numpy_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hello，我是宁萌，我制作了[【Python】NumPy入门教程](https://www.bilibili.com/video/BV1yq4y137w7/)，以下为视频示例中的代码，结合视频练习代码，效果更佳哦！欢迎多多交流呀！\n",
    "\n",
    "联系我：B站：[宁萌Julie](https://space.bilibili.com/40990299), email:Juliecodestack@126.com。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、通过简单例子认识NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.shape,ndim,dtype,size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums1=np.array([1,2,3])\n",
    "nums1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "#数组的大小，每一维上有多少元素\n",
    "print(nums1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "#数组的维度\n",
    "print(nums1.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nums1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int32\n"
     ]
    }
   ],
   "source": [
    "#数组元素的数据类型\n",
    "print(nums1.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "#数组元素个数\n",
    "print(nums1.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums2=np.array([[[ 0,  1,  2,  3],[ 4,  5,  6,  7]],[[ 8,  9, 10, 11],[12, 13, 14, 15]],\n",
    "                 [[16, 17, 18, 19],[20, 21, 22, 23]]])\n",
    "nums2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2, 4)\n",
      "3\n",
      "int32\n",
      "24\n"
     ]
    }
   ],
   "source": [
    "print(nums2.shape)\n",
    "print(nums2.ndim)\n",
    "print(nums2.dtype)\n",
    "print(nums2.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  1.,  2.,  3.],\n",
       "        [ 4.,  5.,  6.,  7.]],\n",
       "\n",
       "       [[ 8.,  9., 10., 11.],\n",
       "        [12., 13., 14., 15.]],\n",
       "\n",
       "       [[16., 17., 18., 19.],\n",
       "        [20., 21., 22., 23.]]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums2_n=np.array([[[ 0,  1,  2,  3],[ 4,  5,  6,  7]],[[ 8,  9, 10, 11],[12, 13, 14, 15]],\n",
    "                 [[16, 17, 18, 19],[20, 21, 22, 23]]],dtype=float)\n",
    "nums2_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums2_n.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['apple', 'banana', 'cherry', 'apple', 'cherry'], dtype='<U6')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit=np.array(['apple','banana','cherry','apple','cherry'])\n",
    "fruit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True, False])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruit=='apple'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.NumPy的向量化运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nums3:\n",
      " [[1 2 3]\n",
      " [4 5 6]]\n",
      "nums4:\n",
      " [[3 5 6]\n",
      " [1 7 8]]\n",
      "nums4+nums3:\n",
      " [[ 4  7  9]\n",
      " [ 5 12 14]]\n"
     ]
    }
   ],
   "source": [
    "#两个形状（shape）相同的数组，可以进行向量化运算（元素与元素）。\n",
    "nums3=np.array([[1,2,3],[4,5,6]])\n",
    "print('nums3:\\n',nums3)\n",
    "nums4=np.array([[3,5,6],[1,7,8]])\n",
    "print('nums4:\\n',nums4)\n",
    "print('nums4+nums3:\\n',nums4+nums3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  3  3]\n",
      " [-3  2  2]]\n",
      "[[ 3 10 18]\n",
      " [ 4 35 48]]\n",
      "[[3.         2.5        2.        ]\n",
      " [0.25       1.4        1.33333333]]\n"
     ]
    }
   ],
   "source": [
    "print(nums4-nums3)\n",
    "print(nums4*nums3)\n",
    "print(nums4/nums3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "广播机制：运算时会把标量（数字）和低维数组扩充至匹配的高维。\n",
    "\n",
    "这里先简单看两个例子，后面还会详细介绍什么样的低维数组才能使用广播机制进行运算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums3+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4, 6],\n",
       "       [5, 7, 9]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums3+nums1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (2,3) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-d096653177a3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mnums1_n\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;31m#注意：如果运行下面这行语句会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mnums3\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mnums1_n\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (2,3) (2,) "
     ]
    }
   ],
   "source": [
    "#无法应用广播的举例\n",
    "nums1_n=np.array([1,2])\n",
    "#注意：如果运行下面这行语句会报错\n",
    "nums3+nums1_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 1 1]\n",
      " [3 2 5]\n",
      " [6 7 4]\n",
      " [7 8 1]\n",
      " [6 8 5]\n",
      " [4 2 6]\n",
      " [6 1 9]\n",
      " [6 3 4]\n",
      " [4 3 3]\n",
      " [3 4 7]\n",
      " [4 9 1]\n",
      " [8 7 2]\n",
      " [8 1 9]\n",
      " [9 2 7]\n",
      " [3 7 9]\n",
      " [4 1 2]\n",
      " [1 5 5]\n",
      " [7 9 9]\n",
      " [3 3 3]\n",
      " [4 8 6]]\n",
      "[[2.5 0.5 0.3]\n",
      " [1.5 1.  1.5]\n",
      " [3.  3.5 1.2]\n",
      " [3.5 4.  0.3]\n",
      " [3.  4.  1.5]\n",
      " [2.  1.  1.8]\n",
      " [3.  0.5 2.7]\n",
      " [3.  1.5 1.2]\n",
      " [2.  1.5 0.9]\n",
      " [1.5 2.  2.1]\n",
      " [2.  4.5 0.3]\n",
      " [4.  3.5 0.6]\n",
      " [4.  0.5 2.7]\n",
      " [4.5 1.  2.1]\n",
      " [1.5 3.5 2.7]\n",
      " [2.  0.5 0.6]\n",
      " [0.5 2.5 1.5]\n",
      " [3.5 4.5 2.7]\n",
      " [1.5 1.5 0.9]\n",
      " [2.  4.  1.8]]\n"
     ]
    }
   ],
   "source": [
    "nums5=np.random.randint(1,10,(20,3))\n",
    "print(nums5)\n",
    "weight=np.array([0.5,0.5,0.3])\n",
    "print(nums5*weight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、NumPy运算速度快（与list对比）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "元素的数据类型：\n",
    "- NumPy: 必须同一类型，连续内存\n",
    "- list: 可以不同类型\n",
    "\n",
    "与list对比，NumPy占用内存更少，更加高效利用内存。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "list1=[1,2,3]\n",
    "print(list1)\n",
    "arr1=np.array(list1)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 'a', 2.2]\n",
      "['1' 'a' '2.2']\n"
     ]
    }
   ],
   "source": [
    "list2=[1,'a',2.2]\n",
    "print(list2)\n",
    "arr2=np.array(list2)\n",
    "print(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<U32\n"
     ]
    }
   ],
   "source": [
    "print(arr2.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对于序列间的运算：\n",
    "- NumPy：向量化运算(vectorization)，批处理\n",
    "- list：使用循环\n",
    "\n",
    "与list对比，NumPy运算速度明显更快。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list_a: [1, 2, 3, 4, 5]\n",
      "arr_a: [1 2 3 4 5]\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
      "[ 7  9 11 13 15]\n",
      "[7, 9, 11, 13, 15]\n"
     ]
    }
   ],
   "source": [
    "list_a=[1,2,3,4,5]\n",
    "list_b=[6,7,8,9,10]\n",
    "arr_a=np.array(list_a)\n",
    "arr_b=np.array(list_b)\n",
    "print('list_a:',list_a)\n",
    "print('arr_a:',arr_a)\n",
    "\n",
    "#注意看list和ndarray加法的不同\n",
    "print(list_a+list_b)\n",
    "print(arr_a+arr_b)\n",
    "\n",
    "#list元素相加要用循环实现\n",
    "list_sum=[]\n",
    "for i in range(len(list_a)):\n",
    "    list_sum.append(list_a[i]+list_b[i])\n",
    "print(list_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.04 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "10.6 µs ± 970 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "list3=list(range(10000))\n",
    "%timeit list4=[i+5 for i in list3]\n",
    "arr3=np.arange(10000)\n",
    "%timeit arr4=arr3+5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、NumPy的使用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 生成ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.通过list或tuple生成ndarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过list生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "(3,)\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "data=np.array([1,2,3])\n",
    "#data=np.array([1,2,3],dtype='float')\n",
    "print(data)\n",
    "print(data.shape)\n",
    "print(data.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]]\n",
      "(1, 3)\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "data=np.array([[1,2,3]])\n",
    "print(data)\n",
    "print(data.shape)\n",
    "print(data.ndim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过tuple生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.array((1,2,3))\n",
    "print(data.shape)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#生成一维数组\n",
    "#data=np.array(((1,2,3)))\n",
    "#生成二维数组\n",
    "data=np.array(((1,2,3),))\n",
    "#data=np.array([(1,2,3)])\n",
    "print(data.shape)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.array(((1,2,3),(4,5,6)))\n",
    "print(data.shape)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.通过NumPy自带函数生成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成一维数组：\n",
    "- 等差数列：np.arange(起,止(不包括),间隔)；np.linspace(起,止(包括),个数)\n",
    "- 等比数列：np.logspace(起始幂,终止幂,个数,base=)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 7, 9])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data=np.arange(10)\n",
    "#data=np.arange(3,10)\n",
    "data=np.arange(3,10,2)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3],\n",
       "        [ 4,  5]],\n",
       "\n",
       "       [[ 6,  7],\n",
       "        [ 8,  9],\n",
       "        [10, 11]]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reshape()函数设置为shape，tuple参数\n",
    "data=np.arange(12).reshape((2,3,2))\n",
    "#用array.reshape()时,直接输入各维度大小也可以\n",
