1. numpy.ipynb

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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt  # Visualize\n",
    "import seaborn as sns  # Visualize"
   ],
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   "source": [
    "# numpy\n",
    "## numpy에 관한 정보\n",
    "\n",
    "- Travis Oliphant가 2005년에 만들었다.\n",
    "- 선형대수는 BLAS와 LaPack라이브러리를 바탕으로 구현했다.\n",
    "- Numerical Python을 의미한다.\n",
    "\n",
    "## 왜 numpy인가?\n",
    "\n",
    "파이썬에서 연속된 자료를 다룰 때는 list(띄엄띄엄 저장)를 사용한다.\n",
    "근데 list특성상 많은 자료를 한꺼번에 다루기에는 너무 느리다\n",
    "\n",
    "그래서 파이썬에서 배열을 쓰고자 할 때 쓰는 것이 바로 numpy이다.\n",
    "\n",
    "numpy는 단순 배열(연속적으로 저장)을 구현해 놓은 것 외에도, 선형 대수, 행렬 연산과 같은 다양한 기능을 제공한다.\n",
    "\n",
    "## 사용\n",
    "`import numpy as np`\n",
    "\n",
    "np로 이름을 짓는 것은 관습이다."
   ]
  },
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   "cell_type": "markdown",
   "source": [
    "## list vs ndarray\n",
    "\n",
    "1. 메모리, 속도\n",
    "2. 연산"
   ],
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     "text": [
      "<class 'list'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "list_data = list(range(1000000))\n",
    "ndarray_data = np.arange(1000000)\n",
    "\n",
    "print(type(list_data))\n",
    "print(type(ndarray_data))"
   ],
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     "text": [
      "CPU times: user 81.4 ms, sys: 19.2 ms, total: 101 ms\n",
      "Wall time: 101 ms\n",
      "CPU times: user 1.63 ms, sys: 93 µs, total: 1.73 ms\n",
      "Wall time: 1.33 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([      0,       2,       4, ..., 1999994, 1999996, 1999998])"
     },
     "execution_count": 3,
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   ],
   "source": [
    "%time [i * 2 for i in list_data]\n",
    "%time ndarray_data * 2"
   ],
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   "source": [
    "3. 모양\n",
    "4. 원소"
   ],
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     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "list_shape = [1, 2, 3, [4, 5, 6]]\n",
    "ndarray_shape = np.array([1, 2, 3, [4, 5, 6]])"
   ],
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   "source": [
    "ndarray에서 중첩(nesting)을 사용하면\n",
    "이렇게 경고가 뜨고 아래에 보이는 것처럼 잘 들어가지 않는다."
   ],
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   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "array([1, 2, 3, list([4, 5, 6])], dtype=object)"
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "ndarray_shape"
   ],
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    "|특징       |list         |ndarray           |\n",
    "|----------|-------------|------------------|\n",
    "|원소       |여러 타입 가능   |전부 같은 타입이어야 함 |\n",
    "|메모리, 속도 |많이 차지, 느림  |적게 차지, 빠름      |\n",
    "|모양(shape)|중첩 가능       |크기가 명확하게 정의   |\n",
    "|연산       |전체 연산 불가   |전체 연산 가능       |"
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   "source": [
    "## dimension (차원)\n",
    "배열의 깊이\n",
    "\n",
    "제일 왼쪽 대괄호 수를 세면 된다."
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "print(np.array(1).ndim)\n",
    "print(np.array([1, 2, 3]).ndim)\n",
    "print(np.array([[1, 2, 3], [4, 5, 6]]).ndim)\n",
    "print(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]).ndim)"
   ],
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   "source": [
    "## Array Indexing, Slicing\n",
    "list, array와 동일\n",
    "\n",
    "한가지 차이점은 콤마(,)를 통해서 안쪽 배열에 접근할 수 있다는 것이다."
   ],
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   "source": [
    "## reshape\n",
    "차원 추가, 제거 또는 각 차원의 요소 수를 변경.\n",
    "각 요소수의 곱이 전체 개수와 맞지 않으면 에러가 뜬다"
   ],
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   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "arr = np.arange(6 * 7 * 8).reshape(6, 7, 8)"
   ],
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  },
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   "cell_type": "markdown",
   "source": [
    "## shape\n",
    "각 차원의 요소 수"
   ],
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     "name": "#%% md\n"
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   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "(6, 7, 8)"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ],
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    }
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  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "250\n",
      "250\n",
      "250\n"
     ]
    }
   ],
   "source": [
    "print(arr[4][3][2])\n",
    "print(arr[4, 3, 2])\n",
    "print(arr.tolist()[4][3][2])\n",
    "# print(arr.tolist()[4, 3, 2])  list로는 불가능"
   ],
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  },
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   "source": [
    "### 가장 왼쪽 줄 가져오기"
   ],
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   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[  0,   8,  16,  24,  32,  40,  48],\n       [ 56,  64,  72,  80,  88,  96, 104],\n       [112, 120, 128, 136, 144, 152, 160],\n       [168, 176, 184, 192, 200, 208, 216],\n       [224, 232, 240, 248, 256, 264, 272],\n       [280, 288, 296, 304, 312, 320, 328]])"
     },
     "execution_count": 10,
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    }
   ],
   "source": [
    "arr[:, :, 0]"
   ],
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    }
   }
  },
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   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "[0,\n 8,\n 16,\n 24,\n 32,\n 40,\n 48,\n 56,\n 64,\n 72,\n 80,\n 88,\n 96,\n 104,\n 112,\n 120,\n 128,\n 136,\n 144,\n 152,\n 160,\n 168,\n 176,\n 184,\n 192,\n 200,\n 208,\n 216,\n 224,\n 232,\n 240,\n 248,\n 256,\n 264,\n 272,\n 280,\n 288,\n 296,\n 304,\n 312,\n 320,\n 328]"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[j[0] for i in arr.tolist() for j in i]"
   ],
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  },
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   "cell_type": "markdown",
   "source": [
    "## copy, view\n",
    "\n",
    "* copy: 배열 복사\n",
    "* view: 배열 주소만 복사"
   ],
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  },
  {
   "cell_type": "markdown",
   "source": [
    "## iterating (반복)\n",
    "nditer를 사용하면 중첩 for문을 사용하지 않고서 반복할 수 있다."
