numpy_basic.ipynb

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   "source": [
    "## 모듈과 패키지"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {
    "id": "ogw4TeaPYSSN"
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   "source": [
    "# 모듈은 변수, 함수, 클래스를 담고 있는 파일 (.py)\n",
    "# 패키지는 여러 모듈을 묶은 것 (폴더)\n",
    "# import 모듈\n",
    "# import 모듈1, 모듈2\n",
    "# 모듈.변수\n",
    "# 모듈.함수()\n",
    "# 모듈.클래스()"
   ]
  },
  {
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     "text": [
      "3.141592653589793\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "import math # math라는 모듈이 있어요\n",
    "print(math.pi)\n",
    "print(math.sqrt(4)) # sqrt는 제곱근 구해줌"
   ]
  },
  {
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     "text": [
      "3.141592653589793\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "import math as m # math를 가져오는 데 m 이라는 이름으로 가져온다\n",
    "print(m.pi)\n",
    "print(m.sqrt(4))"
   ]
  },
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     "text": [
      "3.141592653589793\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "from math import pi, sqrt\n",
    "print(pi)\n",
    "print(sqrt(4))"
   ]
  },
  {
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   "metadata": {
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     "text": [
      "3.141592653589793\n",
      "2.0\n",
      "1.2246467991473532e-16\n"
     ]
    }
   ],
   "source": [
    "from math import * # * 이 all 이란 뜻\n",
    "print(pi)\n",
    "print(sqrt(4))\n",
    "print(sin(pi))"
   ]
  },
  {
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   "execution_count": null,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt as s\n",
    "print(s(4))"
   ]
  },
  {
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     "text": [
      "3.141592653589793\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt as s, pi as p\n",
    "print(p)\n",
    "print(s(4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VHd7hv7kacpi"
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   "source": [
    "# import 패키지\n",
    "# import 패키지.모듈1\n",
    "# import 패키지.모듈1, 패키지.모듈2\n",
    "# 패키지.모듈.변수\n",
    "# 패키지.모듈.함수()\n",
    "# 패키지.모듈.클래스()"
   ]
  },
  {
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   "execution_count": null,
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     "timestamp": 1672662359738,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # numpy는 라이브러리(패키지와 모듈들을 모아놓음)다 (numerical python)\n",
    "a=np.array([1,2,3])\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 52,
     "status": "ok",
     "timestamp": 1672662359739,
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.32371815 -1.48108097 -0.55204499]\n",
      " [-0.19266021  1.60892551  0.86262146]\n",
      " [-0.32125926 -0.83544928 -0.0211097 ]]\n"
     ]
    }
   ],
   "source": [
    "import numpy.random\n",
    "print(numpy.random.randn(3,3)) # Normal distribution, (3,3)은 3행 3열 짜리를 의미"
   ]
  },
  {
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     "status": "ok",
     "timestamp": 1672662359740,
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.2859233  -0.62632203 -0.51923904]\n",
      " [ 0.07283621  0.47690659 -0.74598762]\n",
      " [ 1.12723361 -0.93710686 -1.15619722]]\n"
     ]
    }
   ],
   "source": [
    "import numpy.random as nr\n",
    "print(nr.randn(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 49,
     "status": "ok",
     "timestamp": 1672662359740,
