2Rb5EjsvmGQvR/mvwCTO8DXV8P2P6G0xOi0p3Rk8YS1ezMNzXEqtbO/WERERKQuCGvOppgraXr2B3hu+x1WfQK7l0DCr46bxRsi20Bke4g6cusAAQ2MTg5A59hg5mxKqdXjZlXMioiIiFQ3D2/odLnjlprgKGrXfg2FmZC01nE7ln+Do4VtVHuIbAcN2oCnT43GdodlbVXMioiIiNSkyDYw4kUY/gJk7oKUjZCyCVI2OB5nJEJeGiTOd9yOMFkgvKVj5bEjhW5kO8dYXZOpWqJ2jg3hqp6N6RIXjN1ux1RN71MVKmZFREREjGA2Q1hzx63t+Ue3F+dBWsLhIvfIbYNjPtv0LY7bxh+O7u8d5Chqo9ofLnQPF7k+QVWOGPz/7d17VFVl+gfw74EDeAgODIpwFFS8YGomqUFIkzrioOUlG5ME0yycnHJpkjY6lYhrUkvN2+DCVEDNRMf7jKKZtxHzBgKpIHjBsOSSKD9ADFGe3x/GyeM5ICAKZ/P9rLXX8rz72e9+v+DlcZ99NrZWmPNa10ee53FiM0tERETUkFg/BbTscW+rIAIU5dxrbPPua3J/SQdKC+/9NLIrxwzncWh13324v21O7czq8WDVUe9pIiIiMG/ePOTk5KBbt25YunQpvL29K60/ePAgQkNDcfbsWbi7u+OTTz7BW2+99eQWTERERPSkqVT3flCDVgd08P99/G4ZcO3871dv81Lv/brwZ+D/su5tGXG/19fiA2e375QjNbsQN27eRt+nmz/GkLVTr83shg0bEBoaisjISPj4+GDRokUICAhAeno6mjc3/mJlZmbilVdewfjx47Fu3Trs27cPISEh0Ol0CAgIqIcERERERPXI0uq3Wws6A3j99/GS60Be2u9Nbu7Ze6/Lbtb4A2fJVwowYvlRuGhtcPwf/mho6rWZ/fLLLzFu3DiMHTsWABAZGYmdO3ciKioK06ZNM6qPjIyEh4cHFixYAADo1KkT4uPjsXDhQjazRERERBVsnYA2fve2CuXltfrAWXfnTpigtkZasTvycp5Gc1e3JxymavXWzN6+fRuJiYmYPn26fszCwgL+/v44evSoyWOOHj0Kf3/D/xEEBATggw8+qPQ8paWlKC0t1b8uLLz3EyzKyspQVlb2CAnMR0VO5lUm5lU25lU25lW+BpfZ3v3e1n7A72O3b0J1LR3IS4VKv52F6rcPnKmvpWPKbx1jblpzlDV9p9Lp6ypvTY6vt2b22rVruHv3LlxcXAzGXVxccO7cOZPH5OTkmKwvLCzErVu3oNFojI6ZM2cOwsPDjcYPHDgAW1vbR0hgfvbu3VvfS3iimFfZmFfZmFfZGltewFwyOwF4EXB6EfiDoMmdAmhvXTHY0nJ+xf/t2vXQmR41b0lJSbVr6/0DYI/b9OnTERoaqn9dWFgId3d39O3bF02bNq3HlT05ZWVl2Lt3L/r37w8rK6v6Xs5jx7zKxrzKxrzK1tjyAsrL7PeQ/XWVt+Kd9Oqot2a2WbNmsLS0RG5ursF4bm4uXF1dTR7j6upqsl6r1Zq8KgsANjY2sLGxMRq3srJSxG+qmmhsmZlX2ZhX2ZhX2RpbXqDxZX7UvDU51qLWZ3lE1tbW6NGjB/bt26cfKy8vx759++Dr62vyGF9fX4N64N5l7MrqiYiIiEjZ6q2ZBYDQ0FCsWLECq1evRlpaGv72t7/h5s2b+qcbTJ8+HaNHj9bXjx8/HpcuXcJHH32Ec+fOYdmyZdi4cSMmT55cXxGIiIiIqB7V6z2zgYGB+OWXXzBjxgzk5OTAy8sLu3fv1n/IKzs7G1lZWfp6Dw8P7Ny5E5MnT8bixYvh5uaGlStX8rFcRERERI1UvX8AbMKECZgwYYLJfTExMUZjffr0QVJS0mNeFRERERGZg3q9zYCIiIiI6FGwmSUiIiIis8VmloiIiIjMFptZIiIiIjJbbGaJiIiIyGyxmSUiIiIis8VmloiIiIjMFptZIiIiIjJbbGaJiIiIyGyxmSUiIiIis8VmloiIiIjMFptZIiIiIjJbbGaJiIiIyGyp63sBT5qIAACKiopgZWVVz6t5MsrKylBSUoLCwsJGkZl5lY15lY15la2x5QUaX+a6yltYWAjg976tKo2umc3PzwcAeHh41PNKiIiIiKgqRUVFcHBwqLKm0TWzTk5OAICsrKyHfnGUorCwEO7u7rhy5Qq0Wm19L+exY15lY15lY15la2x5gcaXua7yigiKiorQokWLh9Y2umbWwuLebcIODg6N4jfV/bRabaPKzLzKxrzKxrzK1tjyAo0vc13kre5FR34AjIiIiIjMFptZIiIiIjJbja6ZtbGxQVhYGGxsbOp7KU9MY8vMvMrGvMrGvMrW2PICjS9zfeRVSXWeeUBERERE1AA1uiuzRERERKQcbGaJiIiIyGyxmSUiIiIis8VmloiIiIjMliKb2evXryM4OBharRaOjo545513UFxcXOUxIoIZM2ZAp9NBo9HA398f58+fN6h599130a5dO2g0Gjg7O2Po0KE4d+7c44xiUkREBNq0aYMmTZrAx8cHJ06cqLL+4MGD6N69O2xsbNC+fXvExMQY7O/Tpw9UKpXR9sorrzzGFNVXk7zZ2dkICgqCp6cnLCws8MEHHxjVKCnvli1b0L9/fzg7O0Or1cLX1xd79uwxqFFS3vj4ePj5+aFp06bQaDR4+umnsXDhQoMaJeW935EjR6BWq+Hl5WUw3tDzAjXLfPDgQZN5cnJy9DUNPXNNv8elpaX4+OOP0bp1a9jY2KBNmzaIiorS71dS3rfeestkli5duuhrlJQXANatW4du3brB1tYWOp0Ob7/9NvLz8/X7lZY3IiICnTp1gkajQceOHbFmzRqD/Y8lryjQgAEDpFu