    "#data=np.arange(12).reshape(2,3,2)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.        ,  4.16666667,  5.33333333,  6.5       ,  7.66666667,\n",
       "        8.83333333, 10.        ])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.linspace(3,10,7)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]\n",
      "[ 5.  7.  9. 11. 13. 15. 17. 19. 21. 23.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157a2872948>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(1,10,10)\n",
    "print(x)\n",
    "y=x*2+3\n",
    "print(y)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1000.        ,  1584.89319246,  2511.88643151,  3981.07170553,\n",
       "        6309.5734448 , 10000.        ])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.logspace(3,4,6)\n",
    "#data=np.logspace(3,4,6,base=2)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成二维数组：np.eye(),np.diag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.eye(3)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 0, 0],\n",
       "       [0, 4, 0],\n",
       "       [0, 0, 5]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=np.diag([3,4,5])\n",
    "#data=np.diag([3,4,5],1)\n",
    "#data=np.diag([3,4,5],-2)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成N维数组：\n",
    "- 全0：np.zeros(),np.zeros_like()\n",
    "- 空：np.empty(),np.empty_like()\n",
    "- 全1：np.ones(),np.ones_like()\n",
    "- 全为某个数：np.full(),np.full_like()\n",
    "- 随机：np.random.rand(),np.random.randn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=np.zeros((3,4))\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 0., 0., 0.],\n",
       "       [0., 4., 0., 0.],\n",
       "       [0., 0., 5., 0.]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=np.array([3,4,5])\n",
    "np.fill_diagonal(data1,data2)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 0., 0., 0.],\n",
       "       [0., 6., 0., 0.],\n",
       "       [0., 0., 6., 0.]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.fill_diagonal(data1,6)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]],\n",
       "\n",
       "       [[0, 0, 0, 0],\n",
       "        [0, 0, 0, 0]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3=np.zeros_like(nums2)\n",
    "data3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 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.]]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=np.empty((2,3,4))\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16843008, 16843009, 16843009],\n",
       "       [16843009, 16843009, 16843009]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=np.empty_like(nums3)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=np.ones((3,4))\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]],\n",
       "\n",
       "       [[1, 1, 1, 1],\n",
       "        [1, 1, 1, 1]]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=np.ones_like(nums2)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 6, 6, 6],\n",
       "       [6, 6, 6, 6],\n",
       "       [6, 6, 6, 6]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=np.full((3,4),6)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[3, 3, 3, 3],\n",
       "        [3, 3, 3, 3]],\n",
       "\n",
       "       [[3, 3, 3, 3],\n",
       "        [3, 3, 3, 3]],\n",
       "\n",
       "       [[3, 3, 3, 3],\n",
       "        [3, 3, 3, 3]]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=np.full_like(nums2,3)\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.49671415 -0.1382643   0.64768854  1.52302986 -0.23415337]\n",
      "  [-0.23413696  1.57921282  0.76743473 -0.46947439  0.54256004]\n",
      "  [-0.46341769 -0.46572975  0.24196227 -1.91328024 -1.72491783]\n",
      "  [-0.56228753 -1.01283112  0.31424733 -0.90802408 -1.4123037 ]]\n",
      "\n",
      " [[ 1.46564877 -0.2257763   0.0675282  -1.42474819 -0.54438272]\n",
      "  [ 0.11092259 -1.15099358  0.37569802 -0.60063869 -0.29169375]\n",
      "  [-0.60170661  1.85227818 -0.01349722 -1.05771093  0.82254491]\n",
      "  [-1.22084365  0.2088636  -1.95967012 -1.32818605  0.19686124]]\n",
      "\n",
      " [[ 0.73846658  0.17136828 -0.11564828 -0.3011037  -1.47852199]\n",
      "  [-0.71984421 -0.46063877  1.05712223  0.34361829 -1.76304016]\n",
      "  [ 0.32408397 -0.38508228 -0.676922    0.61167629  1.03099952]\n",
      "  [ 0.93128012 -0.83921752 -0.30921238  0.33126343  0.97554513]]]\n",
      "-0.15465468307048993\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "data1=np.random.randn(3,4,5)\n",
    "print(data1)\n",
    "print(np.mean(data1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.33089802 0.06355835 0.31098232 0.32518332 0.72960618]\n",
      "  [0.63755747 0.88721274 0.47221493 0.11959425 0.71324479]\n",
      "  [0.76078505 0.5612772  0.77096718 0.4937956  0.52273283]\n",
      "  [0.42754102 0.02541913 0.10789143 0.03142919 0.63641041]]\n",
      "\n",
      " [[0.31435598 0.50857069 0.90756647 0.24929223 0.41038292]\n",
      "  [0.75555114 0.22879817 0.07697991 0.28975145 0.16122129]\n",
      "  [0.92969765 0.80812038 0.63340376 0.87146059 0.80367208]\n",
      "  [0.18657006 0.892559   0.53934224 0.80744016 0.8960913 ]]\n",
      "\n",
      " [[0.31800347 0.11005192 0.22793516 0.42710779 0.81801477]\n",
      "  [0.86073058 0.00695213 0.5107473  0.417411   0.22210781]\n",
      "  [0.11986537 0.33761517 0.9429097  0.32320293 0.51879062]\n",
      "  [0.70301896 0.3636296  0.97178208 0.96244729 0.2517823 ]]]\n",
      "0.4935539138404294\n"
     ]
    }
   ],
   "source": [
    "data2=np.random.rand(3,4,5)\n",
    "print(data2)\n",
    "print(np.mean(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 1, 5],\n",
       "       [1, 8, 1, 1],\n",
       "       [2, 2, 6, 7]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3=np.random.randint(1,10,(3,4))\n",
    "data3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.通过数组的变换、拼接生成"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "索引与轴的概念"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1=np.arange(10)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引某个元素\n",
    "arr1[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#切片\n",
    "arr1[3:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10 10 10]\n",
      "[ 0  1  2 10 10 10  6  7  8  9]\n"
     ]
    }
   ],
   "source": [
    "part=arr1[3:6]\n",
    "part[:]=10\n",
    "print(part)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2=np.arange(12).reshape(3,4)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6, 7])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引行\n",
    "arr2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6, 10])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引列\n",
    "arr2[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "#索引某个元素，以下两种方式都可以\n",
    "print(arr2[1][2])\n",
    "print(arr2[1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3=np.arange(24).reshape(3,2,4)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3[0,1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "轴变换：\n",
    "- T(只用于二维数组)\n",
    "- transpose \n",
    "- swapaxes \n",
    "\n",
    "维度变化：\n",
    "- reshape\n",
    "- ravel,flatten\n",
    "- newaxis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4=arr2.T\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  4,  8],\n",
       "       [ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.transpose(1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  8, 16],\n",
       "        [ 1,  9, 17],\n",
       "        [ 2, 10, 18],\n",
       "        [ 3, 11, 19]],\n",
       "\n",
       "       [[ 4, 12, 20],\n",
       "        [ 5, 13, 21],\n",
       "        [ 6, 14, 22],\n",
       "        [ 7, 15, 23]]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.transpose(1,2,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 8,  9, 10, 11],\n",
       "        [16, 17, 18, 19]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [12, 13, 14, 15],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.swapaxes(1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 4, 6])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4=np.array([1,3,4,6])\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [4, 6]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_n=arr4.reshape(2,2)\n",
    "arr_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 4, 6])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ravel()函数产生的数组是原数组的view\n",
    "arr_r=arr_n.ravel()\n",
    "arr_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 2 6]\n",
      "[[1 2]\n",
      " [2 6]]\n"
     ]
    }
   ],
   "source": [
    "arr_r[1:3]=2\n",
    "print(arr_r)\n",
    "print(arr_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 2, 6])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#flatten函数产生的数组是原数组的copy\n",
    "arr_f=arr_n.flatten()\n",
    "arr_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 3 6]\n",
      "[[1 2]\n",
      " [2 6]]\n"
     ]
    }
   ],
   "source": [
    "arr_f[1:3]=3\n",
    "print(arr_f)\n",
    "print(arr_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 2, 6]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4.reshape(1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1],\n",
       "        [2],\n",
       "        [2],\n",