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    "outputId": "b51066a2-e1fc-4065-e573-8c5d54a2848b"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.56514379 -0.29743155  1.79178661]\n",
      " [ 0.4978884   0.51160081 -0.7288657 ]\n",
      " [ 1.9877219  -0.3539639  -0.50884329]]\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import randn\n",
    "print(randn(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 48,
     "status": "ok",
     "timestamp": 1672662359741,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
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    },
    "id": "B2XqB_vAci3h",
    "outputId": "4cc55c37-02c2-410f-957e-834c77c3145d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.78062067 0.05715005 0.20498052]\n",
      " [0.56677711 0.31917149 0.35920579]\n",
      " [0.07544751 0.68242219 0.99885738]]\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import *\n",
    "print(rand(3,3)) # uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 47,
     "status": "ok",
     "timestamp": 1672662359741,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "EiIgr3Kgct87",
    "outputId": "16771288-7df0-458a-b5da-2c8951685c62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.31080171  0.79757886 -0.70272047]\n",
      " [-0.07085504  0.138533    0.0690993 ]\n",
      " [ 1.16447045 -0.27625625  1.28194661]]\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import randn as rn\n",
    "print(rn(3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 46,
     "status": "ok",
     "timestamp": 1672662359742,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
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    },
    "id": "9T02nwbLcywr",
    "outputId": "5cb649ab-87e9-4025-f5e1-66803ae23aae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.94127757  0.99413696  0.00900012]\n",
      " [ 0.13123027 -1.19683389  0.57258444]\n",
      " [ 0.82473785 -0.39945863 -1.71295469]]\n",
      "[[0.79351446 0.42696448 0.95315955]\n",
      " [0.23830058 0.81179781 0.6093667 ]\n",
      " [0.79896916 0.85615562 0.08059551]]\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import randn as rn, rand as r\n",
    "print(rn(3,3))\n",
    "print(r(3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RzMsP9hsc36l"
   },
   "source": [
    "## numpy 본격적으로"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {
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     "base_uri": "https://localhost:8080/"
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    "executionInfo": {
     "elapsed": 44,
     "status": "ok",
     "timestamp": 1672662359742,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "<class 'numpy.ndarray'>\n",
      "int64\n",
      "(4,)\n",
      "float64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.array([1,2,3,4])\n",
    "print(a)\n",
    "print(type(a))\n",
    "print(a.dtype) # data type\n",
    "print(a.shape)\n",
    "b=np.array([1,2, 3.1   , 4])\n",
    "print(b.dtype) # 하나라도 실수면 자동으로 실수 타입으로\n",
    "print(b)"
   ]
  },
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "executionInfo": {
     "elapsed": 19,
     "status": "error",
     "timestamp": 1674645541231,
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    },
    "id": "QRPA6o3EdDy5",
    "outputId": "46dab2fd-e751-4566-eb91-d55544f4a597"
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-eaecdd2e99d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m3\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[0;31m# 이 셀부터는 import numpy as np 는 실행 했다 치고 작성\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m# A = np.array([[1,2],[3,4,5]]) # 리스트와는 달리 이제는 행렬이라서 각 행에 해당하는 숫자의 개수가 같아야 함\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# 차원의 수\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2,3],[3,4,5]]) # 이 셀부터는 import numpy as np 는 실행 했다 치고 작성\n",
