HelloWorld.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "hahaha2\n"
     ]
    }
   ],
   "source": [
    "List=[1,2,3]                    #列表\n",
    "Tup=(1,2,3)                     #元组\n",
    "Dict={'frist':1,'second':2}     #字典\n",
    "a={1,2,3}                       #集合\n",
    "\n",
    "print(f'hahaha{List[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0\n1\n1\n2\n3\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    " \n",
    "def fibonacci(n):           # 生成器函数 - 斐波那契\n",
    "    a, b, counter = 0, 1, 0\n",
    "    while True:\n",
    "        if (counter > n): \n",
    "            return\n",
    "        yield a\n",
    "        a, b = b, a + b\n",
    "        counter += 1\n",
    "f = fibonacci(100)           # f 是一个迭代器，由生成器返回生成\n",
    "\n",
    "for i in range (0,5):\n",
    "    print(next(f))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "3,4\n6,11\n5.0\n"
    }
   ],
   "source": [
    "import cmath\n",
    "class vector:   #运算符重载,向量的实现\n",
    "    def __init__(self,a,b):\n",
    "        self.a=a\n",
    "        self.b=b\n",
    "    def __str__(self):\n",
    "        return f'{self.a},{self.b}'\n",
    "    def __add__(self,other):\n",
    "        return vector(self.a + other.a, self.b + other.b)\n",
    "    def Length(self):\n",
    "        return cmath.sqrt(self.a**2+self.b**2).real\n",
    "\n",
    "v1=vector(3,4)\n",
    "v2=vector(3,7)\n",
    "print(v1)\n",
    "print(v1+v2)\n",
    "print(v1.Length())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "2 3\n"
    }
   ],
   "source": [
    "x=3\n",
    "y=2\n",
    "(x, y) = (y, x) if x > y else (x, y)\n",
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\\d\n格式化字符 你好 格式化数字 2020\nf字符串: 无视转义符\nformat应用:第一 第二 第一\n数字format: +3.14(保留小数) \n           xxx2020(向左补全x,7位为止)\n           2020(补空格)\n"
     ]
    }
   ],
   "source": [
    "#字符串格式化\n",
    "a=r'无视转义符\\d'\n",
    "print(a[5:8])#左闭右开原则\n",
    "print('格式化字符 %s 格式化数字 %d'%('你好',2020))\n",
    "print(f'f字符串: {a[0:5]}')\n",
    "print('format应用:{0} {1} {0}'.format(\"第一\",'第二'))\n",
    "print('数字format: {0:+.2f}(保留小数) \\n           {1:x>7d}(向左补全x,7位为止)'.format(3.14159,2020))\n",
    "print('{0:>15d}(补空格)'.format(2020))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "x = np.arange(0,  3  * np.pi,  0.1) \n",
    "y = np.sin(x)\n",
    "plt.title(\"sine wave form\")  \n",
    "\n",
    "plt.plot(x, y) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "        import re\n",
    "        s=r'\"0P\"'\n",
    "        pattern = re.compile(r'[A-Za-z0-9]')\n",
    "        ch=\"\"\n",
    "        print(ch.join(pattern.findall(s)).upper()[::-1]==ch.join(pattern.findall(s)).upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[], [3], [2], [2, 3], [1], [1, 3], [1, 2], [1, 2, 3]]\n"
     ]
    }
   ],
   "source": [
    "nums=[1,2,3]\n",
    "ans=[]\n",
    "for i in range(pow(2,len(nums))):\n",
    "    k=(r'{0:0>'+str(len(nums))+r'd}').format(int(bin(i)[2:]))\n",
    "    a1=[]\n",
    "    for flag in range(len(nums)):\n",
    "        if(k[flag]=='1'):\n",
    "            a1.append(nums[flag])\n",
    "    ans.append(a1)\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'a'), ('b', 'c'), ('b', 'd'), ('c', 'a'), ('c', 'b'), ('c', 'd'), ('d', 'a'), ('d', 'b'), ('d', 'c')]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "print(list(itertools.permutations('abcd',2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}