       "        [6]]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4.reshape(1,4,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 2, 6]])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4[np.newaxis,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1],\n",
       "        [2],\n",
       "        [2],\n",
       "        [6]]])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4[np.newaxis,:,np.newaxis]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "什么样的低维数组可以进行广播运算？\n",
    "\n",
    "从右往左，每个维度的大小或者与高维数组相同，或者为1。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 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]]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big=np.arange(24).reshape(2,3,4)\n",
    "big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 2,  3,  4,  5],\n",
       "        [ 6,  7,  8,  9],\n",
       "        [10, 11, 12, 13]],\n",
       "\n",
       "       [[14, 15, 16, 17],\n",
       "        [18, 19, 20, 21],\n",
       "        [22, 23, 24, 25]]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#一维数组\n",
    "#small_a=np.array([1,2,3,4])\n",
    "small_a=np.array([2,])\n",
    "#small_a=np.array([1,2])#不匹配\n",
    "#small_a=np.array([1,2,3])#不匹配\n",
    "\n",
    "print(small_a)\n",
    "big+small_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  2,  4,  6],\n",
       "        [ 8, 10, 12, 14],\n",
       "        [16, 18, 20, 22]],\n",
       "\n",
       "       [[12, 14, 16, 18],\n",
       "        [20, 22, 24, 26],\n",
       "        [28, 30, 32, 34]]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#二维数组\n",
    "small_b=np.arange(12).reshape(3,4)\n",
    "#small_b=np.arange(3).reshape(3,1)\n",
    "#small_b=np.arange(4).reshape(1,4)\n",
    "#small_b=np.arange(8).reshape(2,4)#不匹配\n",
    "#small_b=np.arange(6).reshape(2,3)#不匹配\n",
    "\n",
    "print(small_b)\n",
    "big+small_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  2,  4,  6],\n",
       "        [ 8, 10, 12, 14],\n",
       "        [16, 18, 20, 22]],\n",
       "\n",
       "       [[12, 14, 16, 18],\n",
       "        [20, 22, 24, 26],\n",
       "        [28, 30, 32, 34]]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#三维数组\n",
    "small_c=np.arange(12).reshape(1,3,4)\n",
    "#small_c=np.arange(8).reshape(2,1,4)\n",
    "#small_c=np.arange(6).reshape(2,3,1)\n",
    "#small_c=np.arange(4).reshape(1,1,4)\n",
    "#其它的可以自己试试^V^\n",
    "#small_c=np.arange(6).reshape(2,3,2)#不匹配\n",
    "\n",
    "print(small_c)\n",
    "big+small_c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fancy indexing（花式索引）：用整数数组索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5=np.arange(24).reshape(6,4)\n",
    "arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 17, 18, 19],\n",
       "       [12, 13, 14, 15],\n",
       "       [ 4,  5,  6,  7]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[[4,3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  1],\n",
       "       [ 7,  5],\n",
       "       [11,  9],\n",
       "       [15, 13],\n",
       "       [19, 17],\n",
       "       [23, 21]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[:,[3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "shape mismatch: indexing arrays could not be broadcast together with shapes (3,) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-147-078952d113ec>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#注意：如果运行下面这行语句会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marr5\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: shape mismatch: indexing arrays could not be broadcast together with shapes (3,) (2,) "
     ]
    }
   ],
   "source": [
    "#注意：如果运行下面这行语句会报错\n",
    "arr5[[4,3,1],[3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 17],\n",
       "       [15, 13],\n",
       "       [ 7,  5]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[[4,3,1]][:,[3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 13])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[[4,3],[3,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 15,  5])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[[4,3,1],[0,3,1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "boolean indexing（布尔索引）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[arr5>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr6=np.arange(6)\n",
    "arr6>3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[arr6>3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 6,  7],\n",
       "       [10, 11],\n",
       "       [14, 15],\n",
       "       [18, 19],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr7=np.arange(4)\n",
    "arr5[:,arr7>1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数组拼接\n",
    "\n",
    "- hstack,vstack\n",
    "- concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]]\n",
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]]\n",
      "[[0 1]\n",
      " [2 3]\n",
      " [4 5]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "arr_a=np.arange(6).reshape(3,2)\n",
    "print(arr_a)\n",
    "arr_b=np.arange(15).reshape(3,5)\n",
    "print(arr_b)\n",
    "arr_c=np.arange(8).reshape(4,2)\n",
    "print(arr_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0,  1,  2,  3,  4],\n",
       "       [ 2,  3,  5,  6,  7,  8,  9],\n",
       "       [ 4,  5, 10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((arr_a,arr_b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((arr_a,arr_c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0,  1,  2,  3,  4],\n",
       "       [ 2,  3,  5,  6,  7,  8,  9],\n",
       "       [ 4,  5, 10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr_a,arr_b],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr_a,arr_c],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.与其它Python库交互，如pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'exercise', 'flights', 'fmri', 'gammas', 'geyser', 'iris', 'mpg', 'penguins', 'planets', 'taxis', 'tips', 'titanic']\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "print(sns.get_dataset_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4\n",
       "..          ...   ...     ...    ...   ...     ...   ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2\n",
       "\n",
       "[244 rows x 7 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tip_df=sns.load_dataset('tips')\n",
    "tip_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.01\n",
       "1      1.66\n",
       "2      3.50\n",
       "3      3.31\n",
       "4      3.61\n",
       "       ... \n",
       "239    5.92\n",
       "240    2.00\n",
       "241    2.00\n",
       "242    1.75\n",
       "243    3.00\n",
       "Name: tip, Length: 244, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tip_df['tip']是series，带索引\n",
    "tip_df['tip']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.01,  1.66,  3.5 ,  3.31,  3.61,  4.71,  2.  ,  3.12,  1.96,\n",
       "        3.23,  1.71,  5.  ,  1.57,  3.  ,  3.02,  3.92,  1.67,  3.71,\n",
       "        3.5 ,  3.35,  4.08,  2.75,  2.23,  7.58,  3.18,  2.34,  2.  ,\n",
       "        2.  ,  4.3 ,  3.  ,  1.45,  2.5 ,  3.  ,  2.45,  3.27,  3.6 ,\n",
       "        2.  ,  3.07,  2.31,  5.  ,  2.24,  2.54,  3.06,  1.32,  5.6 ,\n",
       "        3.  ,  5.  ,  6.  ,  2.05,  3.  ,  2.5 ,  2.6 ,  5.2 ,  1.56,\n",
       "        4.34,  3.51,  3.  ,  1.5 ,  1.76,  6.73,  3.21,  2.  ,  1.98,\n",
       "        3.76,  2.64,  3.15,  2.47,  1.  ,  2.01,  2.09,  1.97,  3.  ,\n",
       "        3.14,  5.  ,  2.2 ,  1.25,  3.08,  4.  ,  3.  ,  2.71,  3.  ,\n",
       "        3.4 ,  1.83,  5.  ,  2.03,  5.17,  2.  ,  4.  ,  5.85,  3.  ,\n",
       "        3.  ,  3.5 ,  1.  ,  4.3 ,  3.25,  4.73,  4.  ,  1.5 ,  3.  ,\n",
       "        1.5 ,  2.5 ,  3.  ,  2.5 ,  3.48,  4.08,  1.64,  4.06,  4.29,\n",
       "        3.76,  4.  ,  3.  ,  1.  ,  4.  ,  2.55,  4.  ,  3.5 ,  5.07,\n",
       "        1.5 ,  1.8 ,  2.92,  2.31,  1.68,  2.5 ,  2.  ,  2.52,  4.2 ,\n",
       "        1.48,  2.  ,  2.  ,  2.18,  1.5 ,  2.83,  1.5 ,  2.  ,  3.25,\n",
       "        1.25,  2.  ,  2.  ,  2.  ,  2.75,  3.5 ,  6.7 ,  5.  ,  5.  ,\n",
       "        2.3 ,  1.5 ,  1.36,  1.63,  1.73,  2.  ,  2.5 ,  2.  ,  2.74,\n",
       "        2.  ,  2.  ,  5.14,  5.  ,  3.75,  2.61,  2.  ,  3.5 ,  2.5 ,\n",
       "        2.  ,  2.  ,  3.  ,  3.48,  2.24,  4.5 ,  1.61,  2.  , 10.  ,\n",
       "        3.16,  5.15,  3.18,  4.  ,  3.11,  2.  ,  2.  ,  4.  ,  3.55,\n",
       "        3.68,  5.65,  3.5 ,  6.5 ,  3.  ,  5.  ,  3.5 ,  2.  ,  3.5 ,\n",
       "        4.  ,  1.5 ,  4.19,  2.56,  2.02,  4.  ,  1.44,  2.  ,  5.  ,\n",
       "        2.  ,  2.  ,  4.  ,  2.01,  2.  ,  2.5 ,  4.  ,  3.23,  3.41,\n",
       "        3.  ,  2.03,  2.23,  2.  ,  5.16,  9.  ,  2.5 ,  6.5 ,  1.1 ,\n",
       "        3.  ,  1.5 ,  1.44,  3.09,  2.2 ,  3.48,  1.92,  3.  ,  1.58,\n",
       "        2.5 ,  2.  ,  3.  ,  2.72,  2.88,  2.  ,  3.  ,  3.39,  1.47,\n",
       "        3.  ,  1.25,  1.  ,  1.17,  4.67,  5.92,  2.  ,  2.  ,  1.75,\n",
       "        3.  ])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tip=tip_df['tip'].values\n",
    "tip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16.99, 10.34, 21.01, 23.68, 24.59, 25.29,  8.77, 26.88, 15.04,\n",
