删除 '20200404229王文卓/Jupyter_test.ipynb'

20200404229王文卓/Jupyter_test.ipynb

@@ -1,813 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "你好 Python\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('你好 Python')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "test\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('test')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n",
-      "7\n",
-      "8\n",
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "for i in range(10):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = 3.14"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 一级标题\n",
-    "## 二级标题\n",
-    "### 三级标题"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 魔法命令"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %run"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "大家好，很高兴认识你\n"
-     ]
-    }
-   ],
-   "source": [
-    "%run ./My_Test.py"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %load"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %load ./My_Test.py\n",
-    "print('大家好，很高兴认识你')\n",
-    "\n",
-    "def my_print(x):\n",
-    "\tprint(x*2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python好，大家好\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_print('Python好，大家好')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "大家好，很高兴认识你\n",
-      "xixi\n"
-     ]
-    }
-   ],
-   "source": [
-    "from My_Test import my_print\n",
-    "my_print('xixi')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "大家好，很高兴认识你\n"
-     ]
-    }
-   ],
-   "source": [
-    "# notebook对同一个文件只会导入一次\n",
-    "%run ./My_Test.py"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python好，大家好Python好，大家好\n"
-     ]
-    }
-   ],
-   "source": [
-    "my_print('Python好，大家好')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# %timeit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "415 µs ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit li = [i**2 for i in range(1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "469 ms ± 21.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit li = [i**2 for i in range(1000000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5.44 µs ± 421 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%timeit li = [i**2 for i in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %timeit 后面只跟一句代码\n",
-    "# 测试代码块 用%%timit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "742 µs ± 71.2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit\n",
-    "li = []\n",
-    "for i in range(1000):\n",
-    "    li.append(i**2)\n",
-    "# 在python中使用列表生成式比for高效    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# %time 只会测量一次代码的执行时间"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 662 µs, sys: 2 µs, total: 664 µs\n",
-      "Wall time: 678 µs\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time li = [i**2 for i in range(1000)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 953 µs, sys: 60 µs, total: 1.01 ms\n",
-      "Wall time: 1.23 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "li = []\n",
-    "for i in range(1000):\n",
-    "    li.append(i**2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.18 ms ± 403 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "import random\n",
-    "li = [random.random() for i in range(100000)]\n",
-    "%timeit li.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 27.1 ms, sys: 2.68 ms, total: 29.8 ms\n",
-      "Wall time: 31 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "li = [random.random() for i in range(100000)]\n",
-    "%time li.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.61 ms, sys: 133 µs, total: 3.74 ms\n",
-      "Wall time: 4.09 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time li.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.3 ms, sys: 14 µs, total: 3.31 ms\n",
-      "Wall time: 3.4 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time li.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 4.06 ms, sys: 137 µs, total: 4.19 ms\n",
-      "Wall time: 4.28 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%time li.sort()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %%html"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div class='mytest' style='color:red'>html content</div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%html\n",
-    "<div class='mytest' style='color:red'>html content</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %%js"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "document.querySelector('.mytest').innerHTML='我成功了'"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%%js\n",
-    "document.querySelector('.mytest').innerHTML='我成功了'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## %%writefile "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Writing haha.py\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%writefile haha.py\n",
-    "import random\n",
-    "li = [random.random() for i in range(100000)]\n",
-    "%timeit li.sort()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = np.array([1, 2, 3])\n",
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3,)"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = np.array([[1,2,3],\n",
-    "              [4,5,6]]) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 3)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.zeros((5,5,4)) "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]],\n",
-       "\n",
-       "       [[0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.],\n",
-       "        [0., 0., 0., 0.]]])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  2,  3,  4],\n",
-       "       [ 5,  6,  7,  8],\n",
-       "       [ 9, 10, 11, 12]])"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = a[:2, 1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 3],\n",
-       "       [6, 7]])"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
-    "y = np.array([[5,6],[7,8]], dtype=np.float64)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 6.,  8.],\n",
-       "       [10., 12.]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x + y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 6.,  8.],\n",
-       "       [10., 12.]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.add(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.11.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}