Numpy.array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:47:04.020139Z",
     "start_time": "2020-06-08T02:47:03.884223Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18.1\n"
     ]
    }
   ],
   "source": [
    "print(np.version.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [i for i in range(10)]\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 'ML', 6, 7, 8, 9]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[5] = 'ML'\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:44:37.644592Z",
     "start_time": "2020-06-08T02:44:37.640595Z"
    }
   },
   "outputs": [],
   "source": [
    "import array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:45:11.003880Z",
     "start_time": "2020-06-08T02:45:10.997882Z"
    }
   },
   "outputs": [],
   "source": [
    "arr = array.array('i', [i for i in range(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:45:29.932079Z",
     "start_time": "2020-06-08T02:45:29.921086Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:45:41.340078Z",
     "start_time": "2020-06-08T02:45:41.334082Z"
    }
   },
   "outputs": [],
   "source": [
    "arr[5] = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:45:44.028781Z",
     "start_time": "2020-06-08T02:45:44.020786Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('i', [0, 1, 2, 3, 4, 100, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:45:50.932883Z",
     "start_time": "2020-06-08T02:45:50.858929Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "an integer is required (got type str)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-16b86b9d4dfe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0marr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'ML'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: an integer is required (got type str)"
     ]
    }
   ],
   "source": [
    "arr[4] = 'ML'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:47:12.105196Z",
     "start_time": "2020-06-08T02:47:12.097201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr = np.array([i for i in range(10)])\n",
    "nparr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:47:25.815184Z",
     "start_time": "2020-06-08T02:47:25.797194Z"
    }
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'NL'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-9df0da301ab9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnparr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'NL'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'NL'"
     ]
    }
   ],
   "source": [
    "nparr[5] = 'NL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:47:44.690168Z",
     "start_time": "2020-06-08T02:47:44.684173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:48:13.591869Z",
     "start_time": "2020-06-08T02:48:13.587873Z"
    }
   },
   "outputs": [],
   "source": [
    "nparr[4] = 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:48:28.655006Z",
     "start_time": "2020-06-08T02:48:28.648011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 5, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:48:39.229625Z",
     "start_time": "2020-06-08T02:48:39.222629Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:48:53.916351Z",
     "start_time": "2020-06-08T02:48:53.911355Z"
    }
   },
   "outputs": [],
   "source": [
    "nparr[3] = 3.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:49:00.266576Z",
     "start_time": "2020-06-08T02:49:00.260579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:49:05.040671Z",
     "start_time": "2020-06-08T02:49:05.035674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 5, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:49:38.291984Z",
     "start_time": "2020-06-08T02:49:38.285989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr2 = np.array([1,2,3.0])\n",
    "nparr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:57:26.183119Z",
     "start_time": "2020-06-08T02:57:26.177123Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:57:42.695987Z",
     "start_time": "2020-06-08T02:57:42.690991Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:57:56.871222Z",
     "start_time": "2020-06-08T02:57:56.865226Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:58:32.583710Z",
     "start_time": "2020-06-08T02:58:32.577712Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,5), dtype=int)   #arg1 = shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:58:41.619467Z",
     "start_time": "2020-06-08T02:58:41.615470Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T02:59:19.780353Z",
     "start_time": "2020-06-08T02:59:19.774357Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[66, 66, 66, 66, 66],\n",
       "       [66, 66, 66, 66, 66],\n",
       "       [66, 66, 66, 66, 66]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full(shape = (3, 5),  fill_value = 66)  #arg2 = value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:00:51.197963Z",
     "start_time": "2020-06-08T03:00:51.190967Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(0,20,2)]   #前闭后开  不能是float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:01:11.186195Z",
     "start_time": "2020-06-08T03:01:11.180198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 20, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:01:32.675520Z",