       "       14.78, 10.27, 35.26, 15.42, 18.43, 14.83, 21.58, 10.33, 16.29,\n",
       "       16.97, 20.65, 17.92, 20.29, 15.77, 39.42, 19.82, 17.81, 13.37,\n",
       "       12.69, 21.7 , 19.65,  9.55, 18.35, 15.06, 20.69, 17.78, 24.06,\n",
       "       16.31, 16.93, 18.69, 31.27, 16.04, 17.46, 13.94,  9.68, 30.4 ,\n",
       "       18.29, 22.23, 32.4 , 28.55, 18.04, 12.54, 10.29, 34.81,  9.94,\n",
       "       25.56, 19.49, 38.01, 26.41, 11.24, 48.27, 20.29, 13.81, 11.02,\n",
       "       18.29, 17.59, 20.08, 16.45,  3.07, 20.23, 15.01, 12.02, 17.07,\n",
       "       26.86, 25.28, 14.73, 10.51, 17.92, 27.2 , 22.76, 17.29, 19.44,\n",
       "       16.66, 10.07, 32.68, 15.98, 34.83, 13.03, 18.28, 24.71, 21.16,\n",
       "       28.97, 22.49,  5.75, 16.32, 22.75, 40.17, 27.28, 12.03, 21.01,\n",
       "       12.46, 11.35, 15.38, 44.3 , 22.42, 20.92, 15.36, 20.49, 25.21,\n",
       "       18.24, 14.31, 14.  ,  7.25, 38.07, 23.95, 25.71, 17.31, 29.93,\n",
       "       10.65, 12.43, 24.08, 11.69, 13.42, 14.26, 15.95, 12.48, 29.8 ,\n",
       "        8.52, 14.52, 11.38, 22.82, 19.08, 20.27, 11.17, 12.26, 18.26,\n",
       "        8.51, 10.33, 14.15, 16.  , 13.16, 17.47, 34.3 , 41.19, 27.05,\n",
       "       16.43,  8.35, 18.64, 11.87,  9.78,  7.51, 14.07, 13.13, 17.26,\n",
       "       24.55, 19.77, 29.85, 48.17, 25.  , 13.39, 16.49, 21.5 , 12.66,\n",
       "       16.21, 13.81, 17.51, 24.52, 20.76, 31.71, 10.59, 10.63, 50.81,\n",
       "       15.81,  7.25, 31.85, 16.82, 32.9 , 17.89, 14.48,  9.6 , 34.63,\n",
       "       34.65, 23.33, 45.35, 23.17, 40.55, 20.69, 20.9 , 30.46, 18.15,\n",
       "       23.1 , 15.69, 19.81, 28.44, 15.48, 16.58,  7.56, 10.34, 43.11,\n",
       "       13.  , 13.51, 18.71, 12.74, 13.  , 16.4 , 20.53, 16.47, 26.59,\n",
       "       38.73, 24.27, 12.76, 30.06, 25.89, 48.33, 13.27, 28.17, 12.9 ,\n",
       "       28.15, 11.59,  7.74, 30.14, 12.16, 13.42,  8.58, 15.98, 13.42,\n",
       "       16.27, 10.09, 20.45, 13.28, 22.12, 24.01, 15.69, 11.61, 10.77,\n",
       "       15.53, 10.07, 12.6 , 32.83, 35.83, 29.03, 27.18, 22.67, 17.82,\n",
       "       18.78])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bill=tip_df['total_bill'].values\n",
    "bill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.05944673, 0.16054159, 0.16658734, 0.13978041, 0.14680765,\n",
       "       0.18623962, 0.22805017, 0.11607143, 0.13031915, 0.21853857,\n",
       "       0.16650438, 0.14180374, 0.10181582, 0.16277808, 0.20364127,\n",
       "       0.18164968, 0.16166505, 0.22774708, 0.20624632, 0.1622276 ,\n",
       "       0.22767857, 0.13553475, 0.14140774, 0.19228818, 0.160444  ,\n",
       "       0.13138686, 0.14958863, 0.15760441, 0.19815668, 0.15267176,\n",
       "       0.15183246, 0.13623978, 0.19920319, 0.11841469, 0.18391451,\n",
       "       0.14962594, 0.12262416, 0.18133491, 0.12359551, 0.15989767,\n",
       "       0.13965087, 0.14547537, 0.2195122 , 0.13636364, 0.18421053,\n",
       "       0.16402406, 0.22492128, 0.18518519, 0.07180385, 0.16629712,\n",
       "       0.19936204, 0.2526725 , 0.14938236, 0.15694165, 0.16979656,\n",
       "       0.18009236, 0.0789266 , 0.05679667, 0.15658363, 0.13942407,\n",
       "       0.15820601, 0.14482259, 0.17967332, 0.20557682, 0.15008528,\n",
       "       0.15687251, 0.15015198, 0.3257329 , 0.09935739, 0.13924051,\n",
       "       0.16389351, 0.17574692, 0.11690246, 0.19778481, 0.14935506,\n",
       "       0.11893435, 0.171875  , 0.14705882, 0.13181019, 0.156738  ,\n",
       "       0.15432099, 0.20408163, 0.1817279 , 0.15299878, 0.12703379,\n",
       "       0.14843526, 0.15349194, 0.21881838, 0.23674626, 0.14177694,\n",
       "       0.1035554 , 0.15562472, 0.17391304, 0.26348039, 0.14285714,\n",
       "       0.11774956, 0.14662757, 0.12468828, 0.14278915, 0.12038523,\n",
       "       0.22026432, 0.19505852, 0.05643341, 0.15521855, 0.19502868,\n",
       "       0.10677083, 0.19814544, 0.17017057, 0.20614035, 0.27952481,\n",
       "       0.21428571, 0.13793103, 0.10506961, 0.10647182, 0.15558149,\n",
       "       0.20219526, 0.16939526, 0.14084507, 0.14481094, 0.12126246,\n",
       "       0.19760479, 0.12518629, 0.17531557, 0.12539185, 0.20192308,\n",
       "       0.1409396 , 0.17370892, 0.13774105, 0.17574692, 0.09553024,\n",
       "       0.07861635, 0.13961519, 0.13428827, 0.16313214, 0.17798467,\n",
       "       0.14688602, 0.19361084, 0.14134276, 0.125     , 0.20896657,\n",
       "       0.20034345, 0.19533528, 0.12138869, 0.18484288, 0.13998783,\n",
       "       0.17964072, 0.07296137, 0.13732098, 0.17689162, 0.26631158,\n",
       "       0.17768301, 0.15232292, 0.15874855, 0.0814664 , 0.10116338,\n",
       "       0.1721943 , 0.10379905, 0.15      , 0.19492158, 0.12128563,\n",
       "       0.1627907 , 0.19747235, 0.12338063, 0.14482259, 0.17133067,\n",
       "       0.14192496, 0.10789981, 0.14191107, 0.15203022, 0.18814675,\n",
       "       0.19681165, 0.1998735 , 0.71034483, 0.09984301, 0.23781213,\n",
       "       0.09452888, 0.1117943 , 0.13812155, 0.41666667, 0.10251227,\n",
       "       0.10620491, 0.24217745, 0.07717751, 0.28053517, 0.07398274,\n",
       "       0.24166264, 0.16746411, 0.06565988, 0.19283747, 0.17316017,\n",
       "       0.09560229, 0.21150934, 0.09001406, 0.13049096, 0.24125452,\n",
       "       0.19047619, 0.1934236 , 0.11598237, 0.15384615, 0.14803849,\n",
       "       0.21378942, 0.1577708 , 0.15384615, 0.15243902, 0.19483682,\n",
       "       0.19611415, 0.1282437 , 0.07745933, 0.08364236, 0.17476489,\n",
       "       0.0665336 , 0.19930475, 0.18621974, 0.18839488, 0.23074192,\n",
       "       0.08527132, 0.10657194, 0.12942192, 0.18604651, 0.10252157,\n",
       "       0.18092105, 0.25931446, 0.22377622, 0.18773467, 0.11773472,\n",
       "       0.15365704, 0.19821606, 0.14669927, 0.20481928, 0.13019892,\n",
       "       0.08329863, 0.19120459, 0.29198966, 0.13649025, 0.1931745 ,\n",
       "       0.12413108, 0.07936508, 0.03563814, 0.13033771, 0.20392697,\n",
       "       0.07358352, 0.08822232, 0.09820426, 0.15974441])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tip/bill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.99827868852459\n",
      "2.99827868852459\n"
     ]
    }
   ],
   "source": [
    "print(tip.mean())\n",
    "print(np.mean(tip))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Dinner', 'Dinner', 'Dinner', 'Dinner', 'Dinner', ..., 'Dinner', 'Dinner', 'Dinner', 'Dinner', 'Dinner']\n",
       "Length: 244\n",
       "Categories (2, object): ['Lunch', 'Dinner']"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=tip_df['time'].values\n",
    "time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True, False, False, False, False, False,\n",
       "       False, False,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time=='Dinner'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7213114754098361"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(time=='Dinner').sum()/time.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 NumPy快速运算"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数学运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n",
       "       27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39.,\n",
       "       40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50.])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.linspace(1,50,50)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.95090322e-01,  3.82683432e-01,  5.55570233e-01,  7.07106781e-01,\n",
       "        8.31469612e-01,  9.23879533e-01,  9.80785280e-01,  1.00000000e+00,\n",
       "        9.80785280e-01,  9.23879533e-01,  8.31469612e-01,  7.07106781e-01,\n",
       "        5.55570233e-01,  3.82683432e-01,  1.95090322e-01,  1.22464680e-16,\n",
       "       -1.95090322e-01, -3.82683432e-01, -5.55570233e-01, -7.07106781e-01,\n",
       "       -8.31469612e-01, -9.23879533e-01, -9.80785280e-01, -1.00000000e+00,\n",
       "       -9.80785280e-01, -9.23879533e-01, -8.31469612e-01, -7.07106781e-01,\n",
       "       -5.55570233e-01, -3.82683432e-01, -1.95090322e-01, -2.44929360e-16,\n",
       "        1.95090322e-01,  3.82683432e-01,  5.55570233e-01,  7.07106781e-01,\n",
       "        8.31469612e-01,  9.23879533e-01,  9.80785280e-01,  1.00000000e+00,\n",
       "        9.80785280e-01,  9.23879533e-01,  8.31469612e-01,  7.07106781e-01,\n",
       "        5.55570233e-01,  3.82683432e-01,  1.95090322e-01,  3.67394040e-16,\n",
       "       -1.95090322e-01, -3.82683432e-01])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.sin(x*np.pi/16)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.000e+00, 4.000e+00, 9.000e+00, 1.600e+01, 2.500e+01, 3.600e+01,\n",
       "       4.900e+01, 6.400e+01, 8.100e+01, 1.000e+02, 1.210e+02, 1.440e+02,\n",
       "       1.690e+02, 1.960e+02, 2.250e+02, 2.560e+02, 2.890e+02, 3.240e+02,\n",
       "       3.610e+02, 4.000e+02, 4.410e+02, 4.840e+02, 5.290e+02, 5.760e+02,\n",
       "       6.250e+02, 6.760e+02, 7.290e+02, 7.840e+02, 8.410e+02, 9.000e+02,\n",
       "       9.610e+02, 1.024e+03, 1.089e+03, 1.156e+03, 1.225e+03, 1.296e+03,\n",
       "       1.369e+03, 1.444e+03, 1.521e+03, 1.600e+03, 1.681e+03, 1.764e+03,\n",
