1_2_numpy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/1_2_numpy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IO3y50q16c1R"
      },
      "source": [
        "### Numpy ndarray 개요"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8Dnnbi496c1U"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s3wwZ7iM6c1W",
        "outputId": "269c457c-075e-4970-aa2e-343e795024ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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],\n",
        "                  [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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XN-t-kh16c1X",
        "outputId": "19f65319-e1bd-465a-c4c2-845a7a4438d6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array1: 1차원, array2: 2차원, array3: 2차원\n",
            "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))\n",
        "print(f'array1: {array1.ndim}차원, array2: {array2.ndim}차원, array3: {array3.ndim}차원')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "array3"
      ],
      "metadata": {
        "id": "-EMwwLqT8_Hu",
        "outputId": "48601a1a-f5cf-4dcb-96be-bf5328ec745a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2, 3]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ClSXXWoy6c1Y",
        "outputId": "7248ed44-8af9-4b88-ead1-eeea69a78cf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'list'>\n",
            "<class 'numpy.ndarray'>\n",
            "[1 2 3] int64\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": null,
      "metadata": {
        "id": "PjBZdH_k6c1Y",
        "outputId": "504b7ee5-b8bd-441d-ba1f-c04c31ac48e4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['1' '2' 'test'] <U21\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": null,
      "metadata": {
        "id": "Vve6M1wQ6c1Z",
        "outputId": "1b73df94-9513-4896-d696-55222c5e73d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdioYClg6c1Z"
      },
      "source": [
        "### ndarray를 편리하게 생성하기 - arange, zeros, ones"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QgSv2SCe6c1a",
        "outputId": "8507bd86-c17e-43cc-f2db-54a9840e23cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3 4 5 6 7 8 9]\n",
            "int64 (10,)\n"
          ]
        }
      ],
      "source": [
        "sequence_array = np.arange(10)\n",
        "print(sequence_array)\n",
        "print(sequence_array.dtype, sequence_array.shape)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bRqsuix86c1a",
        "outputId": "954b11c8-8502-4f3e-8aa5-a7d76dd902f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "_HFLg2_d6c1b"
      },
      "source": [
        "### reshape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HyhVUQco6c1b",
        "outputId": "8deb6533-2024-477c-9f20-b5e9e18e2479",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "FYL9vZpc6c1b",
        "outputId": "19bcfeed-948e-42ef-a296-9b0c6e495a5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-a40469ec5825>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\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": null,
      "metadata": {
        "id": "4i-CQ0TW6c1b",
        "outputId": "858456e2-500b-4e6d-ee46-40f1cf0dbb2b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
        "\n",
        "array2 = array1.reshape(-1,5)\n",
        "print('array2 shape:',array2.shape)\n",
        "\n",
        "array3 = array1.reshape(5,-1)\n",
        "print('array3 shape:',array3.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ysFmTv4a6c1b",
        "outputId": "cf2cad94-455b-4987-ebca-7fb668a6ea57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188
        }
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-19-07da9760475e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0marray1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0marray4\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\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": null,
      "metadata": {
        "id": "Gd0Vn4fS6c1b"
      },
      "outputs": [],
      "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": "markdown",
      "metadata": {
        "id": "fdkpoppe6c1c"
      },
      "source": [
        "### indexing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQ3yavBD6c1c"
      },
      "source": [
        "* 단일값 추출"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X2gcqBa-6c1c",
        "outputId": "298c0123-cb66-47d0-e23b-93695e7efc6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "array1: [1 2 3 4 5 6 7 8 9]\n",
            "value: 3\n",
            "<class 'numpy.int64'>\n"
          ]
        }
      ],
      "source": [
        "# 1에서 부터 9 까지의 1차원 ndarray 생성\n",
        "array1 = np.arange(start=1, stop=10)\n",
        "print('array1:',array1)\n",
        "# index는 0 부터 시작하므로 array1[2]는 3번째 index 위치의 데이터 값을 의미\n",
        "value = array1[2]\n",
        "print('value:',value)\n",
        "print(type(value))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i7zXoBPM6c1c",