   ],
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  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "[[ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]]\n",
      "[[ 56  57  58  59  60  61  62  63]\n",
      " [ 64  65  66  67  68  69  70  71]\n",
      " [ 72  73  74  75  76  77  78  79]\n",
      " [ 80  81  82  83  84  85  86  87]\n",
      " [ 88  89  90  91  92  93  94  95]\n",
      " [ 96  97  98  99 100 101 102 103]\n",
      " [104 105 106 107 108 109 110 111]]\n",
      "[[112 113 114 115 116 117 118 119]\n",
      " [120 121 122 123 124 125 126 127]\n",
      " [128 129 130 131 132 133 134 135]\n",
      " [136 137 138 139 140 141 142 143]\n",
      " [144 145 146 147 148 149 150 151]\n",
      " [152 153 154 155 156 157 158 159]\n",
      " [160 161 162 163 164 165 166 167]]\n",
      "[[168 169 170 171 172 173 174 175]\n",
      " [176 177 178 179 180 181 182 183]\n",
      " [184 185 186 187 188 189 190 191]\n",
      " [192 193 194 195 196 197 198 199]\n",
      " [200 201 202 203 204 205 206 207]\n",
      " [208 209 210 211 212 213 214 215]\n",
      " [216 217 218 219 220 221 222 223]]\n",
      "[[224 225 226 227 228 229 230 231]\n",
      " [232 233 234 235 236 237 238 239]\n",
      " [240 241 242 243 244 245 246 247]\n",
      " [248 249 250 251 252 253 254 255]\n",
      " [256 257 258 259 260 261 262 263]\n",
      " [264 265 266 267 268 269 270 271]\n",
      " [272 273 274 275 276 277 278 279]]\n",
      "[[280 281 282 283 284 285 286 287]\n",
      " [288 289 290 291 292 293 294 295]\n",
      " [296 297 298 299 300 301 302 303]\n",
      " [304 305 306 307 308 309 310 311]\n",
      " [312 313 314 315 316 317 318 319]\n",
      " [320 321 322 323 324 325 326 327]\n",
      " [328 329 330 331 332 333 334 335]]\n"
     ]
    }
   ],
   "source": [
    "for x in arr:\n",
    "    print(x)"
   ],
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   ],
   "source": [
    "for x in arr:\n",
    "    for y in x:\n",
    "        for z in y:\n",
    "            print(z)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for x in np.nditer(arr):\n",
    "    print(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 행렬 처리, 선형대수\n",
    "np.dot(), np.linalg.inv(), np.linalg.solve()\n",
    "와 같은 함수를 사용해서\n",
    "행렬 처리와 선형대수를 쓸 수 있다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 연산 (element-wise)\n",
    "원소 각각에 적용할 수 있는 연산\n",
    "더하기 곱하기 나누기 빼기를 쉽게 할 수 있다."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 다양한 함수들\n",
    "* arange(시작, 끝): 파이썬 range의 numpy 버전 (끝 제외)\n",
    "* zeros(shape, dtype=타입): 원소가 전부 0인 ndarray 생성\n",
    "* ones(shape, dtype=타입): 원소가 전부 1인 ndarray 생성\n",
    "* linspace(시작, 끝, 개수, dtype): 시작부터 끝까지 개수로 나눈다 (끝 포함)\n",
    "* .sum(): 전체 요소 합\n",
    "* .mean(): 평균\n",
    "* .max() / .min(): 최대, 최소\n",
    "* np.random.rand() / np.random.randn()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.]])"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5, 5))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 1., 1., 1., 1.],\n       [1., 1., 1., 1., 1.],\n       [1., 1., 1., 1., 1.],\n       [1., 1., 1., 1., 1.],\n       [1., 1., 1., 1., 1.]])"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((5, 5))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "array([ 0.        ,  0.20408163,  0.40816327,  0.6122449 ,  0.81632653,\n        1.02040816,  1.2244898 ,  1.42857143,  1.63265306,  1.83673469,\n        2.04081633,  2.24489796,  2.44897959,  2.65306122,  2.85714286,\n        3.06122449,  3.26530612,  3.46938776,  3.67346939,  3.87755102,\n        4.08163265,  4.28571429,  4.48979592,  4.69387755,  4.89795918,\n        5.10204082,  5.30612245,  5.51020408,  5.71428571,  5.91836735,\n        6.12244898,  6.32653061,  6.53061224,  6.73469388,  6.93877551,\n        7.14285714,  7.34693878,  7.55102041,  7.75510204,  7.95918367,\n        8.16326531,  8.36734694,  8.57142857,  8.7755102 ,  8.97959184,\n        9.18367347,  9.3877551 ,  9.59183673,  9.79591837, 10.        ])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 10, 50)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### rand\n",