3bnLs2DE5fPiwtG/fXkaOHFnlMXPnzhUHBwfZtm2bpKSkyJAhQ8TDw0Nu3bqlr1m+fLkcOnRIMjMzJTExUQYPHizu7u5y586dxx1JLzY2VqytrSUqKkrOnj0r48aNE0dHR8nNzTVZf+nSJbG1tZXQ0FBJTU2VpUuXiqWlpezevVtfk5+fL9nZ2frtzJkzYmlpKdHR0U8oVeVqmjczM1MmTpwoq1evFi8vL5k0aZJRjZLyTpo0ST7//HM5ceKEZGRkyPTp08XKykpOnTqlr1FS3lOnTsk333wjZ86ckczMTFm7dq3Y2trK8uXL9TVKylvhxo0b0rZtW/nzn/8s3bp1M9jXkPOK1DzzgQMHBICkp6cb5Lp7966+piFnrs33eMiQIeLj4yN79+6VzMxM+f777yU+Pl6/X0l5CwoKDLJcuXJFnJycJCwsTF+jpLzx8fFiYWEhixcvlkuXLsnhw4elS5cuMmzYMH2NkvIuW7ZM7O3tJTY2Vi5evCjr168XOzs72bFjh77mceRVXDObmpoqAOTkyZP6sbi4OFGpVPLzzz+bPKa8vFxcXV1l3rx5+rGCggKxsbGR9evXV3qulJQUASAXLlyouwAP4e3tLe+//77+9d27d6VFixYyZ84ck/UfffSRdOnSxWAsMDBQAgICKj3HwoULxd7eXoqLi+tm0Y+gpnnv17t3b5PN7IOUkrdC586dJTw8vNL9Sss7bNgwGTVqVKX7lZA3MDBQPvnkEwkLCzNqZh/UkPKK1DxzRTN748aNap+jIWWuad64uDhxcHCQ/Pz8ap/DnPM+aOvWraJSqeTy5cuV1phz3nnz5knbtm0NxpYsWSItW7as9BzmnNfX11emTJliMBYaGip+fn6VnqMu8iruNoOjR4/C0dERPXv21I/5+/vDwsICx48fN3lMZmYmcnJy4O/vrx9zcHCAj48Pjh49avKYmzdvIjo6Gh4eHnB3d6/bEJW4ffs2EhMTDdZpYWEBf3//Std59OhRg3oACAgIqLQeAFatWoU33ngDTz31VN0svJZqk7c2lJS3vLwcRUVFcHJyqrRGSXmTkpLw/fffo3fv3pXWmHve6OhoXLp0CWFhYdU6T0PJCzza99jLyws6nQ79+/fHkSNHqqxtKJlrk3fHjh3o2bMnvvjiC7Rs2RKenp6YMmUKbt26Vel5zDnvg1atWgV/f3+0bt26yhpzzevr64srV65g165dEBHk5uZi06ZNePnllys9jznnLS0tRZMmTQzGNBoNTpw4gbKyMpPH1EVexTWzOTk5aN68ucGYWq2Gk5OTwT1XDx4DAC4uLgbjLi4uRscsW7YMdnZ2sLOzQ1xcHPbu3Qtra+s6TFC5a9eu4e7du9VaZ4WcnByT9YWFhSb/sjxx4gTOnDmDkJCQult4LdUmb00pLe/8+fNRXFyMESNGmNyvlLxubm6wsbFBz5498f7771eax9zznj9/HtOmTcPXX38NtVr90HM0pLxA7TLrdDpERkZi8+bN2Lx5M9zd3dGnTx+cOnXKZH1DylybvJcuXUJ8fDzOnDmDrVu3YtGiRdi0aRPee+89k/Xmnvd+V69eRVxcXJVZzD2vn58f1q1bh8DAQFhbW8PV1RUODg6IiIgwWW/ueQMCArBy5UokJiZCRJCQkICVK1eirKwM165dM6qvq7xm08xOmzbN5A3D929P4sNYwcHBSEpKwqFDh+Dp6YkRI0bg119/feznfVJWrVqFrl27wtvbu76X8kQoKe8333yD8PBwbNy40eg/dBWUkvfw4cNISEhAZGQkFi1ahPXr15usM+e8d+/eRVBQEMLDw+Hp6VmtY8w5b4WOHTvi3XffRY8ePdCrVy9ERUWhV69eRh/0q2DumcvLy6FSqbBu3Tp4e3vj5ZdfxpdffonVq1ebvOBg7nnvt3r1ajg6OuLVV1+ttMbc86ampmLSpEmYMWMGEhMTsXv3bly+fBnjx483WW/ueT/99FMMHDgQL7zwAqysrDB06FCMGTMGwL2rug+qq7xm08x++OGHSEtLq3Jr27YtXF1dkZeXZ3DsnTt3cP36dbi6upqcu2I8NzfXYDw3N9foGAcHB3To0AEvvfQSNm3ahHPnzmHr1q11mLRyzZo1g6WlZbXWWcHV1dVkvVarhUajMRi/efMmYmNj8c4779TtwmupNnlrQkl5Y2NjERISgo0bNxrdVlJBSXk9PDzQtWtXjBs3DpMnT8bMmTONasw9b1FRERISEjBhwgSo1Wqo1WrMmjULKSkpUKvV2L9/v0F9Q8sL1N2fYW9vb1y4cMFovKFlrk1enU6Hli1bwsHBQT/WqVMniAh++ukng1ol5K0gIoiKisKbb75Z6bubSsg7Z84c+Pn5YerUqXj22WcREBCAZcuWISoqCtnZ2Qa1Ssir0WgQFRWFkpISXL58GVlZWWjTpg3s7e3h7OxsUFuXec2mmXV2dsbTTz9d5WZtbQ1fX18UFBQgMTFRf+z+/ftRXl4OHx8fk3N7eHjA1dUV+/bt048VFhbi+PHj8PX1rXRNcu8DdCgtLa27oFWwtrZGjx49DNZZXl6Offv2VbpOX19fg3oA2Lt3r8n6f//73ygtLcWoUaPqduG1VJu8NaGUvOvXr8fYsWOxfv36Kh9topS8DyovLzf5Z9Dc82q1Wpw+fRrJycn6bfz48ejYsSOSk5ON/j5raHmBuvseJycnQ6fTGY03tMy1yevn54erV68aPD4yIyMDFhYWcHNzM6hVQt4Khw4dwoULF6psZJSQt6SkxOiKpKWlJYB7PcT9lJC3gpWVFdzc3GBpaYnY2FgMGjTI6OtQp3lr/dGxBmzAgAHy3HPPyfHjxyU+Pl46dOhg9Giujh07ypYtW/Sv586dK46OjrJ9+3b54