    "# A=np.array([[1,2],[3,4,5]]) # 리스트와는 달리 이제는 행렬이라서 각 행에 해당하는 숫자의 개수가 같아야 함\n",
    "print(A)\n",
    "print(A.shape)\n",
    "print(A.ndim) # 차원의 수\n",
    "print(A.size) # 전체 성분의 수"
   ]
  },
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     "timestamp": 1672662359743,
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     "text": [
      "[0. 0. 0. 0. 0.]\n",
      "[[0 0]\n",
      " [0 0]]\n",
      "[1. 1. 1. 1. 1.]\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "[3 5 7 9]\n",
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n",
      "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros(5))\n",
    "print(np.zeros_like(A))\n",
    "print(np.ones(5))\n",
    "print(np.zeros( (3,3) )) # 튜플을 입력으로! 그냥 np.zeros(3,3)은 안돼요\n",
    "print(np.arange(3,10,2)) # range랑 같은데 array로 만들어줌\n",
    "print(np.arange(0,1,0.1)) # 소수점 가능\n",
    "print(np.linspace(0,1,10, endpoint=False)) # 0 에서부터 1 포함(default) 10개로"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1672662359743,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "mEY_M0PPfRCz",
    "outputId": "0b10f4e0-55ee-472f-aa71-0fafed6cde43"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 7 9]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([1,2,3])\n",
    "b=np.array([4,5,6])\n",
    "c=a+b\n",
    "print(c) # 드디어! 양 옆으로 길어지는 게 아닌 벡터의 합이 나옴!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1672662359744,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "zRGH_ASMfY5q",
    "outputId": "c5d407f0-2a5a-4782-c805-d57ec6d24f97"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 7 9]\n",
      " [2 3 4]]\n",
      "[[-3 -3 -3]\n",
      " [ 0  1  2]]\n",
      "\n",
      "[[ 4 10 18]\n",
      " [ 1  2  3]]\n",
      "[[0.25 0.4  0.5 ]\n",
      " [1.   2.   3.  ]]\n",
      "[[16 25 36]\n",
      " [ 1  1  1]]\n"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2,3],[1,2,3]])\n",
    "B=np.array([[4,5,6],[1,1,1]])\n",
    "C=A+B\n",
    "D=A-B\n",
    "print(C) \n",
    "print(D)\n",
    "print()\n",
    "print(A*B) # 곱셈은? 성분끼리의 곱! (Hadamard product)\n",
    "print(A/B) # 나누기도 마찬가지\n",
    "print(B**2) # 제곱도 각 성분에 대해서 해준다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21,
     "status": "ok",
     "timestamp": 1672662359744,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "LDMR3HebgeIS",
    "outputId": "d4a1ec00-300d-4e20-c59a-3bb6cd17acb6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  4]\n",
      " [ 9 16]]\n",
      "[[ 7 10]\n",
      " [15 22]]\n"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2],[3,4]])\n",
    "B=np.array([[1,2],[3,4]])\n",
    "print(A*B)\n",
    "print(A@B) # 이게 진짜 행렬 곱하기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lwIW_G6ThRuW"
   },
   "source": [
    "## numpy 의 인덱싱 슬라이스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1672662359744,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "EvEXUodtlOyq",
    "outputId": "837b92dc-420e-4980-f04a-ce19a99d45de"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "9\n",
      "[2 3 4]\n",
      "[8 9]\n",
      "[1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "a=np.array([1,2,3,4,5,6,7,8,9]) \n",
    "# 인덱싱과 슬라이싱, list할 때와 동일!\n",
    "print(a[0]) \n",
    "print(a[1])\n",
    "print(a[-1])\n",
    "print(a[1:4])\n",
    "print(a[7:])\n",
    "print(a[:7])\n",