    "0~1까지 실수를 가져오는 것.\n",
    "똑같은 확률로 가져온다."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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gCYwGWFNT1INAXUMishq1CKoeAQ6b2SEzS1AdDH7gim2eA94AYGbfRzUI2t/3s4Z6wvcmFAQisrKeWIRoxLq7ReDuJeAe4HPA01RnBz1pZu81s9trm/0m8E4z+xbwUeBt7u5B1dQsC/kS6USUaETLS4jIysysdnZx+GcNBXpI6+5HqQ4CL7/v3cu+fgq4NcgagrCQL6lbSETW1Nch6w21e7C4Iy1qnSERaUBvT7Trxwi2LC04JyKN6JSF5xQEG6CuIRFpRH0p6rAPfSoI1qlUrpArVtQiEJE19fXEKFWcfCnc6w0pCNZpsVCdAaAlqEVkLelE9XMiWwj3zCEFwTplCtX+vrTOIRCRNaTitYXnigqCLaWe7PWkFxFZTUotgq0poyAQkQbVPyfqPQlhpSBYp3qyp+IKAhG5uqUWgbqGtpZ6X5/GCERkLfUDRnUNbTHZQolYxIhHtc6QiFxdPBohHrWlLuWwUhCsU6ZQJpWIYqYgEJG1pRMxtQi2mkyhrPEBEWlYKh7VYPFWky2WNT4gIg1LJ6I6j2CryRbKmjoqIg1LJaLqGtpqMoXS0pQwEZG1pBUEW0+mUCatMQIRaVAqHiNTLId6BVIFwToUyxVKFVfXkIg0LJ2IUq44xbKCYEuozwVOabBYRBqU6oBlJhQE61B/ITVGICKNWjq7OMQzhxQE66CVR0VkvV5YeE5BsCVo5VERWa9OWIpaQbAOWnlURNarfgKqgmCL0MqjIrJe9QPHMJ9drCBYh2yhRFQrj4rIOiRiEWIRI6tZQ1tD/WQyrTwqIuuRSkQ1WLxVZItlTR0VkXVLxqPk1DW0NWSLZZIaKBaRdUrFozqPYKvIFXUtAhFZv1Q8Sq5YaXcZq1IQrEOuWCEZ169MRNYnGY+oRbBVZAsaIxCR9Qv7NQkaCgIz+2sz+wkzW1dwmNltZvaMmR03s3etss3PmNlTZvakmX1kPT+/lSru5DRGICIbUB8sDutS1I1+sP8J8FbgWTP7PTN76VrfYGZR4F7gTcAR4E4zO3LFNoeB/wTc6u4vB35tHbW3VKFUwdFZxSKyfql4FAfypXCOEzQUBO7+BXf/t8BNwCngC2b2T2b2djOLr/JtNwPH3f2EuxeAjwF3XLHNO4F73X269jwXN7ITrVCf+qUWgYisV/0AMqxTSBvu6jGzbcDbgF8AHgP+iGowfH6Vb9kDnFl2+2ztvuWuB643s380s4fN7LZVnvtuMztmZscmJiYaLbmpsgoCEdmgZMiXom5o0Rwz+xvgpcD9wL9w9/O1hz5uZsc2+fyHgdcDe4EHzewV7j6zfCN3vw+4D2BsbKwtnWz1qV/qGhKR9VpagbSTgwD4M3c/uvwOM+tx97y7j63yPeeAfctu763dt9xZ4GvuXgROmtl3qQbDIw3W1TJaeVRENqreIsgVOniMAPhvK9z30Brf8whw2MwOmVkCeAvwwBXbfJpqawAzG6XaVXSiwZpa6oUxAs24FZH1CfsYwVVbBGa2i2q/fsrMbgTqq60NAOmrfa+7l8zsHuBzQBT4oLs/aWbvBY65+wO1x95oZk8BZeC33H1yU3sUkHqTTi0CEVmvsF+ucq2uoR+nOkC8F3j/svvngd9e64fXupOOXnHfu5d97cBv1P6FWj3JexQEIrJOPbWehI4MAnf/EPAhM/spd/+rFtUUSrlimZ5YhGhES1CLyPpEzEK9zMRaXUN3uftfAgfN7EVH7e7+/hW+bUvSyqMishnJeJRcSJeZWKtrqLf2f1/QhYRdtljR+ICIbFgqxNckWKtr6AO1//9La8oJr+o6Q5oxJCIbkwzxNQkaXXTuD8xswMziZvZFM5sws7uCLi5MtOCciGxGmK9J0Ogh7hvdfQ74SaprDb0E+K2gigqjrC5KIyKb0PEtAl7oQvoJ4JPuPhtQPaGVLZRJ6loEIrJBqRDPGmo0CD5jZt8BXg180cy2A7ngygqXcsXJlzRYLCIbl0xEKZQqlMrh6x5qdBnqdwE/BIzV1gVa5MVLSm9ZC7kSoJVHRWTj6geSc7XPkzBpdNE5gJdRPZ9g+fd8uMn1hNJcrghUm3YiIhuxFATZIiO9iTZXc7lGl6G+H7gO+CbVNYEAnC4JgtlsPQjUIhCRjUkutQiKba7kxRptEYwBRzysF9wM2FwtCNQ1JCIbVT+QrB9YhkmjfR1PALuCLCTM6gmuIBCRjarPOpzLdu4YwSjwlJl9HcjX73T32wOpKmTqL5y6hkRko1JboGvoPUEWEXZqEYjIZoW5a6ihIHD3r5jZAeCwu3/BzNJULzbTFeayRYwX1hQXEVmveNSI2AtjjmHS6FpD7wQ+BXygdtceqpeZ7ApzuRI98QgR07UIRGRjzIxkPBrKrqFGD3F/BbgVmANw92eBHUEVFTaz2aLGB0Rk01LxKLMhHCxuNAjy7l6o36idVNY1U0nnskWND4jIpqUS0c7tGgK+Yma/TfUi9j8GfBL4f8GVFS5zOQWBiGxetUXQuUHwLmACeBz4d1QvSP+7QRUVNnPZkrqGRGTTwjpG0OisoYqZfRr4tLtPBFtS+MzliuweTLW7DBHpcMl4lPOz4Vu4+aotAqt6j5ldAp4BnqldnezdrSkvHGazRV2mUkQ2LRWvjhGEbbWetT7dfp3qbKEfcPcRdx8BbgFuNbNfD7y6ECiWK2R0URoRaYJUPEKhXCFfCtc1CdYKgp8F7nT3k/U73P0EcBfwc0EWFhbzOS0vISLN8cJ6Q+EaJ1grCOLufunKO2vjBPFgSgoXrTwqIs0S1mUm1gqCwgYf2zJeuCiNgkBENies1yRYa9bQK81sboX7DUgGUE/ozKpFICJNEtYWwVWDwN27/tNPS1CLSLO8cLnKcC0zoTmRa3hhCWr9qkRkc5YGi0PWNaRPtzXM6XrFItIk9QPK2YyCoKPM5YpEI0Yipl+ViGxOLBKpnlTWTS0CM7vNzJ4xs+Nm9q6rbPdTZuZmNhZkPRsxmy0ykIxhuhaBiDTBQCrWPWMEZhYF7gXeBBwB7jSzIyts1w/8e+BrQdWyGXPZEgOprjhlQkRaYDAVD92soSBbBDcDx939RO1aBh8D7lhhu/8K/D4QvpWYqLcIFAQi0hwDyXhXdQ3tAc4su322dt8SM7sJ2Ofuf3u1H2Rmd5vZMTM7NjHR2sVPZ7NFBtUiEJEmGeiyFsFVmVkEeD/wm2tt6+73ufuYu49t3749+OKWmcsWGUwrCESkOQZT3dUiOAfsW3Z7b+2+un7gBuDLZnYKeA3wQNgGjNUiEJFmGkh20WAx8Ahw2MwOmVkCeAvwQP1Bd59191F3P+juB4GHgdvd/ViANa2LuzOjIBCRJqq3CCqV8FyTILAgcPcScA/wOeBp4BPu/qSZvdfMbg/qeZtpsVCmXHGGFAQi0iQDqTjusFAIT6ugoUtVbpS7H6V6fePl9614dTN3f32QtWxEfUBnMBUnROEtIh2sPgtxLkQzEnW67FXUTwNX15CINEv9vKQwzRxSEFzFTLZ6yQUFgYg0y0Cq2hETpgFjBcFV1Bec0/RREWmWpa6hEE0hVRBcxfIxAhGRZhhU11BnURCISLPVxwjCdAF7BcFVzGSqS1D39QQ6uUpEukh/TwwzBUHHqJ9VrCWoRaRZIhGjvyfGXE6DxR1By0uISBAGUnG1CDrFbLaoaxGISNOF7ZoECoKrmM0WtbyEiDRd2K5JoCC4CnUNiUgQwna5SgXBVSgIRCQI6hrqEJWKM5ctMqSzikWkydQ11CHm8yUqrpPJRKT5BlJxMoUyxXKl3aUACoJV1ad2adaQiDTbYMjOLlYQrELLS4hIUOorkIZlnEBBsIrpTHUJ6uF0os2ViMhWM1T7XJlREITb1GI1CEZ6FQQi0lwjtSCYWii0uZIqBcEqFAQiEpT658pURkEQatOLBSKmMQIRab56EEwvKghCbXKxwFA6QTSilUdFpLnSiSiJWGSp56HdFASrmM4UGNbJZCISADNjJJ1QEITd1GKBbb097S5DRLaokd7E0uzEdlMQrGJqscBwr1oEIhKMkd4Ek2oRhNvUYpERtQhEJCDDvQkNFoeZuzOdKTCiFoGIBGQkHdcYQZjNZUuUK66zikUkMCO9PczlSqFYeE5BsIL6SR7b+hQEIhKMeo9DGAaMFQQrmFrMA1pnSESCM7x0Uln71xtSEKxgqvbCaPqoiASlfnbxZO3As50UBCuoj+Rr+qiIBGWkW1oEZnabmT1jZsfN7F0rPP4bZvaUmX3bzL5oZgeCrKdRk1pwTkQCtrQC6VYeIzCzKHAv8CbgCHCnmR25YrPHgDF3/37gU8AfBFXPekxnCiTjEdKJWLtLEZEtqn5NgjCcSxBki+Bm4Li7n3D3AvAx4I7lG7j7l9w9U7v5MLA3wHoaNrVYWEprEZEgJGIR+pOxUJxLEGQQ7AHOLLt9tnbfat4BfHalB8zsbjM7ZmbHJiYmmljiyqYWC4xo6qiIBGykNxwLz4VisNjM7gLGgPet9Li73+fuY+4+tn379sDrubSQ1/ISIhK4bb0JLi1s7VlD54B9y27vrd13GTP7UeB3gNvdvf2/EWB8LseuAQWBiARr50CS8blcu8sINAgeAQ6b2SEzSwBvAR5YvoGZ3Qh8gGoIXAywloaVyhUm5vPsHEi2uxQR2eJ2DiS5ONf+49/AgsDdS8A9wOeAp4FPuPuTZvZeM7u9ttn7gD7gk2b2TTN7YJUf1zKTiwUqjoJARAK3cyDJfL7EYr7U1joCnR/p7keBo1fc9+5lX/9okM+/EfVmmoJARIK2s9YFPT6X49rtfW2rIxSDxWFyYbYaBLsUBCISsPoB53ibu4cUBFcYn6++IDs1WCwiAasHwcX59g4YKwiuMD6bIxoxtvUpCEQkWPUDznpPRLsoCK4wPpdje18P0Yi1uxQR2eL6emKkE1F1DYXN+Hxe3UIi0hJmxq6BJOPqGgqX8dmcZgyJSMvsGOhhXF1D4TI+ryAQkdbZqRZBuOSKZWYyRXUNiUjL7BpIMj6Xx93bVoOCYJn6qd5qEYhIq+wYSFIoVZjNtu9KZQqCZS7orGIRabGlKaRtXHxOQbDMmanqNXL2DKfaXImIdIs9Q9XPm7NT2bbVoCBY5tTkItGIsW843e5SRKRLHBrtBeDkpcW21aAgWObEpUX2DqdIxPRrEZHWGEonGE7HOTmpIAiFkxOLS+ksItIqh0Z7OTmhIGg7d+fU5CIHtykIRKS1Do72qmsoDC7O58kUyly7XUEgIq117WgvF+ZyZArtuUCNgqCmnsbqGhKRVjs0Wr0ozalLmbY8v4Kgph4E6hoSkVY7OFqdqXiqTQPGCoKaU5cWScQi7B7SOQQi0lr1A9B2jRMoCGpOXFrkwEha1yEQkZbr7Ymxc6BHQdBuz47Pa6BYRNrm2tE+nh2fb8tzKwiASwt5Tk1muHH/cLtLEZEu9ar9Qzz5/By5Yrnlz60gAB49PQ3A2AEFgYi0x9iBYUoV51tnZlr+3AoC4Bunp0lEI9ywZ7DdpYhIl7qp1iNxrHZg2koKAqq/+Bv2DJCMR9tdioh0qeHeBNdt713qoWilrg+CXLHM42dnGTs40u5SRKTLjR0Y4dHT01Qqrb1aWdcHwRPnZimUK7xa4wMi0mavPjjMbLbI9yYWWvq8XR8ERx+/QDxq3KwWgYi02Q9euw2Azz5xoaXP29VBkC+V+evHzvLGI7sY7k20uxwR6XL7RtLc+pJtfPyRMy3tHurqIPj7J8eZyRR5y8372l2KiAgA/+YH9nNuJstXj19q2XN2bRC4O/c/fJq9wyluvW603eWIiADw4y/fyXA6zv0Pn27Zc3ZtEHzon07x9ZNT3P26a4lofSERCYmeWJS333qIzz81zqcfO9eS5ww0CMzsNjN7xsyOm9m7Vni8x8w+Xnv8a2Z2MMh66r78zEX+x9Hv8IaX7eBnX3OgFU8pItKwX379dfzAwWF+528e55FTU4E/X2BBYGZR4F7gTcAR4E4zO3LFZu8Apt39JcD/BH4/qHrmc0X+7onz3PORb/C2//MI+7eled+/fiVmag2ISLjEohH++M4bGe5N8DMfeIj/8Mlv8YWnxgO7glmQLYKbgePufsLdC8DHgDuu2OYO4EO1rz8FvMEC+mT+86+e5Bf/8ht85bsT/NLrr+Mzv/paRjRTSERC6prBFH/3a6/j53/wIEcfP88vfPgYH/nac4E8l7kHM0XJzH4auM3df6F2+2eBW9z9nmXbPFHb5mzt9vdq21y64mfdDdxdu/lS4JlAir66UaB1w/itpX3rTNq3ztSufTvg7ttXeiDW6ko2wt3vA+5rZw1mdszdx9pZQ1C0b51J+9aZwrhvQXYNnQOWT9DfW7tvxW3MLAYMApMB1iQiIlcIMggeAQ6b2SEzSwBvAR64YpsHgJ+vff3TwD94UH1VIiKyosC6hty9ZGb3AJ8DosAH3f1JM3svcMzdHwD+HLjfzI4DU1TDIqza2jUVMO1bZ9K+dabQ7Vtgg8UiItIZuvbMYhERqVIQiIh0OQXBFcK6LEYzNLBvv2FmT5nZt83si2bWMetvrLVvy7b7KTNzMwvV9L2raWTfzOxnaq/dk2b2kVbXuFENvCf3m9mXzOyx2vvyze2oc73M7INmdrF2rtRKj5uZ/XFtv79tZje1usbLuLv+1f5RHdT+HnAtkAC+BRy5YptfBv609vVbgI+3u+4m7ts/B9K1r39pK+1bbbt+4EHgYWCs3XU38XU