YcfZOjQoQaP5rp48aLMnj1bEhIS5Mcff5QjR47I4MGDxcnJ6aGP1alLsbGxYmNjIzExMZKamip//etfxdHRUXJyckREZNq0afLmm2/q6ysezTV16lRJS0uTiIgIo0dzVXjxxRclMDDwiWWpjprmFRFJSkqSpKQk6dGjhwQFBUlSUpKcPXvWaG4l5F23bp2o1WqJiIgweNRJQUGB0dxKyPuvf/1LduzYIRkZGZKRkSErV64Ue3t7+fjjj43mVkLeB1X1NIOGmFek5pkXLlwo27Ztk/Pnz8vp06dl0qRJYmFhId99953R3A0xc03zFhUViZubmwwfPlzOnj0rhw4dkg4dOkhISIjR3ErIW2HUqFHi4+NT5dxKyBsdHS1qtVqWLVsmFy9elPj4eOnZs6d4e3sbza2EvOnp6bJ27VrJyMiQ48ePS2BgoDg5OUlmZqbR3HWZV5HNbH5+vowcOVLs7OxEq9XK2LFjpaioyKAGgMEzzcrLy+XTTz8VFxcXsbGxkX79+kl6erp+/88//ywDBw6U5s2bi5WVlbi5uUlQUJCcO3fuScXSW7p0qbRq1Uqsra3F29tbjh07pt83ZswY6d27t0H9gQMHxMvLS6ytraVt27Ymn+V27tw5ASDffvvtY159zdU0LwCjrXXr1gY1Ssnbu3dvk3nHjBljMKdS8i5ZskS6dOkitra2otVq5bnnnpNly5YZPINURDl5H1RZM9uQ84rULPPnn38u7dq1kyZNmoiTk5P06dNH9u/fbzRnQ85c0+9xWlqa+Pv7i0ajETc3NwkNDZWSkhKDGiXlLSgoEI1GI1999VWlcyop75IlS6Rz586i0WhEp9NJcHCw/PTTTwY1SsmbmpoqXl5eotFoRKvVytChQ032SXWdVyXywHVuIiIiIiIzYTb3zBIRERERPYjNLBERERGZLTazRERERGS22MwSERERkdliM0tEREREZovNLBERERGZLTazRERERGS22MwSEd3n4MGDUKlUKCgoeKLnjYmJgaOj4yPNcfnyZahUKiQnJ1da8yTyVWcdRER1hc0sETUaKpWqym3mzJn1vUQiIqohdX0vgIjoScnOztb/esOGDZgxYwbS09P1Y3Z2dkhISKjxvLdv34a1tXWdrJGIiGqGV2aJqNFwdXXVbw4ODlCpVAZjdnZ2+trExET07NkTtra26NWrl0HTO3PmTHh5eWHlypXw8PBAkyZNAAAFBQUICQmBs7MztFot/vSnPyElJUV/XEpKCvr27Qt7e3totVr06NHDqHnes2cPOnXqBDs7OwwYMMCgAS8vL8esWbPg5uYGGxsbeHl5Yffu3VVm3rVrFzw9PaHRaNC3b19cvny5yvqgoCAEBgYajJWVlaFZs2ZYs2YNAGD37t148cUX4ejoiKZNm2LQoEG4ePFipXOauoVi27ZtUKlUBmPbt29H9+7d0aRJE7Rt2xbh4eG4c+dOleslImIzS0Rkwscff4wFCxYgISEBarUab7/9tsH+CxcuYPPmzdiyZYv+3tDXX38deXl5iIuLQ2JiIrp3745+/frh+vXrAIDg4GC4ubnh5MmTSExMxLRp02BlZaWfs6SkBPPnz8fatWvxv//9D1lZWZgyZYp+/+LFi7FgwQLMnz8fP/zwAwICAjBkyBCcP3/eZIYrV67gtddew+DBg5GcnIyQkBBMmzatytzBwcH4z3/+g+LiYv3Ynj17UFJSgmHDhgEAbt68idDQUCQkJGDfvn2wsLDAsGHDUF5eXv0v8AMOHz6M0aNHY9KkSUhNTcXy5csRExODzz77rNZzElEjIUREjVB0dLQ4ODgYjR84cEAAyHfffacf27lzpwCQW7duiYhIWFiYWFlZSV5enr7m8OHDotVq5ddffzWYr127drJ8+XIREbG3t5eYmJhK1wNALly4oB+LiIgQFxcX/esWLVrIZ599ZnDc888/L++9956IiGRmZgoASUpKEhGR6dOnS+fOnQ3q//73vwsAuXHjhsl1lJWVSbNmzWTNmjX6sZEjR0pgYKDJehGRX375RQDI6dOnTa7D1Nd669atcv8/Qf369ZPZs2cb1Kxdu1Z0Ol2l5yUiEhHhlVkiIhOeffZZ/a91Oh0AIC8vTz/WunVrODs761+npKSguLgYTZs2hZ2dnX7LzMzUvwUfGhqKkJAQ+Pv7Y+7cuUZvzdva2qJdu3YG5604Z2FhIa5evQo/Pz+DY/z8/JCWlmYyQ1paGnx8fAzGfH19q8ytVqsxYsQIrFu3DsC9q7Dbt29HcHCwvub8+fMYOXIk2rZtC61WizZt2gAAsrKyqpy7KikpKZg1a5bB127cuHHIzs5GSUlJreclIuXjB8CIiEy4/+3/ins7738b/amnnjKoLy4uhk6nw8GDB43mqrhfdObMmQgKCsLOnTsRFxeHsLAwxMbG6t++v/+cFecVkbqIUyPBwcHo3bs38vLysHfvXmg0GgwYMEC/f/DgwWjdujVWrFiBFi1aoLy8HM888wxu375tcj4LCwujHGVlZQavi4uLER4ejtdee83o+Ip7komITGEzS0RUB7p3746cnByo1Wr9lUpTPD094enpicmTJ2PkyJGIjo7WN7NV0Wq1aNGiBY4cOYLevXvrx48cOQJvb2+Tx3Tq1Ak7duwwGDt27NhDz9WrVy+4u7tjw4YNiIuLw+uvv65vtPPz85Geno4VK1bgj3/8IwAgPj6+yvmcnZ1RVFSEmzdv6v8T8OAzaLt374709HS0b9/+oesjIrofm1kiojrg7+8PX19fvPrqq/jiiy/g6emJq1evYufOnRg2bBi6dOmCqVOnYvjw4fDw8MBPP/2EkydP4i9/+Uu1zzF16lSEhYWhXbt28PLyQnR0NJKTk/W3BDxo/PjxWLBgAaZOnYqQkBAkJiYiJiamWucKCgpCZGQkMjIycODAAf34H/7wBzRt2hRfffUVdDodsrKyHvqhMh8fH9ja2uIf//gHJk6ciOPHjxutY8aMGRg0aBBatWqF4cOHw8LCAikpKThz5gz++c9/VmvNRNQ48Z5ZIqI6oFKpsGvXLrz00ksYO3YsPD098cYbb+DHH3+Ei4sLLC0tkZ+fj9GjR8PT0xMjRozAwIEDER4eXu1zTJw4EaGhofjwww/RtWtX7N69Gzt27ECHDh1M1rdq1QqbN2/Gtm3b0K1bN0RGRmL27NnVOldwcDBSU1PRsmVLg/t0LSwsEBsbi8TERDzzzDOYPHky5s2bV+VcTk5O+Prrr7Fr1y507doV69evN/oBFQEBAfjvf/+Lb7/9Fs8//zxeeOEFLFy4EK1bt67Weomo8VJJfdyQRURERERUB3hlloiIiIjMFptZIiIiIjJbbGaJiIiIyGyxmSUiIiIis8VmloiIiIjMFptZIiIiIjJbbGaJiIiIyGyxmSUiIiIis8VmloiIiIjMFptZIiIiIjJbbGaJiIiIyGyxmSUiIiIis/X/7W33EhW3nSMAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.ticker as ticker\n",
        "%matplotlib inline\n",
        "\n",
        "def precision_recall_curve_plot(y_test , pred_proba_c1):\n",
        "    # threshold ndarray와 이 threshold에 따른 정밀도, 재현율 ndarray 추출. \n",
        "    precisions, recalls, thresholds = precision_recall_curve( y_test, pred_proba_c1)\n",
        "    \n",
        "    # X축을 threshold값으로, Y축은 정밀도, 재현율 값으로 각각 Plot 수행. 정밀도는 점선으로 표시\n",
        "    plt.figure(figsize=(8,6))\n",
        "    threshold_boundary = thresholds.shape[0]\n",
        "    plt.plot(thresholds, precisions[0:threshold_boundary], linestyle='--', label='precision')\n",
        "    plt.plot(thresholds, recalls[0:threshold_boundary],label='recall')\n",
        "    \n",
        "    # threshold 값 X 축의 Scale을 0.1 단위로 변경\n",
        "    start, end = plt.xlim()\n",
        "    plt.xticks(np.round(np.arange(start, end, 0.1),2))\n",
        "    \n",
        "    # x축, y축 label과 legend, 그리고 grid 설정\n",
        "    plt.xlabel('Threshold value'); plt.ylabel('Precision and Recall value')\n",
        "    plt.legend(); plt.grid()\n",
        "    plt.show()\n",
        "    \n",
        "precision_recall_curve_plot( y_test, lr_clf.predict_proba(X_test)[:, 1] )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "f1 = f1_score(y_test, pred)\n",
        "print('F1 스코어 {0:.4f}'.format(f1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def get_clf_eval(y_test , pred):\n",
        "    confusion = confusion_matrix( y_test, pred)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    precision = precision_score(y_test , pred)\n",
        "    recall = recall_score(y_test , pred)\n",
        "    # F1 스코어 추가\n",
        "    f1 = f1_score(y_test,pred)\n",
        "    print('오차 행렬')\n",
        "    print(confusion)\n",
        "    # f1 score print 추가\n",
        "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f}, F1:{3:.4f}'.format(accuracy, precision, recall, f1))\n",
        "\n",
        "thresholds = [0.4 , 0.45 , 0.50 , 0.55 , 0.60]\n",
        "pred_proba = lr_clf.predict_proba(X_test)\n",
        "get_eval_by_threshold(y_test, pred_proba[:,1].reshape(-1,1), thresholds)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_curve\n",
        "\n",