       "       1.849e+03, 1.936e+03, 2.025e+03, 2.116e+03, 2.209e+03, 2.304e+03,\n",
       "       2.401e+03, 2.500e+03])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#z=np.square(x)\n",
    "z=x**2\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157ae0eae48>]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x157ae167408>]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 线性代数（矩阵运算）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "[[1 4]\n",
      " [1 1]\n",
      " [3 2]\n",
      " [2 1]]\n"
     ]
    }
   ],
   "source": [
    "mat1=np.arange(12).reshape(3,4)\n",
    "print(mat1)\n",
    "mat2=np.random.randint(1,5,(4,2))\n",
    "print(mat2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3,4) (4,2) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-177-7f8120ac4c2a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#注意：如果运行下面这行语句会报错\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmat1\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mmat2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,4) (4,2) "
     ]
    }
   ],
   "source": [
    "#注意：如果运行下面这行语句会报错\n",
    "mat1*mat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13,  8],\n",
       "       [41, 40],\n",
       "       [69, 72]])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1.dot(mat2)\n",
    "#np.dot(mat1,mat2)\n",
    "#mat1@mat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 14  38  62]\n",
      " [ 38 126 214]\n",
      " [ 62 214 366]]\n",
      "[[ 80  92 104 116]\n",
      " [ 92 107 122 137]\n",
      " [104 122 140 158]\n",
      " [116 137 158 179]]\n"
     ]
    }
   ],
   "source": [
    "print(mat1@mat1.T)\n",
    "print(mat1.T@mat1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 统计运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df=sns.load_dataset(\"iris\")\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 0.2],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5. , 3.4, 1.6, 0.4],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=iris_df.iloc[:,0:4].values\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2078.7"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([876.5, 458.6, 563.7, 179.9])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.2,  9.5,  9.4,  9.4, 10.2, 11.4,  9.7, 10.1,  8.9,  9.6, 10.8,\n",
       "       10. ,  9.3,  8.5, 11.2, 12. , 11. , 10.3, 11.5, 10.7, 10.7, 10.7,\n",
       "        9.4, 10.6, 10.3,  9.8, 10.4, 10.4, 10.2,  9.7,  9.7, 10.7, 10.9,\n",
       "       11.3,  9.7,  9.6, 10.5, 10. ,  8.9, 10.2, 10.1,  8.4,  9.1, 10.7,\n",
       "       11.2,  9.5, 10.7,  9.4, 10.7,  9.9, 16.3, 15.6, 16.4, 13.1, 15.4,\n",
       "       14.3, 15.9, 11.6, 15.4, 13.2, 11.5, 14.6, 13.2, 15.1, 13.4, 15.6,\n",
       "       14.6, 13.6, 14.4, 13.1, 15.7, 14.2, 15.2, 14.8, 14.9, 15.4, 15.8,\n",
       "       16.4, 14.9, 12.8, 12.8, 12.6, 13.6, 15.4, 14.4, 15.5, 16. , 14.3,\n",
       "       14. , 13.3, 13.7, 15.1, 13.6, 11.6, 13.8, 14.1, 14.1, 14.7, 11.7,\n",
       "       13.9, 18.1, 15.5, 18.1, 16.6, 17.5, 19.3, 13.6, 18.3, 16.8, 19.4,\n",
       "       16.8, 16.3, 17.4, 15.2, 16.1, 17.2, 16.8, 20.4, 19.5, 14.7, 18.1,\n",
       "       15.3, 19.2, 15.7, 17.8, 18.2, 15.6, 15.8, 16.9, 17.6, 18.2, 20.1,\n",
       "       17. , 15.7, 15.7, 19.1, 17.7, 16.8, 15.6, 17.5, 17.8, 17.4, 15.5,\n",
       "       18.2, 18.2, 17.2, 15.7, 16.7, 17.3, 15.8])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.100e+00, 3.500e+00, 1.400e+00, 2.000e-01],\n",
       "       [1.000e+01, 6.500e+00, 2.800e+00, 4.000e-01],\n",
       "       [1.470e+01, 9.700e+00, 4.100e+00, 6.000e-01],\n",
       "       [1.930e+01, 1.280e+01, 5.600e+00, 8.000e-01],\n",
       "       [2.430e+01, 1.640e+01, 7.000e+00, 1.000e+00],\n",
       "       [2.970e+01, 2.030e+01, 8.700e+00, 1.400e+00],\n",
       "       [3.430e+01, 2.370e+01, 1.010e+01, 1.700e+00],\n",
       "       [3.930e+01, 2.710e+01, 1.160e+01, 1.900e+00],\n",
       "       [4.370e+01, 3.000e+01, 1.300e+01, 2.100e+00],\n",
       "       [4.860e+01, 3.310e+01, 1.450e+01, 2.200e+00],\n",
       "       [5.400e+01, 3.680e+01, 1.600e+01, 2.400e+00],\n",
       "       [5.880e+01, 4.020e+01, 1.760e+01, 2.600e+00],\n",
       "       [6.360e+01, 4.320e+01, 1.900e+01, 2.700e+00],\n",
       "       [6.790e+01, 4.620e+01, 2.010e+01, 2.800e+00],\n",
       "       [7.370e+01, 5.020e+01, 2.130e+01, 3.000e+00],\n",
       "       [7.940e+01, 5.460e+01, 2.280e+01, 3.400e+00],\n",
       "       [8.480e+01, 5.850e+01, 2.410e+01, 3.800e+00],\n",
       "       [8.990e+01, 6.200e+01, 2.550e+01, 4.100e+00],\n",
       "       [9.560e+01, 6.580e+01, 2.720e+01, 4.400e+00],\n",
       "       [1.007e+02, 6.960e+01, 2.870e+01, 4.700e+00],\n",
       "       [1.061e+02, 7.300e+01, 3.040e+01, 4.900e+00],\n",
       "       [1.112e+02, 7.670e+01, 3.190e+01, 5.300e+00],\n",
       "       [1.158e+02, 8.030e+01, 3.290e+01, 5.500e+00],\n",
       "       [1.209e+02, 8.360e+01, 3.460e+01, 6.000e+00],\n",
       "       [1.257e+02, 8.700e+01, 3.650e+01, 6.200e+00],\n",
       "       [1.307e+02, 9.000e+01, 3.810e+01, 6.400e+00],\n",
       "       [1.357e+02, 9.340e+01, 3.970e+01, 6.800e+00],\n",
       "       [1.409e+02, 9.690e+01, 4.120e+01, 7.000e+00],\n",
       "       [1.461e+02, 1.003e+02, 4.260e+01, 7.200e+00],\n",
       "       [1.508e+02, 1.035e+02, 4.420e+01, 7.400e+00],\n",
       "       [1.556e+02, 1.066e+02, 4.580e+01, 7.600e+00],\n",
       "       [1.610e+02, 1.100e+02, 4.730e+01, 8.000e+00],\n",
       "       [1.662e+02, 1.141e+02, 4.880e+01, 8.100e+00],\n",
       "       [1.717e+02, 1.183e+02, 5.020e+01, 8.300e+00],\n",
       "       [1.766e+02, 1.214e+02, 5.170e+01, 8.500e+00],\n",
       "       [1.816e+02, 1.246e+02, 5.290e+01, 8.700e+00],\n",
       "       [1.871e+02, 1.281e+02, 5.420e+01, 8.900e+00],\n",
       "       [1.920e+02, 1.317e+02, 5.560e+01, 9.000e+00],\n",
       "       [1.964e+02, 1.347e+02, 5.690e+01, 9.200e+00],\n",
       "       [2.015e+02, 1.381e+02, 5.840e+01, 9.400e+00],\n",
       "       [2.065e+02, 1.416e+02, 5.970e+01, 9.700e+00],\n",
       "       [2.110e+02, 1.439e+02, 6.100e+01, 1.000e+01],\n",
       "       [2.154e+02, 1.471e+02, 6.230e+01, 1.020e+01],\n",
       "       [2.204e+02, 1.506e+02, 6.390e+01, 1.080e+01],\n",
       "       [2.255e+02, 1.544e+02, 6.580e+01, 1.120e+01],\n",
       "       [2.303e+02, 1.574e+02, 6.720e+01, 1.150e+01],\n",
       "       [2.354e+02, 1.612e+02, 6.880e+01, 1.170e+01],\n",
       "       [2.400e+02, 1.644e+02, 7.020e+01, 1.190e+01],\n",
       "       [2.453e+02, 1.681e+02, 7.170e+01, 1.210e+01],\n",
       "       [2.503e+02, 1.714e+02, 7.310e+01, 1.230e+01],\n",
       "       [2.573e+02, 1.746e+02, 7.780e+01, 1.370e+01],\n",
       "       [2.637e+02, 1.778e+02, 8.230e+01, 1.520e+01],\n",
       "       [2.706e+02, 1.809e+02, 8.720e+01, 1.670e+01],\n",
       "       [2.761e+02, 1.832e+02, 9.120e+01, 1.800e+01],\n",
       "       [2.826e+02, 1.860e+02, 9.580e+01, 1.950e+01],\n",
       "       [2.883e+02, 1.888e+02, 1.003e+02, 2.080e+01],\n",
       "       [2.946e+02, 1.921e+02, 1.050e+02, 2.240e+01],\n",
       "       [2.995e+02, 1.945e+02, 1.083e+02, 2.340e+01],\n",
       "       [3.061e+02, 1.974e+02, 1.129e+02, 2.470e+01],\n",
       "       [3.113e+02, 2.001e+02, 1.168e+02, 2.610e+01],\n",
       "       [3.163e+02, 2.021e+02, 1.203e+02, 2.710e+01],\n",
       "       [3.222e+02, 2.051e+02, 1.245e+02, 2.860e+01],\n",
       "       [3.282e+02, 2.073e+02, 1.285e+02, 2.960e+01],\n",
       "       [3.343e+02, 2.102e+02, 1.332e+02, 3.100e+01],\n",
       "       [3.399e+02, 2.131e+02, 1.368e+02, 3.230e+01],\n",
       "       [3.466e+02, 2.162e+02, 1.412e+02, 3.370e+01],\n",
       "       [3.522e+02, 2.192e+02, 1.457e+02, 3.520e+01],\n",
       "       [3.580e+02, 2.219e+02, 1.498e+02, 3.620e+01],\n",
       "       [3.642e+02, 2.241e+02, 1.543e+02, 3.770e+01],\n",
       "       [3.698e+02, 2.266e+02, 1.582e+02, 3.880e+01],\n",
       "       [3.757e+02, 2.298e+02, 1.630e+02, 4.060e+01],\n",
       "       [3.818e+02, 2.326e+02, 1.670e+02, 4.190e+01],\n",
       "       [3.881e+02, 2.351e+02, 1.719e+02, 4.340e+01],\n",
       "       [3.942e+02, 2.379e+02, 1.766e+02, 4.460e+01],\n",
       "       [4.006e+02, 2.408e+02, 1.809e+02, 4.590e+01],\n",
       "       [4.072e+02, 2.438e+02, 1.853e+02, 4.730e+01],\n",
       "       [4.140e+02, 2.466e+02, 1.901e+02, 4.870e+01],\n",
       "       [4.207e+02, 2.496e+02, 1.951e+02, 5.040e+01],\n",
       "       [4.267e+02, 2.525e+02, 1.996e+02, 5.190e+01],\n",
       "       [4.324e+02, 2.551e+02, 2.031e+02, 5.290e+01],\n",
       "       [4.379e+02, 2.575e+02, 2.069e+02, 5.400e+01],\n",
       "       [4.434e+02, 2.599e+02, 2.106e+02, 5.500e+01],\n",
       "       [4.492e+02, 2.626e+02, 2.145e+02, 5.620e+01],\n",
       "       [4.552e+02, 2.653e+02, 2.196e+02, 5.780e+01],\n",
       "       [4.606e+02, 2.683e+02, 2.241e+02, 5.930e+01],\n",
       "       [4.666e+02, 2.717e+02, 2.286e+02, 6.090e+01],\n",
       "       [4.733e+02, 2.748e+02, 2.333e+02, 6.240e+01],\n",
       "       [4.796e+02, 2.771e+02, 2.377e+02, 6.370e+01],\n",
       "       [4.852e+02, 2.801e+02, 2.418e+02, 6.500e+01],\n",
       "       [4.907e+02, 2.826e+02, 2.458e+02, 6.630e+01],\n",