        "outputId": "3c909194-a167-4ba7-b527-510892217c1e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "맨 뒤의 값: 9 , 맨 뒤에서 두번째 값: 8\n"
          ]
        }
      ],
      "source": [
        "print('맨 뒤의 값:',array1[-1], ', 맨 뒤에서 두번째 값:',array1[-2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFGObEVp6c1c",
        "outputId": "ff68a942-1a50-489e-d4f4-b3ae1cef2dad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "5MZgVrVX6c1c",
        "outputId": "8c4f9ce4-64df-4ac4-a97a-14f6852fca48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "NpgyA_fA6c1c"
      },
      "source": [
        "* Slicing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VtKraB4S6c1c",
        "outputId": "eb7ec28d-5559-4628-c4c7-38aa05d3802f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "4GhNCAhS6c1d",
        "outputId": "1310fdc1-d861-4111-d946-097880010c5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "r8B9d7ZU6c1d",
        "outputId": "81f535de-bbb0-491f-c94f-c123cb2af6d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
        "array2d = 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": null,
      "metadata": {
        "id": "FaVKbwyT6c1d",
        "outputId": "b11f1be7-d0db-43d8-96cc-f91a855a719e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "pxBhES9c6c1d"
      },
      "source": [
        "* fancy indexing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bvZOULPG6c1d",
        "outputId": "9c043d14-ef1d-4f5f-d668-b36f79a209ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "MybNaR656c1d"
      },
      "source": [
        "* Boolean indexing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OwgICKH26c1d",
        "outputId": "104877c2-029f-4c5d-f938-b553fd760879",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "mJXJZ44O6c1d",
        "outputId": "6243fa17-9416-45b6-f5cb-04399e335b23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, False, False,  True,  True,  True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "array1d > 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aVKb0GUm6c1d",
        "outputId": "deabc4a6-db41-473f-a35b-30174fe84a66",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "Cy0297jX6c1e",
        "outputId": "7c488b63-2b84-4b26-8eda-a1f0cd04a46a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "V6sLMJ5-6c1e"
      },
      "source": [
        "### 행렬의 정렬 – sort( )와 argsort( )\n",
        "\n",
        "* 행렬 정렬"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "laoHwpn46c1e",
        "outputId": "cf53f19f-415d-46ea-ce13-a5b3a417af6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "euBFEhe-6c1e",
        "outputId": "7a8373c0-2df2-44f3-df74-b276d473a36a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "1gy92WHw6c1e",
        "outputId": "6efe6314-bf4e-4b8e-b125-a40f9f98ba3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "로우 방향으로 정렬:\n",
            " [[ 7  1]\n",
            " [ 8 12]]\n",
            "컬럼 방향으로 정렬:\n",
            " [[ 8 12]\n",
            " [ 1  7]]\n"
          ]
        }
      ],
      "source": [
        "array2d = np.array([[8, 12],\n",
        "                   [7, 1 ]])\n",
        "\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('컬럼 방향으로 정렬:\\n', sort_array2d_axis1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksxRMbEP6c1e"
      },
      "source": [
        "* 정렬 행렬의 인덱스 반환"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B5aKxBDZ6c1k",
        "outputId": "95491966-7ead-4b08-a015-c24a2fca7ddd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "TJmaHOKX6c1k",
        "outputId": "232df98a-e286-43de-e216-6cc0043ba4c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "행렬 내림차순 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 내림차순 정렬 시 원본 행렬의 인덱스:', sort_indices_desc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "i1V1_dMb6c1k",
        "outputId": "174d25e9-b713-46e6-f9ed-ae5673cb4a65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": "markdown",
      "metadata": {
        "id": "zWDV0-tW6c1k"
      },
      "source": [
        "### 선형대수 연산 – 행렬 내적과 전치 행렬 구하기\n",
        "\n",
        "* 행렬 내적"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iye_hY8A6c1k",
        "outputId": "986333bb-bf7f-4841-a0d3-9a4de9e38de5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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",
        "\n",
        "dot_product = np.dot(A, B)\n",
        "print('행렬 내적 결과:\\n', dot_product)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oDSsw_Z6c1l"
      },
      "source": [
        "* 전치 행렬"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sa5KHPjV6c1l",
        "outputId": "91185a16-96a3-4430-cf45-5d807146c989",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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": null,
      "metadata": {
        "id": "6HnlDXTV6c1l"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "01XHtEh26c1l"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}