        "# 레이블 값이 1일때의 예측 확률을 추출 \n",
        "pred_proba_class1 = lr_clf.predict_proba(X_test)[:, 1] \n",
        "\n",
        "fprs , tprs , thresholds = roc_curve(y_test, pred_proba_class1)\n",
        "# 반환된 임곗값 배열에서 샘플로 데이터를 추출하되, 임곗값을 5 Step으로 추출. \n",
        "# thresholds[0]은 max(예측확률)+1로 임의 설정됨. 이를 제외하기 위해 np.arange는 1부터 시작\n",
        "thr_index = np.arange(1, thresholds.shape[0], 5)\n",
        "print('샘플 추출을 위한 임곗값 배열의 index:', thr_index)\n",
        "print('샘플 index로 추출한 임곗값: ', np.round(thresholds[thr_index], 2))\n",
        "\n",
        "# 5 step 단위로 추출된 임계값에 따른 FPR, TPR 값\n",
        "print('샘플 임곗값별 FPR: ', np.round(fprs[thr_index], 3))\n",
        "print('샘플 임곗값별 TPR: ', np.round(tprs[thr_index], 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def roc_curve_plot(y_test , pred_proba_c1):\n",
        "    # 임곗값에 따른 FPR, TPR 값을 반환 받음. \n",
        "    fprs , tprs , thresholds = roc_curve(y_test ,pred_proba_c1)\n",
        "\n",
        "    # ROC Curve를 plot 곡선으로 그림. \n",
        "    plt.plot(fprs , tprs, label='ROC')\n",
        "    # 가운데 대각선 직선을 그림. \n",
        "    plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
        "    \n",
        "    # FPR X 축의 Scale을 0.1 단위로 변경, X,Y 축명 설정등   \n",
        "    start, end = plt.xlim()\n",
        "    plt.xticks(np.round(np.arange(start, end, 0.1),2))\n",
        "    plt.xlim(0,1); plt.ylim(0,1)\n",
        "    plt.xlabel('FPR( 1 - Specificity )'); plt.ylabel('TPR( Recall )')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "    \n",
        "roc_curve_plot(y_test, lr_clf.predict_proba(X_test)[:, 1] )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "pred_proba = lr_clf.predict_proba(X_test)[:, 1]\n",
        "roc_score = roc_auc_score(y_test, pred_proba)\n",
        "print('ROC AUC 값: {0:.4f}'.format(roc_score))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def get_clf_eval(y_test, pred=None, pred_proba=None):\n",
        "    confusion = confusion_matrix( y_test, pred)\n",
        "    accuracy = accuracy_score(y_test , pred)\n",
        "    precision = precision_score(y_test , pred)\n",
        "    recall = recall_score(y_test , pred)\n",
        "    f1 = f1_score(y_test,pred)\n",
        "    # ROC-AUC 추가 \n",
        "    roc_auc = roc_auc_score(y_test, pred_proba)\n",
        "    print('오차 행렬')\n",
        "    print(confusion)\n",
        "    # ROC-AUC print 추가\n",
        "    print('정확도: {0:.4f}, 정밀도: {1:.4f}, 재현율: {2:.4f},\\\n",
        "          F1: {3:.4f}, AUC:{4:.4f}'.format(accuracy, precision, recall, f1, roc_auc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0    500\n",
            "1    268\n",
            "Name: Outcome, dtype: int64\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score\n",
        "from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "diabetes_data = pd.read_csv('diabetes.csv')\n",