       "       [4.962e+02, 2.852e+02, 2.502e+02, 6.750e+01],\n",
       "       [5.023e+02, 2.882e+02, 2.548e+02, 6.890e+01],\n",
       "       [5.081e+02, 2.908e+02, 2.588e+02, 7.010e+01],\n",
       "       [5.131e+02, 2.931e+02, 2.621e+02, 7.110e+01],\n",
       "       [5.187e+02, 2.958e+02, 2.663e+02, 7.240e+01],\n",
       "       [5.244e+02, 2.988e+02, 2.705e+02, 7.360e+01],\n",
       "       [5.301e+02, 3.017e+02, 2.747e+02, 7.490e+01],\n",
       "       [5.363e+02, 3.046e+02, 2.790e+02, 7.620e+01],\n",
       "       [5.414e+02, 3.071e+02, 2.820e+02, 7.730e+01],\n",
       "       [5.471e+02, 3.099e+02, 2.861e+02, 7.860e+01],\n",
       "       [5.534e+02, 3.132e+02, 2.921e+02, 8.110e+01],\n",
       "       [5.592e+02, 3.159e+02, 2.972e+02, 8.300e+01],\n",
       "       [5.663e+02, 3.189e+02, 3.031e+02, 8.510e+01],\n",
       "       [5.726e+02, 3.218e+02, 3.087e+02, 8.690e+01],\n",
       "       [5.791e+02, 3.248e+02, 3.145e+02, 8.910e+01],\n",
       "       [5.867e+02, 3.278e+02, 3.211e+02, 9.120e+01],\n",
       "       [5.916e+02, 3.303e+02, 3.256e+02, 9.290e+01],\n",
       "       [5.989e+02, 3.332e+02, 3.319e+02, 9.470e+01],\n",
       "       [6.056e+02, 3.357e+02, 3.377e+02, 9.650e+01],\n",
       "       [6.128e+02, 3.393e+02, 3.438e+02, 9.900e+01],\n",
       "       [6.193e+02, 3.425e+02, 3.489e+02, 1.010e+02],\n",
       "       [6.257e+02, 3.452e+02, 3.542e+02, 1.029e+02],\n",
       "       [6.325e+02, 3.482e+02, 3.597e+02, 1.050e+02],\n",
       "       [6.382e+02, 3.507e+02, 3.647e+02, 1.070e+02],\n",
       "       [6.440e+02, 3.535e+02, 3.698e+02, 1.094e+02],\n",
       "       [6.504e+02, 3.567e+02, 3.751e+02, 1.117e+02],\n",
       "       [6.569e+02, 3.597e+02, 3.806e+02, 1.135e+02],\n",
       "       [6.646e+02, 3.635e+02, 3.873e+02, 1.157e+02],\n",
       "       [6.723e+02, 3.661e+02, 3.942e+02, 1.180e+02],\n",
       "       [6.783e+02, 3.683e+02, 3.992e+02, 1.195e+02],\n",
       "       [6.852e+02, 3.715e+02, 4.049e+02, 1.218e+02],\n",
       "       [6.908e+02, 3.743e+02, 4.098e+02, 1.238e+02],\n",
       "       [6.985e+02, 3.771e+02, 4.165e+02, 1.258e+02],\n",
       "       [7.048e+02, 3.798e+02, 4.214e+02, 1.276e+02],\n",
       "       [7.115e+02, 3.831e+02, 4.271e+02, 1.297e+02],\n",
       "       [7.187e+02, 3.863e+02, 4.331e+02, 1.315e+02],\n",
       "       [7.249e+02, 3.891e+02, 4.379e+02, 1.333e+02],\n",
       "       [7.310e+02, 3.921e+02, 4.428e+02, 1.351e+02],\n",
       "       [7.374e+02, 3.949e+02, 4.484e+02, 1.372e+02],\n",
       "       [7.446e+02, 3.979e+02, 4.542e+02, 1.388e+02],\n",
       "       [7.520e+02, 4.007e+02, 4.603e+02, 1.407e+02],\n",
       "       [7.599e+02, 4.045e+02, 4.667e+02, 1.427e+02],\n",
       "       [7.663e+02, 4.073e+02, 4.723e+02, 1.449e+02],\n",
       "       [7.726e+02, 4.101e+02, 4.774e+02, 1.464e+02],\n",
       "       [7.787e+02, 4.127e+02, 4.830e+02, 1.478e+02],\n",
       "       [7.864e+02, 4.157e+02, 4.891e+02, 1.501e+02],\n",
       "       [7.927e+02, 4.191e+02, 4.947e+02, 1.525e+02],\n",
       "       [7.991e+02, 4.222e+02, 5.002e+02, 1.543e+02],\n",
       "       [8.051e+02, 4.252e+02, 5.050e+02, 1.561e+02],\n",
       "       [8.120e+02, 4.283e+02, 5.104e+02, 1.582e+02],\n",
       "       [8.187e+02, 4.314e+02, 5.160e+02, 1.606e+02],\n",
       "       [8.256e+02, 4.345e+02, 5.211e+02, 1.629e+02],\n",
       "       [8.314e+02, 4.372e+02, 5.262e+02, 1.648e+02],\n",
       "       [8.382e+02, 4.404e+02, 5.321e+02, 1.671e+02],\n",
       "       [8.449e+02, 4.437e+02, 5.378e+02, 1.696e+02],\n",
       "       [8.516e+02, 4.467e+02, 5.430e+02, 1.719e+02],\n",
       "       [8.579e+02, 4.492e+02, 5.480e+02, 1.738e+02],\n",
       "       [8.644e+02, 4.522e+02, 5.532e+02, 1.758e+02],\n",
       "       [8.706e+02, 4.556e+02, 5.586e+02, 1.781e+02],\n",
       "       [8.765e+02, 4.586e+02, 5.637e+02, 1.799e+02]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5.1   ,  17.85  ,  24.99  ,   4.998 ],\n",
       "       [  4.9   ,  14.7   ,  20.58  ,   4.116 ],\n",
       "       [  4.7   ,  15.04  ,  19.552 ,   3.9104],\n",
       "       [  4.6   ,  14.26  ,  21.39  ,   4.278 ],\n",
       "       [  5.    ,  18.    ,  25.2   ,   5.04  ],\n",
       "       [  5.4   ,  21.06  ,  35.802 ,  14.3208],\n",
       "       [  4.6   ,  15.64  ,  21.896 ,   6.5688],\n",
       "       [  5.    ,  17.    ,  25.5   ,   5.1   ],\n",
       "       [  4.4   ,  12.76  ,  17.864 ,   3.5728],\n",
       "       [  4.9   ,  15.19  ,  22.785 ,   2.2785],\n",
       "       [  5.4   ,  19.98  ,  29.97  ,   5.994 ],\n",
       "       [  4.8   ,  16.32  ,  26.112 ,   5.2224],\n",
       "       [  4.8   ,  14.4   ,  20.16  ,   2.016 ],\n",
       "       [  4.3   ,  12.9   ,  14.19  ,   1.419 ],\n",
       "       [  5.8   ,  23.2   ,  27.84  ,   5.568 ],\n",
       "       [  5.7   ,  25.08  ,  37.62  ,  15.048 ],\n",
       "       [  5.4   ,  21.06  ,  27.378 ,  10.9512],\n",
       "       [  5.1   ,  17.85  ,  24.99  ,   7.497 ],\n",
       "       [  5.7   ,  21.66  ,  36.822 ,  11.0466],\n",
       "       [  5.1   ,  19.38  ,  29.07  ,   8.721 ],\n",
       "       [  5.4   ,  18.36  ,  31.212 ,   6.2424],\n",
       "       [  5.1   ,  18.87  ,  28.305 ,  11.322 ],\n",
       "       [  4.6   ,  16.56  ,  16.56  ,   3.312 ],\n",
       "       [  5.1   ,  16.83  ,  28.611 ,  14.3055],\n",
       "       [  4.8   ,  16.32  ,  31.008 ,   6.2016],\n",
       "       [  5.    ,  15.    ,  24.    ,   4.8   ],\n",
       "       [  5.    ,  17.    ,  27.2   ,  10.88  ],\n",
       "       [  5.2   ,  18.2   ,  27.3   ,   5.46  ],\n",
       "       [  5.2   ,  17.68  ,  24.752 ,   4.9504],\n",
       "       [  4.7   ,  15.04  ,  24.064 ,   4.8128],\n",
       "       [  4.8   ,  14.88  ,  23.808 ,   4.7616],\n",
       "       [  5.4   ,  18.36  ,  27.54  ,  11.016 ],\n",
       "       [  5.2   ,  21.32  ,  31.98  ,   3.198 ],\n",
       "       [  5.5   ,  23.1   ,  32.34  ,   6.468 ],\n",
       "       [  4.9   ,  15.19  ,  22.785 ,   4.557 ],\n",
       "       [  5.    ,  16.    ,  19.2   ,   3.84  ],\n",
       "       [  5.5   ,  19.25  ,  25.025 ,   5.005 ],\n",
       "       [  4.9   ,  17.64  ,  24.696 ,   2.4696],\n",
       "       [  4.4   ,  13.2   ,  17.16  ,   3.432 ],\n",
       "       [  5.1   ,  17.34  ,  26.01  ,   5.202 ],\n",
       "       [  5.    ,  17.5   ,  22.75  ,   6.825 ],\n",
       "       [  4.5   ,  10.35  ,  13.455 ,   4.0365],\n",
       "       [  4.4   ,  14.08  ,  18.304 ,   3.6608],\n",
       "       [  5.    ,  17.5   ,  28.    ,  16.8   ],\n",
       "       [  5.1   ,  19.38  ,  36.822 ,  14.7288],\n",
       "       [  4.8   ,  14.4   ,  20.16  ,   6.048 ],\n",
       "       [  5.1   ,  19.38  ,  31.008 ,   6.2016],\n",
       "       [  4.6   ,  14.72  ,  20.608 ,   4.1216],\n",
       "       [  5.3   ,  19.61  ,  29.415 ,   5.883 ],\n",
       "       [  5.    ,  16.5   ,  23.1   ,   4.62  ],\n",
       "       [  7.    ,  22.4   , 105.28  , 147.392 ],\n",
       "       [  6.4   ,  20.48  ,  92.16  , 138.24  ],\n",
       "       [  6.9   ,  21.39  , 104.811 , 157.2165],\n",
       "       [  5.5   ,  12.65  ,  50.6   ,  65.78  ],\n",
       "       [  6.5   ,  18.2   ,  83.72  , 125.58  ],\n",
       "       [  5.7   ,  15.96  ,  71.82  ,  93.366 ],\n",
       "       [  6.3   ,  20.79  ,  97.713 , 156.3408],\n",
       "       [  4.9   ,  11.76  ,  38.808 ,  38.808 ],\n",
       "       [  6.6   ,  19.14  ,  88.044 , 114.4572],\n",
       "       [  5.2   ,  14.04  ,  54.756 ,  76.6584],\n",
       "       [  5.    ,  10.    ,  35.    ,  35.    ],\n",
       "       [  5.9   ,  17.7   ,  74.34  , 111.51  ],\n",
       "       [  6.    ,  13.2   ,  52.8   ,  52.8   ],\n",
       "       [  6.1   ,  17.69  ,  83.143 , 116.4002],\n",
       "       [  5.6   ,  16.24  ,  58.464 ,  76.0032],\n",
       "       [  6.7   ,  20.77  ,  91.388 , 127.9432],\n",
       "       [  5.6   ,  16.8   ,  75.6   , 113.4   ],\n",
       "       [  5.8   ,  15.66  ,  64.206 ,  64.206 ],\n",
       "       [  6.2   ,  13.64  ,  61.38  ,  92.07  ],\n",
       "       [  5.6   ,  14.    ,  54.6   ,  60.06  ],\n",
       "       [  5.9   ,  18.88  ,  90.624 , 163.1232],\n",
       "       [  6.1   ,  17.08  ,  68.32  ,  88.816 ],\n",
       "       [  6.3   ,  15.75  ,  77.175 , 115.7625],\n",
       "       [  6.1   ,  17.08  ,  80.276 ,  96.3312],\n",
       "       [  6.4   ,  18.56  ,  79.808 , 103.7504],\n",
       "       [  6.6   ,  19.8   ,  87.12  , 121.968 ],\n",
       "       [  6.8   ,  19.04  ,  91.392 , 127.9488],\n",
       "       [  6.7   ,  20.1   , 100.5   , 170.85  ],\n",
       "       [  6.    ,  17.4   ,  78.3   , 117.45  ],\n",
       "       [  5.7   ,  14.82  ,  51.87  ,  51.87  ],\n",
       "       [  5.5   ,  13.2   ,  50.16  ,  55.176 ],\n",
       "       [  5.5   ,  13.2   ,  48.84  ,  48.84  ],\n",
       "       [  5.8   ,  15.66  ,  61.074 ,  73.2888],\n",
       "       [  6.    ,  16.2   ,  82.62  , 132.192 ],\n",
       "       [  5.4   ,  16.2   ,  72.9   , 109.35  ],\n",
       "       [  6.    ,  20.4   ,  91.8   , 146.88  ],\n",
       "       [  6.7   ,  20.77  ,  97.619 , 146.4285],\n",