    "print(a[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672662359744,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "4PSLeE0flTMC",
    "outputId": "2cfd56d8-b2f3-450e-cd62-703fe99c62df"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[7 8 9]\n",
      "[[4 5 6]\n",
      " [7 8 9]]\n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "3\n",
      "3\n",
      "[[1, 2, 3, 4], [5, 6, 7, 8]]\n",
      "3\n",
      "[3 6 9]\n"
     ]
    }
   ],
   "source": [
    "# 행렬에 대한 인덱싱과 슬라이싱\n",
    "A=np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "print(A[0]) # 하나만 쓰면 행에 대한 인덱싱 (리스트 속 리스트 생각)\n",
    "print(A[-1])\n",
    "print(A[1:])\n",
    "print(A[:])\n",
    "print(A[0][2])\n",
    "print(A[0,2]) # 2차원 행렬도 동일한데, 리스트와 달리 이런 것도 됨\n",
    "B=[[1,2,3,4], [5,6,7,8]]\n",
    "print(B)\n",
    "print(B[0][2])\n",
    "# print(B[0,2]) # error!\n",
    "print(A[1,:]) # 1 행, 전부\n",
    "print(A[1,0:3:2])\n",
    "print(A[:,2]) # 전부, 2 열\n",
    "print(A[:][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1672662359745,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "y6Zg1lZ9ZIBe",
    "outputId": "665f32fe-663e-4340-b699-82a108d74a06"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "(2, 3, 4)\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# 3차원 행렬 인덱싱\n",
    "A=np.array([ [[0,1,2,3],[4,5,6,7],[8,9,10,11]] ,   \n",
    "             [[12,13,14,15],[16,17,18,19],[20,21,22,23]] ])\n",
    "print(A)\n",
    "print(A.shape) # 대괄호가 하나 늘어나면 왼쪽에 shape 값이 추가 된다.\n",
    "print(A[0,1,2])\n",
    "\n",
    "a=np.array([[[1,2,3,4]]])\n",
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 427,
     "status": "ok",
     "timestamp": 1672662360158,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "DBjH6loolzdS",
    "outputId": "d0f51ebd-6e61-4e56-9b74-f5849a73b683"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "[[False False  True False]\n",
      " [False  True False  True]]\n",
      "[3 3 3]\n",
      "[[  1   2 100   4]\n",
      " [  5 100   7 100]]\n",
      "[[1 2]\n",
      " [7 8]]\n",
      "[1 2]\n"
     ]
    }
   ],
   "source": [
    "# boolean 인덱싱\n",
    "a=[1,2,3,4,5,3,3]\n",
    "print(a==3) # 여러개 값 들어있는 리스트랑 3 달랑 하나랑 같냐? 다르다!\n",
    "A=np.array([[1,2,3,4],[5,3,7,3]])\n",
    "print(A==3) # 리스트와 달리 각 성분에 대해 비교해줌\n",
    "print(A[A==3]) # True, False가 담긴 행렬로 인덱싱 가능!!\n",
    "\n",
    "A[A==3] = 100\n",
    "print(A) # 그러면 이런 것도 가능하다! (3과 같은 애를 100으로 바꿔줘)\n",
    "\n",
    "A=np.array([[1,2],[3,4],[5,6],[7,8]])\n",
    "B=np.array([True, False, False, True]) # 참고로 그냥 리스트여도 됨\n",
    "print(A[B,:]) # 0행, 3행 슬라이싱\n",
    "\n",
    "b=np.array([1,2,3,4])\n",
    "print(b[[True,True,False,False]])\n",
    "c=[1,2,3,4]\n",
    "# c[[True,True,False,False]] # error!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 62,
     "status": "ok",
     "timestamp": 1672662360158,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "FzKn6wIJZAgn",
    "outputId": "3b426fa8-3d33-43ca-8964-70bef0dcc416"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "3\n",
      "[3 4 5]\n",
      "[[3 3 3]\n",
      " [4 4 4]]\n",
      "[1 2 3]\n",
      "(2, 2, 3)\n",
      "[[[1 2 3]\n",
      "  [4 5 6]]\n",
      "\n",
      " [[4 5 6]\n",
      "  [4 5 6]]]\n"
     ]
    }
   ],
   "source": [
    "# array로 인덱싱\n",
    "a=np.array([1,2,3,4,5])\n",
    "A=a[2]\n",
    "print(A)\n",
    "A=a[ np.array(2) ] # np.array를 안에다가?\n",
    "print(A)\n",
    "A=a[ np.array([2,3,4]) ]\n",
    "print(A)\n",
    "A=a[ np.array([[2,2,2],[3,3,3]]) ]\n",