        "print(diabetes_data['Outcome'].value_counts())\n",
        "diabetes_data.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n"
          ]
        }
      ],
      "source": [
        "diabetes_data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "오차 행렬\n",
            "[[87 13]\n",
            " [22 32]]\n",
            "정확도: 0.7727, 정밀도: 0.7111, 재현율: 0.5926,          F1: 0.6465, AUC:0.8083\n"
          ]
        }
      ],
      "source": [
        "# 피처 데이터 세트 X, 레이블 데이터 세트 y를 추출. \n",
        "# 맨 끝이 Outcome 컬럼으로 레이블 값임. 컬럼 위치 -1을 이용해 추출 \n",
        "X = diabetes_data.iloc[:, :-1]\n",
        "y = diabetes_data.iloc[:, -1]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 156, stratify=y)\n",
        "\n",
        "# 로지스틱 회귀로 학습,예측 및 평가 수행. \n",
        "lr_clf = LogisticRegression(solver='liblinear')\n",
        "lr_clf.fit(X_train , y_train)\n",
        "pred = lr_clf.predict(X_test)\n",
        "pred_proba = lr_clf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "get_clf_eval(y_test , pred, pred_proba)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pred_proba_c1 = lr_clf.predict_proba(X_test)[:, 1]\n",
        "precision_recall_curve_plot(y_test, pred_proba_c1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "      <td>768.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.845052</td>\n",
              "      <td>120.894531</td>\n",
              "      <td>69.105469</td>\n",
              "      <td>20.536458</td>\n",
              "      <td>79.799479</td>\n",
              "      <td>31.992578</td>\n",
              "      <td>0.471876</td>\n",
              "      <td>33.240885</td>\n",
              "      <td>0.348958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.369578</td>\n",
              "      <td>31.972618</td>\n",
              "      <td>19.355807</td>\n",
              "      <td>15.952218</td>\n",
              "      <td>115.244002</td>\n",
              "      <td>7.884160</td>\n",
              "      <td>0.331329</td>\n",
              "      <td>11.760232</td>\n",
              "      <td>0.476951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.300000</td>\n",
              "      <td>0.243750</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>117.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.500000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.372500</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.250000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>127.250000</td>\n",
              "      <td>36.600000</td>\n",
              "      <td>0.626250</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
              "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
              "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
              "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