     "start_time": "2020-06-08T03:01:32.667525Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ,\n",
       "        2.2,  2.4,  2.6,  2.8,  3. ,  3.2,  3.4,  3.6,  3.8,  4. ,  4.2,\n",
       "        4.4,  4.6,  4.8,  5. ,  5.2,  5.4,  5.6,  5.8,  6. ,  6.2,  6.4,\n",
       "        6.6,  6.8,  7. ,  7.2,  7.4,  7.6,  7.8,  8. ,  8.2,  8.4,  8.6,\n",
       "        8.8,  9. ,  9.2,  9.4,  9.6,  9.8, 10. , 10.2, 10.4, 10.6, 10.8,\n",
       "       11. , 11.2, 11.4, 11.6, 11.8, 12. , 12.2, 12.4, 12.6, 12.8, 13. ,\n",
       "       13.2, 13.4, 13.6, 13.8, 14. , 14.2, 14.4, 14.6, 14.8, 15. , 15.2,\n",
       "       15.4, 15.6, 15.8, 16. , 16.2, 16.4, 16.6, 16.8, 17. , 17.2, 17.4,\n",
       "       17.6, 17.8, 18. , 18.2, 18.4, 18.6, 18.8, 19. , 19.2, 19.4, 19.6,\n",
       "       19.8])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 20, 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:02:35.385743Z",
     "start_time": "2020-06-08T03:02:35.379747Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  2.22222222,  4.44444444,  6.66666667,  8.88888889,\n",
       "       11.11111111, 13.33333333, 15.55555556, 17.77777778, 20.        ])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0 ,20, 10)  #双闭合，arg3为数据个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:02:47.763609Z",
     "start_time": "2020-06-08T03:02:47.755615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20.])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0 ,20, 11) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:03:45.890685Z",
     "start_time": "2020-06-08T03:03:45.880690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10)  #arg1 from  arg2 to   前闭后开\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:04:28.286190Z",
     "start_time": "2020-06-08T03:04:28.278195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 1, 8, 3, 0, 8, 5, 2, 5, 5])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, 10)  #arg3 = len(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:05:10.752020Z",
     "start_time": "2020-06-08T03:05:10.744025Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 1, 10)  #前闭后开"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:05:28.611997Z",
     "start_time": "2020-06-08T03:05:28.606001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 4, 5, 6, 6, 6, 6, 7, 6, 5])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(4, 8, size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:05:59.786590Z",
     "start_time": "2020-06-08T03:05:59.781593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 4, 7, 5, 7],\n",
       "       [4, 6, 6, 7, 5],\n",
       "       [6, 5, 4, 5, 5]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(4, 8, size = (3,5))    #size = tupple   shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:07:32.351076Z",
     "start_time": "2020-06-08T03:07:32.345080Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 6, 5, 6],\n",
       "       [6, 6, 5, 6],\n",
       "       [4, 5, 7, 6]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.random.randint(4, 8, size=(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:07:46.666039Z",
     "start_time": "2020-06-08T03:07:46.659044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 6, 5, 6],\n",
       "       [6, 6, 5, 6],\n",
       "       [4, 5, 7, 6]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.random.randint(4, 8, size=(3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:08:05.427709Z",
     "start_time": "2020-06-08T03:08:05.420713Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41358769878652346"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random()  #form 0-1  dtype = float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:08:14.801972Z",
     "start_time": "2020-06-08T03:08:14.790979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04881279, 0.09992856, 0.50806631, 0.20024754, 0.74415417,\n",
       "       0.192892  , 0.70084475, 0.29322811, 0.77447945, 0.00510884])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random(size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:08:20.914531Z",
     "start_time": "2020-06-08T03:08:20.908534Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.11285765, 0.11095367, 0.24766823, 0.0232363 , 0.72732115],\n",
       "       [0.34003494, 0.19750316, 0.90917959, 0.97834699, 0.53280254],\n",
       "       [0.25913185, 0.58381262, 0.32569065, 0.88889931, 0.62640453]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random(size = (3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:08:44.965098Z",
     "start_time": "2020-06-08T03:08:44.958102Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19459960304411825"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal()  #standard normal dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:23:09.659442Z",
     "start_time": "2020-06-08T03:23:09.653444Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -9.28554011,   2.74608204, -15.30129356,  11.52586796,\n",
       "         -9.03398446],\n",
       "       [  1.85628367,   2.0523546 ,   4.79998645,  11.44001244,\n",
       "         -6.10184167],\n",
       "       [  2.83441843,  14.32059651,  -4.11855559,  32.17276051,\n",