    "print(A) # 인덱싱된 애들로 2행 3열짜리 행렬을 만든다\n",
    "\n",
    "a=[1,2,3]\n",
    "# a[ [1,1,1,1,2,2,2] ] # error!\n",
    "\n",
    "a=np.array([[1,2,3],[4,5,6]])\n",
    "print(a[0])\n",
    "A=a[ np.array([[0,1],[1,1]]) ]\n",
    "print(A.shape) # 예를 들어, a[0] = array([1,2,3])과 같이 1차원 데이터이므로 한 차원이 뒤에 늘어나서 2,2, \"3\" 이 된다!\n",
    "print(A) # segmentation 결과 그림 보여줄 때 사용!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 61,
     "status": "ok",
     "timestamp": 1672662360159,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "BBEF3i6xl1sK",
    "outputId": "70f48350-1c6d-41f3-c0c7-6d95e4259b03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [7 8]]\n",
      "(4, 2)\n",
      "2\n",
      "[2]\n",
      "[2]\n",
      "[2]\n",
      "[[3 4]\n",
      " [3 4]\n",
      " [5 6]\n",
      " [5 6]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2],[3,4],[5,6],[7,8]])\n",
    "print(A)\n",
    "print(A.shape)\n",
    "\n",
    "# 1. A[몇 번째 행이냐, 몇 번째 열이냐]\n",
    "print(A[0,1])\n",
    "# 2. A[ array(bool) ] => A와 같은 shape을 가지는 array형 bool이 어디에 True를 가지고 있냐\n",
    "print(A[ np.array([[False,True],[False,False],[False,False],[False,False]]) ])\n",
    "print(A[A==2])\n",
    "# 3. A[몇 번째 값에 True가 있냐, 몇 번째 값에 True가 있냐]\n",
    "print(A[ [True,False,False,False], [False,True] ])\n",
    "# 4. A[ array ] # 몇 번째 것을 어떻게 쌓을거냐\n",
    "print(A[ np.array([1,1,2,2,2]) ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QYBXKfCupHU9"
   },
   "source": [
    "## numpy의 여러 함수들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 56,
     "status": "ok",
     "timestamp": 1672662360159,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "0lfFqRGOpI-0",
    "outputId": "6e64dfdf-5d33-489b-db14-6c4bdada5ecf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.67088393  1.20670741  0.54234205]\n",
      " [ 1.36383223 -0.25169022  0.41783062]\n",
      " [ 0.47468166 -0.92600609  0.21809043]]\n",
      "[[0.89976423 0.07464855 0.03793748]\n",
      " [0.83516205 0.6430013  0.08906904]\n",
      " [0.56968633 0.28267234 0.7287293 ]]\n",
      "[[-1.67088393  1.20670741  0.54234205]]\n"
     ]
    }
   ],
   "source": [
    "A=np.random.randn(3,3) # 여긴 튜플로 안줘도 됨\n",
    "B=np.random.rand(3,3)\n",
    "print(A)\n",
    "print(B)\n",
    "print(A[A[:,0]<0,:]) # 0 번째 열이 음수인 행들"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 44,
     "status": "ok",
     "timestamp": 1672662360159,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "J6KxUXALpniU",
    "outputId": "fb2fa9d4-4482-4936-c9d7-c628a5c6a049"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.39310191  1.09891698 -1.14853052]\n",
      " [ 1.42179433 -0.71248915 -0.58522194]\n",
      " [-1.35936487 -0.97865628  0.31967606]]\n",
      "[[0.39310191 1.09891698 1.14853052]\n",
      " [1.42179433 0.71248915 0.58522194]\n",
      " [1.35936487 0.97865628 0.31967606]]\n",
      "[[0.6269784  1.04829241 1.07169516]\n",
      " [1.19239017 0.84409072 0.764998  ]\n",
      " [1.16591804 0.98927058 0.56539903]]\n",
      "[[1.48156937 3.0009142  0.3171024 ]\n",
      " [4.14455044 0.49042194 0.55698223]\n",
      " [0.25682384 0.37581575 1.37668173]]\n",
      "[[-0.93368638  0.09432513  0.13848331]\n",
      " [ 0.35191968 -0.33899059 -0.53576412]\n",
      " [ 0.30701758 -0.02157479 -1.1404471 ]]\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "[[ 0.  1. -1.]\n",
      " [ 1. -1. -1.]\n",
      " [-1. -1.  0.]]\n",
      "[[ 0.39  1.1  -1.15]\n",
      " [ 1.42 -0.71 -0.59]\n",
      " [-1.36 -0.98  0.32]]\n",
      "[[ 0.  1. -2.]\n",
      " [ 1. -1. -1.]\n",