              "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
              "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
              "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
              "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
              "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
              "\n",
              "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
              "count  768.000000                768.000000  768.000000  768.000000  \n",
              "mean    31.992578                  0.471876   33.240885    0.348958  \n",
              "std      7.884160                  0.331329   11.760232    0.476951  \n",
              "min      0.000000                  0.078000   21.000000    0.000000  \n",
              "25%     27.300000                  0.243750   24.000000    0.000000  \n",
              "50%     32.000000                  0.372500   29.000000    0.000000  \n",
              "75%     36.600000                  0.626250   41.000000    1.000000  \n",
              "max     67.100000                  2.420000   81.000000    1.000000  "
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diabetes_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(diabetes_data['Glucose'], bins=100)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Glucose 0 건수는 5, 퍼센트는 0.65 %\n",
            "BloodPressure 0 건수는 35, 퍼센트는 4.56 %\n",
            "SkinThickness 0 건수는 227, 퍼센트는 29.56 %\n",
            "Insulin 0 건수는 374, 퍼센트는 48.70 %\n",
            "BMI 0 건수는 11, 퍼센트는 1.43 %\n"
          ]
        }
      ],
      "source": [
        "# 0값을 검사할 피처명 리스트 객체 설정\n",
        "zero_features = ['Glucose', 'BloodPressure','SkinThickness','Insulin','BMI']\n",
        "\n",
        "# 전체 데이터 건수\n",
        "total_count = diabetes_data['Glucose'].count()\n",
        "\n",
        "# 피처별로 반복 하면서 데이터 값이 0 인 데이터 건수 추출하고, 퍼센트 계산\n",
        "for feature in zero_features:\n",
        "    zero_count = diabetes_data[diabetes_data[feature] == 0][feature].count()\n",
        "    print('{0} 0 건수는 {1}, 퍼센트는 {2:.2f} %'.format(feature, zero_count, 100*zero_count/total_count))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# zero_features 리스트 내부에 저장된 개별 피처들에 대해서 0값을 평균 값으로 대체\n",
        "mean_zero_features = diabetes_data[zero_features].mean()\n",
        "diabetes_data[zero_features]=diabetes_data[zero_features].replace(0, mean_zero_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "오차 행렬\n",
            "[[90 10]\n",
            " [21 33]]\n",
            "정확도: 0.7987, 정밀도: 0.7674, 재현율: 0.6111,          F1: 0.6804, AUC:0.8433\n"
          ]
        }
      ],
      "source": [
        "X = diabetes_data.iloc[:, :-1]\n",
        "y = diabetes_data.iloc[:, -1]\n",
        "\n",
        "# StandardScaler 클래스를 이용해 피처 데이터 세트에 일괄적으로 스케일링 적용\n",