       "       [  6.3   ,  14.49  ,  63.756 ,  82.8828],\n",
       "       [  5.6   ,  16.8   ,  68.88  ,  89.544 ],\n",
       "       [  5.5   ,  13.75  ,  55.    ,  71.5   ],\n",
       "       [  5.5   ,  14.3   ,  62.92  ,  75.504 ],\n",
       "       [  6.1   ,  18.3   ,  84.18  , 117.852 ],\n",
       "       [  5.8   ,  15.08  ,  60.32  ,  72.384 ],\n",
       "       [  5.    ,  11.5   ,  37.95  ,  37.95  ],\n",
       "       [  5.6   ,  15.12  ,  63.504 ,  82.5552],\n",
       "       [  5.7   ,  17.1   ,  71.82  ,  86.184 ],\n",
       "       [  5.7   ,  16.53  ,  69.426 ,  90.2538],\n",
       "       [  6.2   ,  17.98  ,  77.314 , 100.5082],\n",
       "       [  5.1   ,  12.75  ,  38.25  ,  42.075 ],\n",
       "       [  5.7   ,  15.96  ,  65.436 ,  85.0668],\n",
       "       [  6.3   ,  20.79  , 124.74  , 311.85  ],\n",
       "       [  5.8   ,  15.66  ,  79.866 , 151.7454],\n",
       "       [  7.1   ,  21.3   , 125.67  , 263.907 ],\n",
       "       [  6.3   ,  18.27  , 102.312 , 184.1616],\n",
       "       [  6.5   ,  19.5   , 113.1   , 248.82  ],\n",
       "       [  7.6   ,  22.8   , 150.48  , 316.008 ],\n",
       "       [  4.9   ,  12.25  ,  55.125 ,  93.7125],\n",
       "       [  7.3   ,  21.17  , 133.371 , 240.0678],\n",
       "       [  6.7   ,  16.75  ,  97.15  , 174.87  ],\n",
       "       [  7.2   ,  25.92  , 158.112 , 395.28  ],\n",
       "       [  6.5   ,  20.8   , 106.08  , 212.16  ],\n",
       "       [  6.4   ,  17.28  ,  91.584 , 174.0096],\n",
       "       [  6.8   ,  20.4   , 112.2   , 235.62  ],\n",
       "       [  5.7   ,  14.25  ,  71.25  , 142.5   ],\n",
       "       [  5.8   ,  16.24  ,  82.824 , 198.7776],\n",
       "       [  6.4   ,  20.48  , 108.544 , 249.6512],\n",
       "       [  6.5   ,  19.5   , 107.25  , 193.05  ],\n",
       "       [  7.7   ,  29.26  , 196.042 , 431.2924],\n",
       "       [  7.7   ,  20.02  , 138.138 , 317.7174],\n",
       "       [  6.    ,  13.2   ,  66.    ,  99.    ],\n",
       "       [  6.9   ,  22.08  , 125.856 , 289.4688],\n",
       "       [  5.6   ,  15.68  ,  76.832 , 153.664 ],\n",
       "       [  7.7   ,  21.56  , 144.452 , 288.904 ],\n",
       "       [  6.3   ,  17.01  ,  83.349 , 150.0282],\n",
       "       [  6.7   ,  22.11  , 126.027 , 264.6567],\n",
       "       [  7.2   ,  23.04  , 138.24  , 248.832 ],\n",
       "       [  6.2   ,  17.36  ,  83.328 , 149.9904],\n",
       "       [  6.1   ,  18.3   ,  89.67  , 161.406 ],\n",
       "       [  6.4   ,  17.92  , 100.352 , 210.7392],\n",
       "       [  7.2   ,  21.6   , 125.28  , 200.448 ],\n",
       "       [  7.4   ,  20.72  , 126.392 , 240.1448],\n",
       "       [  7.9   ,  30.02  , 192.128 , 384.256 ],\n",
       "       [  6.4   ,  17.92  , 100.352 , 220.7744],\n",
       "       [  6.3   ,  17.64  ,  89.964 , 134.946 ],\n",
       "       [  6.1   ,  15.86  ,  88.816 , 124.3424],\n",
       "       [  7.7   ,  23.1   , 140.91  , 324.093 ],\n",
       "       [  6.3   ,  21.42  , 119.952 , 287.8848],\n",
       "       [  6.4   ,  19.84  , 109.12  , 196.416 ],\n",
       "       [  6.    ,  18.    ,  86.4   , 155.52  ],\n",
       "       [  6.9   ,  21.39  , 115.506 , 242.5626],\n",
       "       [  6.7   ,  20.77  , 116.312 , 279.1488],\n",
       "       [  6.9   ,  21.39  , 109.089 , 250.9047],\n",
       "       [  5.8   ,  15.66  ,  79.866 , 151.7454],\n",
       "       [  6.8   ,  21.76  , 128.384 , 295.2832],\n",
       "       [  6.7   ,  22.11  , 126.027 , 315.0675],\n",
       "       [  6.7   ,  20.1   , 104.52  , 240.396 ],\n",
       "       [  6.3   ,  15.75  ,  78.75  , 149.625 ],\n",
       "       [  6.5   ,  19.5   , 101.4   , 202.8   ],\n",
       "       [  6.2   ,  21.08  , 113.832 , 261.8136],\n",
       "       [  5.9   ,  17.7   ,  90.27  , 162.486 ]])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumprod(data,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4644999999999997"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.84333333, 3.05733333, 3.758     , 1.19933333])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.55 , 2.375, 2.35 , 2.35 , 2.55 , 2.85 , 2.425, 2.525, 2.225,\n",
       "       2.4  , 2.7  , 2.5  , 2.325, 2.125, 2.8  , 3.   , 2.75 , 2.575,\n",
       "       2.875, 2.675, 2.675, 2.675, 2.35 , 2.65 , 2.575, 2.45 , 2.6  ,\n",
       "       2.6  , 2.55 , 2.425, 2.425, 2.675, 2.725, 2.825, 2.425, 2.4  ,\n",
       "       2.625, 2.5  , 2.225, 2.55 , 2.525, 2.1  , 2.275, 2.675, 2.8  ,\n",
       "       2.375, 2.675, 2.35 , 2.675, 2.475, 4.075, 3.9  , 4.1  , 3.275,\n",
       "       3.85 , 3.575, 3.975, 2.9  , 3.85 , 3.3  , 2.875, 3.65 , 3.3  ,\n",
       "       3.775, 3.35 , 3.9  , 3.65 , 3.4  , 3.6  , 3.275, 3.925, 3.55 ,\n",
       "       3.8  , 3.7  , 3.725, 3.85 , 3.95 , 4.1  , 3.725, 3.2  , 3.2  ,\n",
       "       3.15 , 3.4  , 3.85 , 3.6  , 3.875, 4.   , 3.575, 3.5  , 3.325,\n",
       "       3.425, 3.775, 3.4  , 2.9  , 3.45 , 3.525, 3.525, 3.675, 2.925,\n",
       "       3.475, 4.525, 3.875, 4.525, 4.15 , 4.375, 4.825, 3.4  , 4.575,\n",
       "       4.2  , 4.85 , 4.2  , 4.075, 4.35 , 3.8  , 4.025, 4.3  , 4.2  ,\n",
       "       5.1  , 4.875, 3.675, 4.525, 3.825, 4.8  , 3.925, 4.45 , 4.55 ,\n",
       "       3.9  , 3.95 , 4.225, 4.4  , 4.55 , 5.025, 4.25 , 3.925, 3.925,\n",
       "       4.775, 4.425, 4.2  , 3.9  , 4.375, 4.45 , 4.35 , 3.875, 4.55 ,\n",
       "       4.55 , 4.3  , 3.925, 4.175, 4.325, 3.95 ])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.896056416666667"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.9\n",
      "0.1\n"
     ]
    }
   ],
   "source": [
    "print(np.max(data))\n",
    "print(np.min(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.9, 4.4, 6.9, 2.5])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(data,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.1, 4.9, 4.7, 4.6, 5. , 5.4, 4.6, 5. , 4.4, 4.9, 5.4, 4.8, 4.8,\n",
       "       4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5. ,\n",
       "       5. , 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5. , 5.5, 4.9, 4.4,\n",
       "       5.1, 5. , 4.5, 4.4, 5. , 5.1, 4.8, 5.1, 4.6, 5.3, 5. , 7. , 6.4,\n",
       "       6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5. , 5.9, 6. , 6.1, 5.6,\n",
       "       6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7,\n",
       "       6. , 5.7, 5.5, 5.5, 5.8, 6. , 5.4, 6. , 6.7, 6.3, 5.6, 5.5, 5.5,\n",
       "       6.1, 5.8, 5. , 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3,\n",
       "       6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5,\n",
       "       7.7, 7.7, 6. , 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2,\n",
       "       7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6. , 6.9, 6.7, 6.9, 5.8,\n",
       "       6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(data,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 条件运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [2, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 2, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2],\n",
       "       [6, 2, 6, 2]])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(data>5,6,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.1, 3.5, 1.4, 1. ],\n",
       "       [4.9, 3. , 1.4, 1. ],\n",
       "       [4.7, 3.2, 1.3, 1. ],\n",
       "       [4.6, 3.1, 1.5, 1. ],\n",
       "       [5. , 3.6, 1.4, 1. ],\n",
       "       [5.4, 3.9, 1.7, 1. ],\n",
       "       [4.6, 3.4, 1.4, 1. ],\n",
       "       [5. , 3.4, 1.5, 1. ],\n",
       "       [4.4, 2.9, 1.4, 1. ],\n",
       "       [4.9, 3.1, 1.5, 1. ],\n",
       "       [5.4, 3.7, 1.5, 1. ],\n",
       "       [4.8, 3.4, 1.6, 1. ],\n",
       "       [4.8, 3. , 1.4, 1. ],\n",
       "       [4.3, 3. , 1.1, 1. ],\n",
       "       [5.8, 4. , 1.2, 1. ],\n",
       "       [5.7, 4.4, 1.5, 1. ],\n",
       "       [5.4, 3.9, 1.3, 1. ],\n",
       "       [5.1, 3.5, 1.4, 1. ],\n",
       "       [5.7, 3.8, 1.7, 1. ],\n",
       "       [5.1, 3.8, 1.5, 1. ],\n",
       "       [5.4, 3.4, 1.7, 1. ],\n",
       "       [5.1, 3.7, 1.5, 1. ],\n",
       "       [4.6, 3.6, 1. , 1. ],\n",
       "       [5.1, 3.3, 1.7, 1. ],\n",
       "       [4.8, 3.4, 1.9, 1. ],\n",
       "       [5. , 3. , 1.6, 1. ],\n",
       "       [5. , 3.4, 1.6, 1. ],\n",
       "       [5.2, 3.5, 1.5, 1. ],\n",
       "       [5.2, 3.4, 1.4, 1. ],\n",
       "       [4.7, 3.2, 1.6, 1. ],\n",
       "       [4.8, 3.1, 1.6, 1. ],\n",
       "       [5.4, 3.4, 1.5, 1. ],\n",
       "       [5.2, 4.1, 1.5, 1. ],\n",
       "       [5.5, 4.2, 1.4, 1. ],\n",
       "       [4.9, 3.1, 1.5, 1. ],\n",
       "       [5. , 3.2, 1.2, 1. ],\n",
       "       [5.5, 3.5, 1.3, 1. ],\n",
       "       [4.9, 3.6, 1.4, 1. ],\n",
       "       [4.4, 3. , 1.3, 1. ],\n",
       "       [5.1, 3.4, 1.5, 1. ],\n",
       "       [5. , 3.5, 1.3, 1. ],\n",
       "       [4.5, 2.3, 1.3, 1. ],\n",
       "       [4.4, 3.2, 1.3, 1. ],\n",
       "       [5. , 3.5, 1.6, 1. ],\n",
       "       [5.1, 3.8, 1.9, 1. ],\n",
       "       [4.8, 3. , 1.4, 1. ],\n",
       "       [5.1, 3.8, 1.6, 1. ],\n",
       "       [4.6, 3.2, 1.4, 1. ],\n",
       "       [5.3, 3.7, 1.5, 1. ],\n",