      " [-2. -1.  0.]]\n",
      "[[ 1.  2. -1.]\n",
      " [ 2. -0. -0.]\n",
      " [-1. -0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "A=np.random.randn(3,3)\n",
    "print(A)\n",
    "print(np.abs(A))\n",
    "print(np.sqrt(np.abs(A)))\n",
    "print(np.exp(A))\n",
    "print(np.log(np.abs(A)))\n",
    "print(np.log(np.exp(1))) # np.exp(1) = e^1\n",
    "print(np.log10(10))\n",
    "print(np.log2(2))\n",
    "print(np.round(A)) # 반올림\n",
    "print(np.round(A,2)) # 소수점 둘째자리까지\n",
    "print(np.floor(A)) # 내림\n",
    "print(np.ceil(A)) # 올림"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 39,
     "status": "ok",
     "timestamp": 1672662360160,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "PFPjWX54qaXi",
    "outputId": "8ad58a56-7781-499d-d499-00029b177e3e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.49999999999999994\n",
      "0.5000000000000001\n",
      "0.9999999999999999\n",
      "-0.9999999958776927\n"
     ]
    }
   ],
   "source": [
    "print(np.sin(np.pi/6))\n",
    "print(np.cos(np.pi/3))\n",
    "print(np.tan(np.pi/4))\n",
    "print(np.tanh(-10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 37,
     "status": "ok",
     "timestamp": 1672662360160,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "huDfIsJoqdDa",
    "outputId": "cd2caa43-010a-43c8-d6df-b935da479861"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False False  True False False]\n",
      "[False False False False  True]\n"
     ]
    }
   ],
   "source": [
    "np.nan # not a number\n",
    "# np.log(-1)\n",
    "print(np.isnan([1,2,np.nan,3,4]))\n",
    "print(np.isinf([1,2,3,4,np.inf]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1672662360160,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "C9G0Rdhiqn3a",
    "outputId": "82abc814-a38e-4545-af25-da0bbba5d432"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.60111745  0.65358943  0.33652573 -0.45064509]\n",
      " [ 0.56531042 -0.72526529 -0.81906752  0.34428911]\n",
      " [-1.63712718 -0.93759457 -0.23680838 -1.58124809]]\n",
      "0.6535894288117479\n",
      "[0.56531042 0.65358943 0.33652573 0.34428911]\n",
      "[ 0.65358943  0.56531042 -0.23680838]\n",
      "-1.6371271793873285\n",
      "[-1.63712718 -0.93759457 -0.81906752 -1.58124809]\n",
      "[-0.60111745 -0.81906752 -1.63712718]\n",
      "1\n",
      "[1 0 0 1]\n",
      "[1 0 2]\n"
     ]
    }
   ],
   "source": [
    "A=np.random.randn(3,4)\n",
    "print(A)\n",
    "print(np.max(A))\n",
    "print(np.max(A,axis=0))\n",
    "print(np.max(A,axis=1)) # 1D array로 바꿔버림\n",
    "print(np.max(A,axis=0, keepdims=True))\n",
    "print(np.max(A,axis=1, keepdims=True)) # 3 행 1열 짜리 2D array\n",
    "print(np.min(A))\n",
    "print(np.min(A,axis=0))\n",
    "print(np.min(A,axis=1))\n",
    "print(np.argmax(A))\n",
    "print(np.argmax(A,axis=0)) # 각 열에서 가장 큰 애가 존재하는 인덱스\n",
    "print(np.argmax(A,axis=1)) # 각 행에서 가장 큰 애가 존재하는 인덱스"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1672662360161,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "5h8JeQ8bqsrv",
    "outputId": "0cf370c9-1cf9-4cda-83ff-b7af887a9745"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.65515746]\n",
      " [-0.44962029]\n",
      " [ 3.20124317]\n",
      " [ 0.68946336]\n",
      " [ 0.07725051]\n",
      " [ 1.18731823]]\n",
      "[[-0.65515746]\n",
      " [-0.44962029]\n",
      " [ 0.07725051]\n",
      " [ 0.68946336]\n",
      " [ 1.18731823]\n",
      " [ 3.20124317]]\n",
      "[[ 3.20124317]\n",
      " [ 1.18731823]\n",
      " [ 0.68946336]\n",
      " [ 0.07725051]\n",
      " [-0.44962029]\n",
      " [-0.65515746]]\n"
     ]
    }
   ],
   "source": [
    "a=np.random.randn(6,1)\n",
    "print(a)\n",
    "a_sorted=np.sort(a,axis=0)\n",
    "print(a_sorted)\n",
    "print(np.flipud(a_sorted))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1672662360162,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "KJUEZtB4rglS",
    "outputId": "9d3e5b96-ff4d-4f6c-d884-7a903e875eec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.75981859  0.36521311 -1.52186851 -0.59986822]\n",
      " [-1.33080073  0.05467037 -0.60351552 -2.19124035]\n",
      " [-1.13160495  1.79967378  0.57823896 -1.01418047]]\n",
      "-7.3551011293919935\n",
      "[-3.51634221 -4.07088624  0.23212732]\n",
      "[[-3.51634221]\n",
      " [-4.07088624]\n",
      " [ 0.23212732]]\n",
      "-0.6129250941159995\n",
      "[-0.87908555 -1.01772156  0.05803183]\n",
      "[[-0.87908555]\n",
      " [-1.01772156]\n",
      " [ 0.05803183]]\n",
      "1.0885848676263121\n"
     ]
    }
   ],
   "source": [
    "A=np.random.randn(3,4)\n",
    "print(A)\n",
    "print(np.sum(A))\n",
    "print(np.sum(A,axis=1))\n",
    "print(np.sum(A,axis=1,keepdims=True))\n",
    "print(np.mean(A))\n",
    "print(np.mean(A,axis=1))\n",
    "print(np.mean(A,axis=1,keepdims=True))\n",
    "print(np.std(A)) # standard deviation 표준 편차\n",
    "\n",
    "print(A.sum(axis=1, keepdims=True))\n",
    "print(A.mean(axis=1, keepdims=True))\n",
    "print(A.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 32,
     "status": "ok",
     "timestamp": 1672662360162,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "CmuensVlsERE",
    "outputId": "9d6122d1-9acb-40a5-ad6a-9c6ec18cf3e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 1 1 4 2 4 4 4 1 1 3]\n",
      "(12,)\n",
      "[[[3 4 1]\n",
      "  [1 4 2]]\n",
      "\n",
      " [[4 4 4]\n",
      "  [1 1 3]]]\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "A=np.random.randint(1,5,size=(12,)) # 1부터 5미만 12개 정수 (1 차원은 (N,) 과 같이 표현)\n",
    "print(A)\n",
    "print(A.shape)\n",
    "\n",
    "B=A.reshape(2,2,3)\n",
    "print(B)\n",
    "print(B.ndim) # 3 차원 행렬이다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30,
     "status": "ok",
     "timestamp": 1672662360162,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "VdXu_OZ1sgNK",
    "outputId": "f5d54411-75aa-4033-802a-30178110e1c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "[[9]]\n",
      "[[9]]\n",
      "(4, 6, 3)\n"
     ]
    }
   ],
   "source": [
    "a=np.array([1,2,3])\n",
    "b=np.array([2,2,1])\n",
    "print(np.sum(a*b))\n",
    "\n",
    "a=a.reshape(3,1)\n",
    "b=b.reshape(3,1)\n",
    "print(a.transpose()@b)\n",
    "print(a.T@b)\n",
    "\n",
    "A=np.random.randn(4,3,6)\n",
    "print(A.transpose(0,2,1).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 29,
     "status": "ok",
     "timestamp": 1672662360163,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "aYsTsVD7sjmj",
    "outputId": "3f037a1b-6d7f-481b-dccf-0420cf2981d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]\n",
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [15 16 17 18 19]]\n",
      "(4, 5)\n",
      "(2, 5, 2)\n",
      "(2, 2, 5)\n",
      "(1, 20)\n",
      "(20, 1)\n"
     ]
    }
   ],
   "source": [
    "A=np.arange(20)\n",
    "print(A)\n",
    "print(A.reshape(4,5))\n",
    "print(A.reshape(4,-1).shape) # 4개 행이 될 수 있도록 열의 수를 맞춰라\n",
    "print(A.reshape(2,5,-1).shape)\n",
    "print(A.reshape(2,-1,5).shape)\n",