       "        -11.14927612]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(0, 10,(3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:26:34.911892Z",
     "start_time": "2020-06-08T03:26:34.905895Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random.normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:27:16.021598Z",
     "start_time": "2020-06-08T03:27:16.016601Z"
    }
   },
   "outputs": [],
   "source": [
    "np.random?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-08T03:27:33.774328Z",
     "start_time": "2020-06-08T03:27:33.756339Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function normal:\n",
      "\n",
      "normal(...) method of numpy.random.mtrand.RandomState instance\n",
      "    normal(loc=0.0, scale=1.0, size=None)\n",
      "    \n",
      "    Draw random samples from a normal (Gaussian) distribution.\n",
      "    \n",
      "    The probability density function of the normal distribution, first\n",
      "    derived by De Moivre and 200 years later by both Gauss and Laplace\n",
      "    independently [2]_, is often called the bell curve because of\n",
      "    its characteristic shape (see the example below).\n",
      "    \n",
      "    The normal distributions occurs often in nature.  For example, it\n",
      "    describes the commonly occurring distribution of samples influenced\n",
      "    by a large number of tiny, random disturbances, each with its own\n",
      "    unique distribution [2]_.\n",
      "    \n",
      "    .. note::\n",
      "        New code should use the ``normal`` method of a ``default_rng()``\n",
      "        instance instead; see `random-quick-start`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    loc : float or array_like of floats\n",
      "        Mean (\"centre\") of the distribution.\n",
      "    scale : float or array_like of floats\n",
      "        Standard deviation (spread or \"width\") of the distribution. Must be\n",
      "        non-negative.\n",
      "    size : int or tuple of ints, optional\n",
      "        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
      "        ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
      "        a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
      "        Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray or scalar\n",
      "        Drawn samples from the parameterized normal distribution.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scipy.stats.norm : probability density function, distribution or\n",
      "        cumulative density function, etc.\n",
      "    Generator.normal: which should be used for new code.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The probability density for the Gaussian distribution is\n",
      "    \n",
      "    .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n",
      "                     e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n",
      "    \n",
      "    where :math:`\\mu` is the mean and :math:`\\sigma` the standard\n",
      "    deviation. The square of the standard deviation, :math:`\\sigma^2`,\n",
      "    is called the variance.\n",
      "    \n",
      "    The function has its peak at the mean, and its \"spread\" increases with\n",
      "    the standard deviation (the function reaches 0.607 times its maximum at\n",
      "    :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n",
      "    normal is more likely to return samples lying close to the mean, rather\n",
      "    than those far away.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] Wikipedia, \"Normal distribution\",\n",
      "           https://en.wikipedia.org/wiki/Normal_distribution\n",
      "    .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability,\n",
      "           Random Variables and Random Signal Principles\", 4th ed., 2001,\n",
      "           pp. 51, 51, 125.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Draw samples from the distribution:\n",
      "    \n",
      "    >>> mu, sigma = 0, 0.1 # mean and standard deviation\n",
      "    >>> s = np.random.normal(mu, sigma, 1000)\n",
      "    \n",
      "    Verify the mean and the variance:\n",
      "    \n",
      "    >>> abs(mu - np.mean(s))\n",
      "    0.0  # may vary\n",
      "    \n",
      "    >>> abs(sigma - np.std(s, ddof=1))\n",
      "    0.1  # may vary\n",
      "    \n",
      "    Display the histogram of the samples, along with\n",
      "    the probability density function:\n",
      "    \n",
      "    >>> import matplotlib.pyplot as plt\n",
      "    >>> count, bins, ignored = plt.hist(s, 30, density=True)\n",
      "    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n",
      "    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
      "    ...          linewidth=2, color='r')\n",
      "    >>> plt.show()\n",
      "    \n",
      "    Two-by-four array of samples from N(3, 6.25):\n",
      "    \n",
      "    >>> np.random.normal(3, 2.5, size=(2, 4))\n",
      "    array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random\n",
      "           [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(np.random.normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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