        "scaler = StandardScaler( )\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size = 0.2, random_state = 156, stratify=y)\n",
        "\n",
        "# 로지스틱 회귀로 학습, 예측 및 평가 수행. \n",
        "lr_clf = LogisticRegression()\n",
        "lr_clf.fit(X_train , y_train)\n",
        "pred = lr_clf.predict(X_test)\n",
        "pred_proba = lr_clf.predict_proba(X_test)[:, 1]\n",
        "\n",
        "get_clf_eval(y_test , pred, pred_proba)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "임곗값:  0.3\n",
            "오차 행렬\n",
            "[[67 33]\n",
            " [11 43]]\n",
            "정확도: 0.7143, 정밀도: 0.5658, 재현율: 0.7963\n",
            "임곗값:  0.33\n",
            "오차 행렬\n",
            "[[72 28]\n",
            " [12 42]]\n",
            "정확도: 0.7403, 정밀도: 0.6000, 재현율: 0.7778\n",
            "임곗값:  0.36\n",
            "오차 행렬\n",
            "[[76 24]\n",
            " [15 39]]\n",
            "정확도: 0.7468, 정밀도: 0.6190, 재현율: 0.7222\n",
            "임곗값:  0.39\n",
            "오차 행렬\n",
            "[[78 22]\n",
            " [16 38]]\n",
            "정확도: 0.7532, 정밀도: 0.6333, 재현율: 0.7037\n",
            "임곗값:  0.42\n",
            "오차 행렬\n",
            "[[84 16]\n",
            " [18 36]]\n",
            "정확도: 0.7792, 정밀도: 0.6923, 재현율: 0.6667\n",
            "임곗값:  0.45\n",
            "오차 행렬\n",
            "[[85 15]\n",
            " [18 36]]\n",
            "정확도: 0.7857, 정밀도: 0.7059, 재현율: 0.6667\n",
            "임곗값:  0.48\n",
            "오차 행렬\n",
            "[[88 12]\n",
            " [19 35]]\n",
            "정확도: 0.7987, 정밀도: 0.7447, 재현율: 0.6481\n",
            "임곗값:  0.5\n",
            "오차 행렬\n",
            "[[90 10]\n",
            " [21 33]]\n",
            "정확도: 0.7987, 정밀도: 0.7674, 재현율: 0.6111\n"
          ]
        }
      ],
      "source": [
        "thresholds = [0.3 , 0.33 ,0.36,0.39, 0.42 , 0.45 ,0.48, 0.50]\n",
        "pred_proba = lr_clf.predict_proba(X_test)\n",
        "get_eval_by_threshold(y_test, pred_proba[:,1].reshape(-1,1), thresholds )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "오차 행렬\n",
            "[[88 12]\n",
            " [19 35]]\n",
            "정확도: 0.7987, 정밀도: 0.7447, 재현율: 0.6481,          F1: 0.6931, AUC:0.8433\n"
          ]
        }
      ],
      "source": [
        "# 임곗값을 0.48로 설정한 Binarizer 생성\n",
        "binarizer = Binarizer(threshold=0.48)\n",
        "\n",
        "# 위에서 구한 lr_clf의 predict_proba() 예측 확률 array에서 1에 해당하는 컬럼값을 Binarizer변환. \n",
        "pred_th_048 = binarizer.fit_transform(pred_proba[:, 1].reshape(-1,1)) \n",
        "\n",
        "get_clf_eval(y_test , pred_th_048, pred_proba[:, 1])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python (Pyodide)",
      "language": "python",
      "name": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
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  },
  "nbformat": 4,
  "nbformat_minor": 4
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