    "print(A.reshape(1,-1).shape) # 2차원 행 벡터\n",
    "print(A.reshape(-1,1).shape) # 2차원 열 벡터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1672662360163,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "VFBBHxp2zFyr",
    "outputId": "8e770adf-89b6-49c6-f1db-641e45ec3e5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4, 5, 6)\n",
      "(4, 5, 6)\n",
      "(2, 3, 4, 5)\n",
      "(2, 3, 4, 5)\n",
      "(3, 4, 6)\n",
      "(3, 4, 6)\n"
     ]
    }
   ],
   "source": [
    "x=np.random.randn(2,3,4,5,6)\n",
    "print(x[1,2,:,:,:].shape) \n",
    "print(x[1,2,...].shape) # x[1, 2, …] 는 x[1, 2, :, :, :] 와 같습니다.\n",
    "print(x[:,:,:,:,3].shape)\n",
    "print(x[...,3].shape) # x[…, 3] 는 x[:, :, :, :, 3] 와 같습니다.\n",
    "print(x[1,:,:,3,:].shape)\n",
    "print(x[1,...,3,:].shape) # x[1, …, 3, :] 는 x[1, :, :, 3, :] 와 같습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 25,
     "status": "ok",
     "timestamp": 1672662360163,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "VsBKRAiJzbBz",
    "outputId": "c625e82e-844d-4186-bd72-084f9f48c278"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 0. 0.]\n",
      " [1. 1. 0. 0.]]\n",
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n",
      "[[1. 1. 0. 0.]\n",
      " [1. 1. 0. 0.]]\n",
      "[[1. 1.]\n",
      " [1. 1.]\n",
      " [0. 0.]\n",
      " [0. 0.]]\n",
      "[[[1. 1.]\n",
      "  [1. 1.]]\n",
      "\n",
      " [[0. 0.]\n",
      "  [0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "A=np.ones((3,4))\n",
    "B=np.zeros((3,4))\n",
    "C=np.vstack([A,B])\n",
    "D=np.hstack([A,B])\n",
    "E=np.block([[A],[B]])\n",
    "F=np.block([A,B])\n",
    "G=np.block([ [[A]], [[B]] ])\n",
    "\n",
    "print(C)\n",
    "print(D)\n",
    "print(E)\n",
    "print(F)\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1672662360163,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "EOsKnQTvzeAL",
    "outputId": "279b4a54-b754-473e-8ab3-d4e27eb64507"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1, 3, 1, 1, 4, 1)\n",
      "(3, 4)\n",
      "(1, 3, 4, 1)\n"
     ]
    }
   ],
   "source": [
    "A = np.random.randn(1,1,1,3,1,1,4,1)\n",
    "# print(A)\n",
    "print(A.shape)\n",
    "print(A.squeeze().shape)\n",
    "print(A.squeeze(axis=(0,2,4,5)).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1672662360164,
     "user": {
      "displayName": "혁펜하임",
      "userId": "13709511177929718136"
     },
     "user_tz": -540
    },
    "id": "fgFgG48p02YK",
    "outputId": "17669d2d-184c-4358-9707-93ee22e59fd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 4)\n",
      "(3, 4, 1)\n",
      "(3, 1, 4)\n",
      "(1, 3, 4)\n",
      "(3, 4, 1)\n",
      "(3, 1, 4)\n",
      "(1, 3, 4)\n",
      "(3, 4, 1)\n",
      "(3, 1, 4)\n"
     ]
    }
   ],
   "source": [
    "A = np.random.randn(3,4)\n",
    "A1 = A[np.newaxis,...] # np.newaxis is None <- True\n",
    "print(A1.shape)\n",
    "A2 = A[...,np.newaxis]\n",
    "print(A2.shape)\n",
    "A3 = A[:,np.newaxis,:]\n",
    "print(A3.shape)\n",
    "A1 = np.expand_dims(A,axis=0)\n",
    "print(A1.shape)\n",
    "A2 = np.expand_dims(A,axis=2)\n",
    "print(A2.shape)\n",
    "A3 = np.expand_dims(A,axis=1)\n",
    "print(A3.shape)\n",
    "A1 = A.reshape(1,3,4)\n",
    "print(A1.shape)\n",
    "A2 = A.reshape(3,4,1)\n",
    "print(A2.shape)\n",
    "A3 = A.reshape(3,1,4)\n",
    "print(A3.shape)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyNjRfOZpQnp6ObWNotawq9w",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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