       "       [5. , 3.3, 1.4, 1. ],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(data<1,1,data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumPy使用小例子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "求解MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100,)\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "ys=np.random.randn(100,2)\n",
    "ypred=ys[:,0]\n",
    "print(ypred.shape)\n",
    "y=ys[:,1]\n",
    "n=ypred.size\n",
    "print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6519126880579673"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=1/n*np.sum((ypred-y)**2,axis=0)\n",
    "#mse=1/n*np.sum((ypred-y)**2)\n",
    "mse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算机显示彩色图像是R、G、B三色，每一种颜色用8位表示，因此取值范围[0,255]。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157ae233f08>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color=np.array([[[255,0,0]]])\n",
    "print(color.shape)\n",
    "plt.imshow(color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[255   0   0]\n",
      "  [  0 255   0]\n",
      "  [  0   0 255]\n",
      "  [200 150 100]]\n",
      "\n",
      " [[  0   0   0]\n",
      "  [128 128 128]\n",
      "  [255 255 255]\n",
      "  [ 50 150 100]]]\n",
      "(2, 4, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157ae1b3ac8>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img=np.array([[[255,0,0],[0,255,0],[0,0,255],[200,150,100]],[[0,0,0],[128,128,128],[255,255,255],[50,150,100]]])\n",
    "print(img)\n",
    "print(img.shape)\n",
    "\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "灰度图像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 84.15  84.15  84.15 148.5 ]\n",
      " [  0.   126.72 252.45  99.  ]]\n",
      "(2, 4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157ae2f9708>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weight=np.array([0.33,0.33,0.33])\n",
    "img_gray=img@weight\n",
    "print(img_gray)\n",
    "print(img_gray.shape)\n",
    "plt.imshow(img_gray,cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "好玩的彩色格子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[205 222 127]\n",
      "  [ 51  82 249]\n",
      "  [110 143 116]\n",
      "  ...\n",
      "  [217 140 103]\n",
      "  [189 209 216]\n",
      "  [224 110 103]]\n",
      "\n",
      " [[ 59 170 203]\n",
      "  [235  99 239]\n",
      "  [ 67 132 119]\n",
      "  ...\n",
      "  [191  87 108]\n",
      "  [162 239  69]\n",
      "  [210 157 198]]\n",
      "\n",
      " [[ 73 232 223]\n",
      "  [225  62 155]\n",
      "  [ 59  78 221]\n",
      "  ...\n",
      "  [169  71 151]\n",
      "  [207  48 153]\n",
      "  [ 90  36  67]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[ 66 151  94]\n",
      "  [218 215 167]\n",
      "  [122 151 183]\n",
      "  ...\n",
      "  [138  22 188]\n",
      "  [245  68 179]\n",
      "  [230  60 223]]\n",
      "\n",
      " [[  4 230  16]\n",
      "  [ 86  33  93]\n",
      "  [218 224 143]\n",
      "  ...\n",
      "  [105  44  12]\n",
      "  [149 214  48]\n",
      "  [ 47  16 147]]\n",
      "\n",
      " [[159 231 177]\n",
      "  [104 125 189]\n",
      "  [142 169  10]\n",
      "  ...\n",
      "  [153 129 221]\n",
      "  [166  98   4]\n",
      "  [211  59 208]]]\n"
     ]
    }
   ],
   "source": [
    "img1=np.random.randint(0,256,(256,256,3))\n",
    "print(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157ae363988>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157ae3da8c8>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img2=np.random.randint(0,256,(16,16,3))\n",
    "plt.imshow(img2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157af448bc8>"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=np.arange(16*16*3).reshape(16,16,3)\n",
    "img3=np.sin(data*np.pi/16)\n",
    "plt.imshow(img3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.00000000e+00  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  1.22464680e-16 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -2.44929360e-16]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 3.67394040e-16 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -4.89858720e-16  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01  6.12323400e-16]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-7.34788079e-16  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  8.57252759e-16 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -9.79717439e-16]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 1.10218212e-15 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -1.22464680e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01  4.89982516e-15]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-1.46957616e-15  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01 -1.96067284e-15 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -1.71450552e-15]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 5.38968388e-15 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -1.95943488e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01 -1.47081412e-15]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-2.20436424e-15  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  5.87954260e-15 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -2.44929360e-15]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[-9.80955401e-16 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -9.79965032e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01  6.36940132e-15]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-2.93915232e-15  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01 -4.91096681e-16 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01  3.92134568e-15]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 6.85926004e-15 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -3.42901104e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01 -1.23796127e-18]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-1.07793678e-14  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  7.34911876e-15 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -3.91886976e-15]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 4.88620758e-16 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01  2.94162824e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01  7.83897748e-15]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[-4.40872848e-15  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  9.78479478e-16 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -1.17590852e-14]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 8.32883620e-15 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -4.89858720e-15  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01  1.56791929e-14]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]\n",
      "\n",
      " [[ 1.96191080e-15  1.95090322e-01  3.82683432e-01]\n",
      "  [ 5.55570233e-01  7.07106781e-01  8.31469612e-01]\n",
      "  [ 9.23879533e-01  9.80785280e-01  1.00000000e+00]\n",
      "  [ 9.80785280e-01  9.23879533e-01  8.31469612e-01]\n",
      "  [ 7.07106781e-01  5.55570233e-01  3.82683432e-01]\n",
      "  [ 1.95090322e-01  8.81869492e-15 -1.95090322e-01]\n",
      "  [-3.82683432e-01 -5.55570233e-01 -7.07106781e-01]\n",
      "  [-8.31469612e-01 -9.23879533e-01 -9.80785280e-01]\n",
      "  [-1.00000000e+00 -9.80785280e-01 -9.23879533e-01]\n",
      "  [-8.31469612e-01 -7.07106781e-01 -5.55570233e-01]\n",
      "  [-3.82683432e-01 -1.95090322e-01 -1.95993006e-14]\n",
      "  [ 1.95090322e-01  3.82683432e-01  5.55570233e-01]\n",
      "  [ 7.07106781e-01  8.31469612e-01  9.23879533e-01]\n",
      "  [ 9.80785280e-01  1.00000000e+00  9.80785280e-01]\n",
      "  [ 9.23879533e-01  8.31469612e-01  7.07106781e-01]\n",
      "  [ 5.55570233e-01  3.82683432e-01  1.95090322e-01]]\n",
      "\n",
      " [[ 1.95819692e-15 -1.95090322e-01 -3.82683432e-01]\n",
      "  [-5.55570233e-01 -7.07106781e-01 -8.31469612e-01]\n",
      "  [-9.23879533e-01 -9.80785280e-01 -1.00000000e+00]\n",
      "  [-9.80785280e-01 -9.23879533e-01 -8.31469612e-01]\n",
      "  [-7.07106781e-01 -5.55570233e-01 -3.82683432e-01]\n",
      "  [-1.95090322e-01 -1.27388026e-14  1.95090322e-01]\n",
      "  [ 3.82683432e-01  5.55570233e-01  7.07106781e-01]\n",
      "  [ 8.31469612e-01  9.23879533e-01  9.80785280e-01]\n",
      "  [ 1.00000000e+00  9.80785280e-01  9.23879533e-01]\n",
      "  [ 8.31469612e-01  7.07106781e-01  5.55570233e-01]\n",
      "  [ 3.82683432e-01  1.95090322e-01 -4.90230108e-15]\n",
      "  [-1.95090322e-01 -3.82683432e-01 -5.55570233e-01]\n",
      "  [-7.07106781e-01 -8.31469612e-01 -9.23879533e-01]\n",
      "  [-9.80785280e-01 -1.00000000e+00 -9.80785280e-01]\n",
      "  [-9.23879533e-01 -8.31469612e-01 -7.07106781e-01]\n",
      "  [-5.55570233e-01 -3.82683432e-01 -1.95090322e-01]]]\n"
     ]
    }
   ],
   "source": [
    "print(img3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157af43ba08>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.abs(img3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x157af56e648>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img4=img3*255\n",
    "plt.imshow(img4.astype('uint8'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}