7DDwGDNdu72h33U3ct/uAX6p9fQQ41e66G9y31wE3AU+s8vibgc8CBrwG+Fo761WL4HKhWhajydbcN3f/krtnajcfpnruRydo5HUD+K9U17PKtbK4TWpk394J3Ovu0wDufrHFNW5UI/vmwEDt60Hg+RbWt2Hu/iDVmZCruQP4sFc9DAyZ2TWtqe7FFASX2wOcWXb7bO2+Fbdx9xIwC2xrSXWb08i+LfcOqkcsnWDNfas1vfe5+9+2srAmaOR1ux643sz+0cweNrPbWlbd5jSyb+8B7jKzs8BR4FdbU1rg1vv3GKiOWGJCWsvM7gLGgH/W7lqawcwiwPuBt7W5lKDEqHYPvZ5qK+5BM3uFu8+0s6gmuRP4C3f/QzP7QarnHd3g7pV2F7aVqEVwua28LEYj+4aZ/SjwO8Dt7p5vUW2btda+9QM3AF82s1NU+2Qf6JAB40Zet7PAA+5edPeTwHepBkPYNbJv7wA+AeDuDwFJqou2dbqG/h5bRUFwua28LMaa+2ZmNwIfoBoCndLPDGvsm7vPuvuoux9094NUxz9ud/dj7Sl3XRp5T36aamsAMxul2lV0ooU1blQj+/Yc8AYAM/s+qkEw0dIqg/EA8HO12UOvAWbd/Xy7ilHX0DK+9ZbFWNLgvr0P6AM+WRv/fs7db29b0Q1qcN86UoP79jngjWb2FFAGfsvdQ99KbXDffhP4MzP7daoDx2/rhAMvM/so1XAerY1v/GcgDuDuf0p1vOPNwHEgA7y9PZVWaYkJEZEup64hEZEupyAQEelyCgIRkS6nIBAR6XIKAhGRLqcgEBHpcgoCEZEu9/8BVxLC067tMoYAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.random.rand(1000000))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### randn\n",
    "정규 분포를 따르는 랜덤\n",
    "평균이 0이고 표준편차가 1\n",
    "인자로 shape를 받는다."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.random.randn(1000000))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### normal\n",
    "세세하게 정규 분포를 조절할 수 있다."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.random.normal(size=1000000))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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osyY7NZG8dBtHMdNjCcWYs6CqNLb3sbQoI+KnC49Xnp9OU+cAAbsexUyRJRRjzkJX/wi9Qz4qCqOnu+u08oJ0Bkf9NLT3eR2KiRCWUIw5C6eXKIm0e59Mxelj2nTA7o9ipsYSijFnoalzgIzkBAoyou9uCbnpSeSkJtr9UcyUWUIx5iwc6hygrCAdibLxk9PKC9LZ3NSN2vUoZgosoRgzQ0dPnOLk4Cjl+ZG9GOSZlBek0zUwQmN7/+SVTcyzhGLMDG12uoLKonD85LR3x1Gs28tMgSUUY2Zoc1M3KYlxFGeleB2Ka/LSkyjOSqb2oA3Mm8lZQjFmhmqbuinLT4+660/GEhEuW5JPrY2jmCmwhGLMDLT3DtHUORCV04XHu7Q8n46+Ybtq3kzKEooxM7D5UHBMISYSypI8AJs+bCZlCcWYGdjc1E1aUjwl2dGzftdElhSkU5Bh4yhmcpZQjJmBzU3dXLw4l/i46B0/OU1EuHRJno2jmElZQjFmmk4MjLDveB+XLcn3OpQ5c1l5Hq09QxzptvvMm4m5mlBEZI2I1ItIo4isD7E9WUQedbbXikjZmG13OuX1InKdU7ZQRF4WkT0isltE/m5M/e+KSIuIbHcen3Dz2Ezs2uKMn6wuz/M4krlzqZM8NzVZt5eZmGv3KxWReOA+4BrgKLBFRGpUdc+YarcCJ1S1QkTWAfcAnxeRKmAdsAKYD7wgIssAH/D3qvq2iGQCW0Xk+TFt3quqP3LrmIyBYHdXUkIcF5Rms78t+q8g31DbjKqSnhTPhtpmfH7lC5cu8josE4bcPENZDTSq6kFVHQE2AmvH1VkLPOw8fxy4WoKLIq0FNqrqsKo2AY3AalVtVdW3AVS1D9gLLHDxGIz5gM2HurlwYQ7JCfFehzJnRITK4kz2t/XZfebNhNxMKAuAI2NeH+WDf/zfraOqPqAHyJ/Kvk732IVA7ZjiO0Rkh4g8JCK5oYISkdtEpE5E6jo6OqZ9UCa29Q/72NXSw6Ux1N112rLiDAZG/Bw7aeMoJrSIHJQXkQzgP4H/qaq9TvFPgaXAKqAV+HGofVX1AVWtVtXqwsLCuQjXRJG6Q90E9L0xhVhSUZSJAPVtdsMtE5qbCaUFWDjmdalTFrKOiCQA2UDXmfYVkUSCyeQRVf3d6Qqq2qaqflUNAP9OsMvNmFm1uambhDjhwkU5Xocy5zKSE1iQmxoT40ZmZtxMKFuAShEpF5EkgoPsNePq1AC3OM9vAl7S4ET3GmCdMwusHKgENjvjKw8Ce1X1f49tSERKxrz8DLBr1o/IxLzNTd2cX5pNWpJr81nC2rLiTI50n+LkqRGvQzFhyLXfClX1icgdwHNAPPCQqu4WkbuAOlWtIZgcfiUijUA3waSDU+8xYA/BmV23q6pfRK4AvgTsFJHtzlt9W1WfAX4oIqsABQ4B/82tYzOxZ0NtM6P+ANuaT3J5RT4bapu9DskTy4ozeWlfO6/t7+SGlfO9DseEGVf/zXL+0D8zruw7Y54PAZ+bYN+7gbvHlb0BhLw0WVW/dLbxGnMmR7pP4VeNifW7JlKam0paUjyv1LdbQjEfEJGD8sZ4oalzAAEW58duQokToaIog9caOggEbPqweT9LKMZMUVPXACXZKaQkxs71J6EsL86ks3+E3cd6J69sYsqUEoqI/E5EPikiloBMTPIFAhzpPhXVt/udqoqiDABebWj3OBITbqaaIO4HvgDsF5EfiMhyF2MyJuwcOzHIqF8pi+HurtMyUxI5f0E2r9TbhcHm/aaUUFT1BVX9S+AigjOoXhCRP4vIXznXhRgT1Zq6TgHYGYrjo8sLebv5BD2nRr0OxYSRKXdhiUg+8BXgq8A24CcEE8zzrkRmTBhp6uynKDOZjOTYvP5kvI8uLySg8HqjnaWY90x1DOX3wOtAGvBpVb1BVR9V1f8BZLgZoDFe8weUw102fjLWytIcslMTrdvLvM9U/936d+eakneJSLKzGnC1C3EZEzb2tvYy7AtQbuMn70qIj+OKygJebehAVQkuYmFi3VS7vL4fouyt2QzEmHBV2xS8oZadobxnQ20zKQnxdPQN8+M/NbChtjlmVw8w7znjGYqIzCO4bHyqiFzIe1epZxHs/jIm6tUe7CIvPYnsVJt/Mtay4mBvd0NbH/NzUj2OxoSDybq8riM4EF8KjF2MsQ/4tksxGRM2AgGltqn73WsvzHsyUxKZn51CQ1sfH11e5HU4JgycMaGo6sPAwyLyWVX9zzmKyZiwsfd4Lz2Doyyx7q6QlhVn8tr+DgZH/KQmxfYKAmbyLq8vquqvgTIR+cb47eOXkDcm2mw6GBw/WVJoZyihVBZn8kpDBwc6+jlvQbbX4RiPTTYof/rfsgwgM8TDmKj21oEuyvLTbPxkAovy0khJjKPB7uJomLzL69+cr9+bm3CMCR/+gLK5qYtPnF8yeeUYFR8nLC3MYH97P8F745lYNtULG38oIlkikigiL4pIh4h80e3gjPHS3tZeeod8fGhp7N0/fjoqijLoGRyls9/u4hjrpnodyrWq2gt8iuBaXhXAN90KyphwsOlgFwCXLbGEciaVRcHe78Z26/aKdVNNKKe7xj4J/FZVe1yKx5iw8daBLpYUpFOcleJ1KGEtLz2JvPQkGtv7vQ7FeGyqCeVpEdkHXAy8KCKFwNBkO4nIGhGpF5FGEVkfYnuyiDzqbK8VkbIx2+50yutF5DqnbKGIvCwie0Rkt4j83Zj6eSLyvIjsd77mTvHYjPmA4PhJN5fa2cmUVBRmcLBzgFF/wOtQjIemunz9euDDQLWqjgIDwNoz7SMi8cB9wPVAFXCziFSNq3YrcEJVK4B7gXucfauAdcAKYA1wv9OeD/h7Va0CLgNuH9PmeuBFVa0EXnReGzMju4/10Dds4ydTVVGUwbAvwPYjJ70OxXhoOndgPAf4vIh8GbgJuHaS+quBRlU9qKojwEY+mITWAg87zx8HrpbgKnNrgY3O4pNNQCOwWlVbVfVtAFXtA/YSXBpmfFsPAzdO49iMeZ93x0/K8zyOJDIsLcxAgNf3d3odivHQVGd5/Qr4EXAFcInzmGyV4QXAkTGvj/LeH/8P1FFVH9AD5E9lX6d77EKg1ikqVtVW5/lxoHiCY7lNROpEpK6jw5beNqG9daCLJYXpFNn4yZSkJsWzIDeVN/bb71Qsm+ry9dVAlYbJRHMRyQD+E/ifzuyz91FVFZGQsarqA8ADANXV1WFxPCa8DI36eetgF5+vXuh1KBGloiiD1/d30js0SlaKXQgai6ba5bULmDfNtluAsb+RpU5ZyDoikgBkA11n2te55fB/Ao+o6u/G1GkTkRKnTgnQPs14jWFDbTP3PLuPodHAu69tWfapqSjKwB9Q3jrQ5XUoxiNTTSgFwB4ReU5Eak4/JtlnC1ApIuUikkRwkH38PjXALc7zm4CXnLOgGmCdMwusHKgENjvjKw8Ce0OsIza2rVuAJ6d4bMa8T8PxPhLihPICW79rOhblpZGWFM8bNo4Ss6ba5fXd6Tasqj4RuQN4DogHHlLV3SJyF1CnqjUEk8OvRKQR6CaYdHDqPQbsITiz63ZV9YvIFcCXgJ0ist15q287d5P8AfCYiNwKHAb+y3RjNgagoa2f8oJ0khKmM2fFJMTFcdmSfN5otIQSq6aUUFT1VRFZDFSq6gsikkYwSUy23zPAM+PKvjPm+RDwuQn2vRu4e1zZG7x3k6/x9buAqyeLyZgzOTEwQkf/MKttdteMXFFRwEv72jl64hSluXYPvlgz1Vlef01wWu+/OUULgCdciskYzzQ4y4dUFlt310xcWVkAYN1eMWqq5/S3A5cDvQCquh+wW7SZqNNwvI/ctEQKM5K9DiUiVRRlUJyVbNejxKipJpRh5+JE4N0ZWTbl1kSVEV+AAx0DLCvOJDj/w0yXiHBFRSFvHujEH7A/EbFmqgnlVRH5NpAqItcAvwWeci8sY+Ze3aFuRvwBlhXbvePOxpWVBZw8NcquFltDNtZMNaGsBzqAncB/IzjQ/v+5FZQxXniloYP4OGFJod0//mxcWVlAnMCL++xSsFgz1cUhAwQH4f+7qt6kqv8eLlfNGzNbXq3voCw/jeSESScwmjPIz0jm4sW5vLCnzetQzBw7Y0KRoO+KSCdQD9Q7d2v8zpn2MybSHDs5SH1bn3V3zZJrqorZ09rL0ROnvA7FzKHJzlC+TnB21yWqmqeqecClwOUi8nXXozNmjrzaEFzU0BLK2Tm9VM3QSHDpmh/8cZ8tXRNDJksoXwJudpaQB0BVDwJfBL7sZmDGzKVX6zuYn51CUaZNF54NBZnJFGYks7f1A2u3mig2WUJJVNUPTChX1Q7AlhM1UWHUH+DNxk4+srzQpgvPonNLsmjqHGBwxO91KGaOTJZQRma4zZiIsfXwCfqGfXxkmV2rO5vOLckkoNDQ1ud1KGaOTJZQVopIb4hHH3D+XARojNtebeggIU64vMJu9zubFualkZ6cwB7r9ooZZ1wcUlVt/qSJeq/Ud3Dx4lwy7aZQsypOhHPnZbKzpYcRX8BWb44B9h02Ma2td4i9rb18dLl1d7nh3JIshn0BNh20m27FgqneD8WYqLOhtpmth7sBODXis+mtLqgoyiApPo4/7jrOVcsKvQ7HuMzOUExMq2/rJyslgXlZKV6HEpUS4+NYPi+TP+0+js8f8Doc4zJLKCZm+QNKY3sflba6sKvOW5BN18AImw91ex2KcZmrCUVE1ohIvYg0isj6ENuTReRRZ3utiJSN2XanU14vIteNKX9IRNpFZNe4tr4rIi0ist15fMLNYzOR7+iJUwyN2urCbltenElqYjx/3Hnc61CMy1xLKCISD9wHXA9UATeLSNW4arcCJ1S1ArgXuMfZt4rg/eVXAGuA+532AH7hlIVyr6quch7PTFDHGADq2/qIE6gotLszuikpIY6PnVPIs7uP2z1SopybZyirgUZVPejcnGsjsHZcnbXAw87zx4GrJdj3sBbYqKrDzrIvjU57qOprgJ07m7PW0NbHwrw0UpNsdrzbrj+vhI6+Yeqs2yuquZlQFgBHxrw+6pSFrKOqPqAHyJ/ivqHcISI7nG6x3FAVROQ2EakTkbqOjo6pHYmJOh19wxw7OcRy6+6aEx8/p4jkhOBsLxO9omlQ/qfAUmAV0Ar8OFQlVX1AVatVtbqw0KYxxqrXnNWFKy2hzIn05AQ+uryQP+5qJWDdXlHLzYTSAiwc87rUKQtZx7lPfTbQNcV930dV21TV79wM7N9xusiMCeWVhg4ykhMoybbpwnPlE+eX0NY7zNvNJ7wOxbjEzYSyBagUkXIRSSI4yF4zrk4NcIvz/CbgJedOkDXAOmcWWDlQCWw+05uJSMmYl58Bdk1U18Q2f0B5fX8Hy4oziLPpwnNiQ20zXf0jJMQJP/pTg11EGqVcSyjOmMgdwHPAXuAxVd0tIneJyA1OtQeBfBFpBL5B8N71qOpu4DFgD/AscLuq+gFE5DfAW8ByETkqIrc6bf1QRHaKyA7gYwRvDmbMB7xz9CQnT41ad9ccS0mM59ySLHYcPWmzvaKUq0uvOFN3nxlX9p0xz4eAz02w793A3SHKb56g/pfOKlgTM16p7yBOoLLIpgvPtVULc9jZ0sP+dlvSPhpF06C8MVPyakMHqxbmkJZkS9nNtcriDFIT49l+5KTXoRgXWEIxMaWrf5gdR0/azbQ8khAXxwWl2ext7aV/2Od1OGaWWUIxMeX1/Z2owkeX25Rxr6xamMOoX3nOrkmJOpZQTEx5taGDvPQkzl+Q7XUoMWtRXhq5aYk8sf2MVwKYCGQJxcSMQEB5raGDqyoLiIuz6cJeERFWLczhzcZO2nuHvA7HzCJLKCZm7GzpoWtgxO7OGAZWLswhoFDzzjGvQzGzyBKKiRkv17cjAldWFngdSswrykzh/AXZ1u0VZSyhmJjxwt42LlqUS35GstehGODGCxewq6WXRrsmJWpYQjEx4djJQXa19HJNVbHXoRjHp1eWECfwxDbr9ooWllBMTHhxbxsAf3GuJZRwUZSZwuUVBTyxvYXgEn4m0llCMVFvQ20zv3zrMAUZSWxu6mZDbbMtThgmbly1gKMnBtl62FYgjgaWUEzUGxzxc7BjgHPnZXkdihnnuvPmkZIYZ4PzUcIWMzJRb/exHvyqnF9qFzOGk9NniZVFmfzu7RaWF2cRHyd84dJFHkdmZsrOUEzU29HSQ156EgtyUr0OxYSwamEOp0b87G+z2V6RzhKKiWqd/cMcaO/ngtJsxG6mFZbeXYH46EmvQzFnyRKKiWrP7GxFgZWlOV6HYiaQEBfH+c4KxMOjfq/DMWfBEoqJar+tO0pJdgrFWXbv+HC2qjS4AvGe1l6vQzFnwRKKiVp7jvWys6WHixfneh2KmcSi/DRy0hLtxlsRztWEIiJrRKReRBpFZH2I7cki8qizvVZEysZsu9MprxeR68aUPyQi7SKya1xbeSLyvIjsd77aX5EY91jdEZLi41hl3V1hL06ElaU5NLb309E37HU4ZoZcSygiEg/cB1wPVAE3i0jVuGq3AidUtQK4F7jH2bcKWAesANYA9zvtAfzCKRtvPfCiqlYCLzqvTYwaGvXzxPYWrllRTFqyzY6PBKsW5qDA0ztsKZZI5eYZymqgUVUPquoIsBFYO67OWuBh5/njwNUSnIqzFtioqsOq2gQ0Ou2hqq8B3SHeb2xbDwM3zuKxmAjzx12tnDw1ys2X2DUNkaI4K4WS7BSe2G4JJVK5mVAWAEfGvD7qlIWso6o+oAfIn+K+4xWraqvz/DgQctEmEblNROpEpK6jo2Mqx2Ei0K83NVNekM6Hl+Z7HYqZhpWlObxz5CSHOge8DsXMQFQOymtwpbmQq82p6gOqWq2q1YWFdl/xaLTnWC9bD5/gLy9dZHdmjDArF+YgAk/aWUpEcjOhtAALx7wudcpC1hGRBCAb6JrivuO1iUiJ01YJ0D7jyE1EOr3o43drdpPgJBJbBDKyZKcmcml5Hk/aCsQRyc2EsgWoFJFyEUkiOMheM65ODXCL8/wm4CXn7KIGWOfMAisHKoHNk7zf2LZuAZ6chWMwEWZo1M/2Iye5oDSHtCQbjI9EN65awMHOAXa29Hgdipkm1xKKMyZyB/AcsBd4TFV3i8hdInKDU+1BIF9EGoFv4MzMUtXdwGPAHuBZ4HZV9QOIyG+At4DlInJURG512voBcI2I7Af+wnltYsy2IycZ8Qe4tDzP61DMDF1/fglJ8XF2460I5Oq/cKr6DPDMuLLvjHk+BHxugn3vBu4OUX7zBPW7gKvPJl4T2VSV2oNdLMhJpTTXFoKMVNmpiXzsnEKe2nGMb3/iHBLio3KoNyrZd8pEjaauAdr7hrm0PM8Wgoxwn7mwlI6+YV5tsJmYkcQSiokabx3oIjUxngvsyviId/W5RRRlJvOITaqIKDZqaaLCsZOD7G3t5fKKApIS7P+kSHZ6Zt6K+dm8vK+d+15uJDctyW68FQHsN89EhV9vOowqXFZuFzJGi0vKgsvxbW4KtTCGCUeWUEzEGxr1s3HLEc4tySI3PcnrcMwsyUlL4tySLDY3dTPiC3gdjpkCSygm4j31zjG6B0b4kC2zEnWuqChgcNTP280nvA7FTIElFBPRVJVf/PkQy4ozWFKQ7nU4ZpYtzk+jNDeVNxs78QfsyvlwZwnFRLTNTd3sPtbLlz9UZlOFo5CIcGVlIV0DI/xhZ+vkOxhPWUIxEe3+Vw6Qn57EZy8q9ToU45IV87MoykzmJy802FlKmLOEYiLWrpYeXm3o4L9eUU5qUvzkO5iIFCfC1ecWc6BjgKfeseVYwpklFBOx7n+lkYzkBL542WKvQzEuWzE/i3PmZfKTF/fj89uMr3BlCcVEnA21zfzzc/U8s/M4l5Tl8ocdrbZMfZSLE+Hr1yyjqXPA7ugYxiyhmIj0p93HSU2M58pKu0larLi2qpgV87P41xf3M2pnKWHJEoqJOAc6+tnf3s9HlhWSkmhjJ7FCRPj7a5fR3H2KRzYd9jocE4Kt5WUiis8f4Okdx8hNS7QLGWPMhtpmVJWKogx+8Ow+RvzKbVct8TosM4adoZiI8khtM229w3zi/BIS7T4ZMUdE+NT5JYz4Arywp83rcMw49htpIkZb7xA/eq6eisIMqkqyvA7HeKQoK4UPLclny6FudtltgsOKqwlFRNaISL2INIrI+hDbk0XkUWd7rYiUjdl2p1NeLyLXTdamiPxCRJpEZLvzWOXmsZm5972ndjPiD7B21Xy7Kj7GffycYtKS4vluzW5U7WLHcOFaQhGReOA+4HqgCrhZRKrGVbsVOKGqFcC9wD3OvlXAOmAFsAa4X0Tip9DmN1V1lfPY7taxmbn3wp42ntl5nK9dXUl+RrLX4RiPpSbFc92KedQdPsFv6456HY5xuHmGshpoVNWDqjoCbATWjquzFnjYef44cLUE//VcC2xU1WFVbQIanfam0qaJMv3DPr7z5C6WF2fy11faIKwJumhxLqvL8/inP+zheM+Q1+EY3E0oC4AjY14fdcpC1lFVH9AD5J9h38navFtEdojIvSIS8t9YEblNROpEpK6jw+5XHe421DZz2y/raO0Z4mPLC3l8q/03aoLiRPjhZy9g1B/g27/faV1fYSCaBuXvBM4BLgHygG+FqqSqD6hqtapWFxbaRXHh7kj3Kd460MWlS/JYlG/L05v3KytI5x+uO4eX9rXzu7dbvA4n5rmZUFqAhWNelzplIeuISAKQDXSdYd8J21TVVg0aBv6DYPeYiWCj/gBPbG8hMyWBa6vmeR2OCVNf+XAZ1Ytz+d5Tu63ry2NuJpQtQKWIlItIEsFB9ppxdWqAW5znNwEvafC8tQZY58wCKwcqgc1nalNESpyvAtwI7HLx2MwceOC1g7T2DPHplfPtingT0obaZjZuOcJVlYUMjvr54s9r+bVdRe8Z1xKKMyZyB/AcsBd4TFV3i8hdInKDU+1BIF9EGoFvAOudfXcDjwF7gGeB21XVP1GbTluPiMhOYCdQAHzfrWMz7tvf1sdPXtjPefOzWDE/2+twTJgryEzmU+fPp7GjnzcbO70OJ2ZJLA9kVVdXa11dnddhmHH8AeWzP/0zh7sG+NuPVpCRbCsEmcmpKhs2N7OvtY8nbr+c80vtHxG3iMhWVa0eXx5Ng/ImSjz4xkG2HznJd29YYcnETJmI8JkLF5CeHM/XNm5jYNjndUgxxxKKCSsHOvr58Z8auKaqmBtWzvc6HBNh0pIS+C/VCznUNcBdT+3xOpyYYwnFhI2hUT93bNhGalI8d994ni2vYmZkSWEGf/uRpTxad4Q/7Gj1OpyYYv0JJixsqG3miW0t7G3t5csfWswLe9u9DslEsK9fs4w3GztZ/7sdrJifRVmBXcM0F+wMxYSFTQe72HyomysrCzhnnq0kbM7Ob+uOcm3VPHx+Zd0Dm/jFm4fsNtFzwBKK8dxL+9p4escxlhdnct0Ku4DRzI7c9CTWXbKQtt4hHq07QiCGZ7TOFUsoxlOvNnTwN79+m5LsVNZdspA4Gzcxs6iyOJNPXlDC3tZennrnmK335TJLKMYzz+xs5a9/WUdFYQZ/dXkZyXY1vHHBh5cWcFVlAbVN3Xznyd0EApZU3GIJxcy5QEC5/5VGbt/wNucvyOaRr15KWpLNDzHuuW7FPK6sLOBXmw7ztY3bGBzxex1SVLLfYjOnfv76QR7fepR9x/s4f0E2N6yczx93Hfc6LBPlRIQ1K+ZxeUUB9zy7jwMdA/zrulVUFmd6HVpUsTMUM2e2HOrm/77UyP62fj69cj7rLllIYrz9CJq5ISL8zUeW8tAtl9DWO8Qn/88b/Pz1g9YFNotsLS9by8t1Xf3D/OCP+/jt1qPkpiVy8+pFlOameR2WiWF9Q6PB656O97G6PI97PnsB5XatypRNtJaXJRRLKK4ZGvXzm83N/MsL+xkY9vHVK5cwLyuFpAQ7KzHeU1Xebj7Jn/YcZ8QX4GtXV3LbVUvsrHkKJkooNoZiZt3giJ8Nm5v5lxca6BvysaQgna98uIzirBSvQzPmXSLCxYtz+daa5fyvmt3883P1PPXOMf7/T1VxeUWB1+FFJDtDsTOUWdNycpBfbzrMxs3NnDg1SnlBOlefU8SSwgyvQzNmUnuO9fD0jlZODo6yrDiDn6y7kHNLbNWGUOwMxbhCValt6ubhPx/iud3B2VrXVBVz6xVLaGzv9zg6Y6auan42lcWZbDrYxcv17Vz/k9e5oqKAz1+ykGtXFJOcYNdJTcbOUOwMZdqGfX52tfTyx52tPLOzlWM9Q6QmxnNJWS6XLsknNy3J6xCNOSunRnwMDPt5rO4ILScHyU1L5GPnFPHxc4q4srKQ7NREr0P0lA3Kh2AJ5cyGfX6aOgdoaOunsa2PhrZ+9rf3cajrFP6AkhQfx5WVBeSmJ3He/GwbbDdRJ6BKY3s/25pP0NDWz+ConziBc0uyuHhxLhcvzuWiRbmU5qbG1O0WPEkoIrIG+AkQD/xcVX8wbnsy8EvgYqAL+LyqHnK23QncCviBr6nqc2dqU0TKgY1APrAV+JKqjpwpPksowS6rjr5hmjoHONx1ikNdAxzsGKChvY/DTuIAiBPIS0+iKDOFoqxk5mWlsKw4kxRbLsXECH9AOXriFA1t/TR3D3Cke5ARfwCA/PQklhZlsLQwg/KCNIqzUijMTKY4K4WizGQykhOiKuHM+RiKiMQD9wHXAEeBLSJSo6pjb6N2K3BCVStEZB1wD/B5EakC1gErgPnACyKyzNlnojbvAe5V1Y0i8jOn7Z+6dXzTpaqoghL8ryf4PPg1uP291+rUDyjglPsCis+vjPoDjPoD+ALKiC/glAcY8Qfw+RVfIMCIL/h11B/g1IifU8P+4NcRHz2Do3T2D9PZP+J8HWZoNPBunAlxwqL8NFIS4rmqsuDdBFKQkWzTKU1Mi48TFuenszg/eL2KP6C09Q7R3H2KlpODtPUMsfNoD4OjH1zWJTFeyEpJJDs1kczURLJSEshKTSQrJZGs1ATn67jylAQS4uOIF0Ek+P5xIsTFQZwI8TLudZxTzykXYc6TmJuD8quBRlU9CCAiG4G1wNiEshb4rvP8ceD/SvATWAtsVNVhoElEGp32CNWmiOwFPg58wanzsNOuKwnln57ew282N78/CYRICMGvbkQwMwlxQmpiPBkpCaQnJ1CQkUxZfjq56UnkpydRkJFMdmoi8XHR85+UMW6JjxPm56QyPyf1feVDo356h0bpG/LRNzRK76CPwVE/gyN+Bkf9nBr20d0/zOBogKHRYJnfpav14wQn6QhxAkLwd1sE/u1LF3NlZeGsvp+bCWUBcGTM66PApRPVUVWfiPQQ7LJaAGwat+8C53moNvOBk6rqC1H/fUTkNuA252W/iNRP45i8VgB0eh2Ex+wzCLLPwT4DOIvP4Kp/Oqv3XRyqMOamDavqA8ADXscxEyJSF6rfMpbYZxBkn4N9BhB+n4GbneItwMIxr0udspB1RCQByCY4OD/RvhOVdwE5ThsTvZcxxhgXuZlQtgCVIlIuIkkEB9lrxtWpAW5xnt8EvKTBaWc1wDoRSXZmb1UCmydq09nnZacNnDafdPHYjDHGjONal5czJnIH8BzBKb4PqepuEbkLqFPVGuBB4FfOoHs3wQSBU+8xggP4PuB2VfUDhGrTectvARtF5PvANqftaBORXXWzzD6DIPsc7DOAMPsMYvrCRmOMMbPHLiwwxhgzKyyhGGOMmRWWUMKIiOSIyOMisk9E9orIh0QkT0SeF5H9ztdcp66IyL+KSKOI7BCRi7yOf7aIyNdFZLeI7BKR34hIijMRo9Y53kedSRk4EzcedcprRaTM4/BnREQeEpF2Edk1pmza33sRucWpv19Ebgn1XuFsgs/hn53fiR0i8nsRyRmz7U7nc6gXkevGlK9xyhpFZP0cH8ZZCfUZjNn29yKiIlLgvA6vn4XgkiD2CIcHwSv8v+o8TwJygB8C652y9cA9zvNPAH8EBLgMqPU6/ln6DBYATUCq8/ox4CvO13VO2c+Av3We/3fgZ87zdcCjXh/DDI/7KuAiYNeYsml974E84KDzNdd5nuv1sc3C53AtkOA8v2fM51AFvAMkA+XAAYKTdeKd50uc36N3gCqvj+1sPgOnfCHBCUmHgYJw/FmwM5QwISLZBH+QHgRQ1RFVPUlwGZqHnWoPAzc6z9cCv9SgTQSvwymZ06DdkwCkOtcVpQGtBJfWedzZPv5zOP35PA5cLXO9gNEsUNXXCM50HGu63/vrgOdVtVtVTwDPA2tcD34WhfocVPVP+t4qGJsIXmcGY5ZoUtUm4PQSTe8u+6TBBWJPL/sUESb4WQC4F/gHgqs7nRZWPwuWUMJHOdAB/IeIbBORn4tIOlCsqq1OneNAsfM81NI2IZebiSSq2gL8CGgmmEh6CK4efVJDL63zvuV7nPr5cxmzi6b7vY/Kn4lx/ivB/8ghhj4HEVkLtKjqO+M2hdVnYAklfCQQPM39qapeCAwQ7OZ4lwbPZaN6nrczTrCWYIKdD6QTYf9luyEWvveTEZF/JHhd2iNexzKXRCQN+DbwHa9jmYwllPBxFDiqqrXO68cJJpi2011Zztd2Z/tUlraJRH8BNKlqh6qOAr8DLmfipXUmWr4nGkz3ex+tPxOIyFeATwF/6SRXiJ3PYSnBf7DeEZFDBI/nbRGZR5h9BpZQwoSqHgeOiMhyp+hqgisFjF2eZuySMjXAl51ZHpcBPWO6RyJZM3CZiKQ5YyGnP4eJltaZaPmeaDDd7/1zwLUikuuc6V3rlEU0Cd5U7x+AG1T11JhN01qiaa7jni2qulNVi1S1TFXLCP7zeZHzNyO8fha8ntFgj/fN4lgF1AE7gCcIzs7IB14E9gMvAHlOXSF4s7EDwE6g2uv4Z/Fz+B6wD9gF/IrgLJ4lBP9YNAK/BZKduinO60Zn+xKv45/hMf+G4JjRKME/GLfO5HtPcIyh0Xn8ldfHNUufQyPB8YDtzuNnY+r/o/M51APXjyn/BNDgbPtHr4/rbD+DcdsP8d4sr7D6WbClV4wxxswK6/IyxhgzKyyhGGOMmRWWUIwxxswKSyjGGGNmhSUUY4wxs8ISijHGmFlhCcUYY8ys+H9EWq8FS7EMJAAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(np.random.normal(1000, 100, 100000))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}